Table of Contents Author Guidelines Submit a Manuscript
Computational Intelligence and Neuroscience
Volume 2019, Article ID 9374802, 19 pages
https://doi.org/10.1155/2019/9374802
Research Article

Evaluating a Semiautonomous Brain-Computer Interface Based on Conformal Geometric Algebra and Artificial Vision

Centro de Investigación y de Estudios Avanzados (Cinvestav), Unidad Monterrey, Apodaca, Nuevo Leon 66600, Mexico

Correspondence should be addressed to David Gutiérrez; gro.eeei@ztgd

Received 10 June 2019; Accepted 30 October 2019; Published 27 November 2019

Guest Editor: Hyun S. Kim

Copyright © 2019 Mauricio Adolfo Ramírez-Moreno and David Gutiérrez. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

Abstract

In this paper, we evaluate a semiautonomous brain-computer interface (BCI) for manipulation tasks. In such a system, the user controls a robotic arm through motor imagery commands. In traditional process-control BCI systems, the user has to provide those commands continuously in order to manipulate the effector of the robot step-by-step, which results in a tiresome process for simple tasks such as pick and replace an item from a surface. Here, we take a semiautonomous approach based on a conformal geometric algebra model that solves the inverse kinematics of the robot on the fly, and then the user only has to decide on the start of the movement and the final position of the effector (goal-selection approach). Under these conditions, we implemented pick-and-place tasks with a disk as an item and two target areas placed on the table at arbitrary positions. An artificial vision (AV) algorithm was used to obtain the positions of the items expressed in the robot frame through images captured with a webcam. Then, the AV algorithm is integrated into the inverse kinematics model to perform the manipulation tasks. As proof-of-concept, different users were trained to control the pick-and-place tasks through the process-control and semiautonomous goal-selection approaches so that the performance of both schemes could be compared. Our results show the superiority in performance of the semiautonomous approach as well as evidence of less mental fatigue with it.

1. Introduction

A brain-computer interface (BCI) is a system that enables a real-time user-device communication pathway through brain activity. Through the years, research and development on BCI has mainly been oriented to the creation of rehabilitation systems as well as systems that help disabled patients regain to some extent their lost or diminished capabilities [1]. Some reported devices that have been successfully controlled using BCIs are spellers, electric wheelchairs, robotic arms, electric prostheses, and humanoid robots [25]. In BCI studies, the most common technique used to acquire brain noninvasively is electroencephalography (EEG).

In order to manipulate the device through brain activity, the design of the BCI must include the following stages: signal acquisition, filtering, feature extraction, classification, device modeling, and control [6]. During the filtering stage, unwanted noise and artifacts are removed from the signals using temporal and spatial filters. Then, temporal or spatial features of interest are extracted from the signals to build feature vectors. These vectors are formed by characteristic components of the signals, which are then used in the classification stage to decipher user intention. Lastly, the device is manipulated based on the result of the classification algorithm. Depending on the device and the complexity of the system, a model of the system is needed to perform with precision the desired tasks. BCIs can be divided into two groups based on their control strategy: process control and goal selection. In the process-control strategy, users are continuously controlling each part of the process. This is done by performing low-level commands continuously through the BCI, with no additional assistance. On the other hand, in the goal-selection strategy, users are responsible for selecting their desired goal and the system provides assistance to successfully perform the tasks with minimum effort [7]. In this case, the user performs high-level tasks by sending simple commands through the BCI.

Common paradigms used as control commands in BCI include steady-state visual evoked potentials (SSVEPs), P300 waveform, and motor imagery (MI). SSVEP is a resonance phenomenon occurring at occipital and parietal lobes as a result of oscillatory visual stimulus presented to a user at a constant frequency [8]. The P300 is an EEG signal component that appears 300 ms after an event of voluntary attention, and it is usually observed during visual or auditory stimulus presentation [9]. MI presents as an event-related desynchronization (ERD) found at the sensorimotor areas, which generates a contralateral power decrease in a frequency range from 8–13 Hz (also known as the μ band) [10]. Controlling a BCI with SSVEP and P300 requires less training in comparison to MI, as the first represents an involuntary response to a stimulus. However, its use in BCI is limited due to its requirement of a stimulus presentation device. The training process to control MI-based BCIs (MI-BCI) might involve stimulus presentation as well. However, it can be excluded for its final application on the BCI. Even though MI-BCIs require longer training periods, they are better suited for close-to-real-life environments and self-paced BCIs [11].

Several studies present successfully implemented ERD-based BCIs, most of them using a process-control strategy [1214]. Some goal-selection BCIs have been reported as well [15, 16]. In [7], users were trained on process-control and goal-selection MI-BCIs to perform one-dimensional cursor movements on a screen. The results suggest that users performing on goal-selection strategy showed higher accuracy and faster learning in comparison to the process-control approach. However, the authors state that a direct comparison of goal selection and process control in a more complicated (real-world) scenario has not yet been presented. In the proposed study, three-dimensional object manipulation tasks through a robotic arm are implemented in a MI-BCI. The complexity of the three-dimensional movements on real objects is higher than the one-dimensional movements on virtual objects presented in [7]. In [17], a semiautonomous BCI is implemented to manipulate a robotic arm to perform tasks such as pouring a beverage inside a glass in a tray, through SSVEP. In future research, similar tasks as in [17] could be implemented in our BCI using MI instead, allowing a more natural execution of daily-life context tasks without the need of a stimulus presentation screen.

In a typical process-control MI-BCI, the user controls the direction of the final effector of a robotic arm through low-level commands, which means that the user has to maneuver the robot in a three-dimensional space to reach for a desired target. Clearly, the user remains in a high attention state during the maneuvers, as he/she is continuously aware of the final effector position during the whole task. This continuous awareness might lead to generation of mental fatigue or frustration, which is undesirable as it can directly affect user performance and learning [18]. The analysis of P300 features, such as amplitude and latency, has been shown to be useful in identifying the depth of cognitive information processing [19]. The amplitude of P300 waveform tends to decrease when users encounter cognitive tasks with high difficulty [20]. On the other hand, P300 latency has shown to increase when the stimulus is cognitively difficult to process [21]. Another study has reported correlation between changes in the P300 component and BCI performance [22]. The evidence provided by these studies might suggest that the analysis of P300 could be implemented as a mental fatigue indicator during BCI training and control.

In order to diminish mental fatiguing in BCI systems, a semiautonomous BCI using a goal-selection strategy is here proposed. This system assists the user to perform a specific task by calculating all the variables needed to successfully execute it. Some studies have previously presented BCI designs focusing on this semiautonomous approach with successful results on performance, accuracy, and comfort for the user [17, 23, 24]. Therefore, this paper presents the implementation of a traditional low-level MI-BCI and a semiautonomous MI-BCI designed to perform object manipulation tasks with a robotic arm. In the process-control strategy MI-BCI, the user commands the final effector of the robot to move in a three-dimensional space to reach for a target placed on a table. In the semiautonomous MI-BCI, one small disk and two target areas are placed on a table. Here, the robot reaches for the disk and places it on a specific target, which is selected by the user. As proof-of-concept, two volunteers were trained on each BCI system, and their performance was evaluated and compared. A statistical P300 analysis was performed on all users in order to observe mental fatigue differences induced by the operation of low-level and semiautonomous BCIs.

In order to model the robot used in this experiment, a conformal geometric algebra (CGA) model was implemented in both the traditional and semiautonomous BCIs to solve the inverse kinematics of the robotic arm, i.e., obtaining the joint angles needed for a specific position of the final effector. Additionally, an artificial vision (AV) algorithm was integrated into the semiautonomous BCI in order to provide information about the positions of the items on the table referenced to robot frame. As the implementation of the semiautonomous BCI implies a higher computational load, the CGA model was chosen for the solution of the inverse kinematics. CGA has shown to represent an operation reduction and in some cases, a decrease in computational load when compared to traditional inverse kinematics solution [25].

This paper is organized as follows. The CGA model and AV algorithm are described in Section 2, and the design of both BCIs is explained in Section 3. Evaluations on both algorithms and performance results of users controlling both BCIs are presented in Section 4. Preliminary short reports of the system’s implementation (but not its evaluation) have been presented in [26] and [27].

2. Robot Modeling and Artificial Vision

In this section, we describe each of the components required to compute the inverse kinematics of a robotic arm by using CGA. Furthermore, here we explain in detail the AV algorithm used to obtain the positions of the objects to be manipulated by the robot.

2.1. Conformal Geometric Algebra

Traditional methods to solve the inverse kinematics of robots include several matrix operations as well as many trigonometric expressions. All this can result in a quite complex solution depending on the modeled robot [28]. In this study, a conformal geometric algebra (CGA) model is proposed instead, as it is considered to be computationally lighter, easier to implement, and highly intuitive. CGA has proved to be a powerful tool when solving the inverse kinematics of robotic arms [29, 30]. It also offers an operation reduction when compared to traditional methods and provides efficient runtime solutions. More information on computational efficiency characteristics can be found in [31].

With this model, the joint angles of the robot are obtained for a specific position of the final effector. In CGA, two new dimensions () are defined, representing a point in the origin and a point in the infinity, respectively, in addition to the three-dimensional Euclidean space () [29]. In this space, geometric entities (points, lines, circles, planes, and spheres) and calculations involving them (distances and intersections) can be represented with simple algebraic equations.

Also, the geometric product between two vectors a and b is defined as a combination of the inner product and the outer product:

The inner product is used to calculate distances between elements, and the outer product generates a bivector, which is an element occupying the space spanned by both vectors. It is also used to find the intersection between two elements. The intersection M of two geometric objects A and B represented in CGA is given by or . The element is the duality of A and is expressed aswhere , which allows for a change in representation of the same element. Standard and dual representations of commonly used geometrical objects in CGA are shown in Table 1. There, x and n are points represented as a linear combination of the 3D base vectors:

Table 1: Representations of the conformal geometric entities.

There are two possible representations of the same element, as shown in Table 1. A circle can be represented as the space spanned by three points in space as well as the intersection of two spheres. Also, a line can be expressed as the intersection of two planes as well as the space spanned by two points expanded to the infinity.

Making use of the previous equations and relationships, a CGA model to solve the inverse kinematics of a manipulator robot was obtained following the proposed method in [32]. The modeled robot was the Dynamixel AX-18A Smart robotic arm, which is a five-degree-of-freedom (5-DOF) manipulator robot. Figure 1 shows the modeled robot as well as its joints and links. The DOF of this robot corresponds to its shoulder rotation, elbow flexion-extension, wrist flexion-extension, wrist rotation, and hand open-close function [33]. The inverse kinematics solution was obtained for joints , , and . For the particularities of the manipulation tasks, joints and were not considered for simplicity.

Figure 1: Joints and links of our 5-DOF Dynamixel AX-18A Smart robotic arm.
2.2. Our CGA Model

Next, we describe the required CGA model that we implemented specifically for our system.

2.2.1. Fixed Joints and Planes

The origin of the CGA model was located at joint , located at the center of the rotational base of the robot; therefore, . Joint is also a fixed joint with constant position, found directly above joint . The position for joint was defined as . Now, let us consider the desired final effector position as a point in space . Then, a vertical plane representing the direction of the final effector is described aswhere represents the origin in robot frame and the Euclidian z axis. As the position of the final effector is used to define , the direction of plane changes consistently with . A plane , representing the rotational base of the robot, is defined aswhere and represent the Euclidean x and y axes. Planes and are shown in Figure 2.

Figure 2: Planes and representing the orientation of the final effector and the robot base, respectively.
2.2.2. Calculation of Joint’s Position

In a kinematic chain model of a robotic arm using CGA, the implemented method to find joint is based on the intersection of two spheres centered at joints and with radii equal to the lengths of the links connecting with and with , respectively. The intersection of both spheres results in a circle, which is then intersected to the plane of the final effector to obtain a point pair representing two possible configurations for joint . One point is then selected as , depending on the desired configuration. The process requires the following:(i)Spheres centered at point with radius r are given by(ii)There are two methods for creating a circle. We can either intersect two spheres and byor we can intersect a plane π and a sphere s by(iii)The intersection of a circle c and a plane π to create a point pair is given by(iv)Finally, to obtain a point from , we have

Based on the previous expressions, and in order to find the position of joint in our modeled robot, two spheres must be constructed, and they have to be centered at and . However, the position of joint is yet unknown in our model. A similar situation occurs if the desired position is instead joint . In this particular case, is known but not . Given this situation, another approach was implemented in order to find joint .

2.2.3. Position of Joint

Using (6), sphere was centered at with radius equal to the length of link . Hence, in order to find joint , another sphere must be intersected to . In order to construct , its center must be defined. This is achieved by first creating an auxiliary sphere , centered at the origin with radius equal to the horizontal component of the distance from to . This is valid as the distance from to is constant for any position of the final effector .

Then, using (8), is intersected to plane to obtain circle . Next, using (9), is intersected to plane to produce point pair , from which one point is selected as using (10). The procedure to find point , which corresponds to the center of the desired sphere to be intersected with , is shown in Figure 3.

Figure 3: The intersection of spheres (bottom) and (top) define the circle , from which we find the position of point .

Using (6), sphere is centered at with radius equal to the vertical component of the distance from to . Then, the intersection of spheres and is given by (7), which results in circle . Using (9), the intersection of with plane renders point pair . Finally, the position of is obtained from given by (10). The whole procedure previously detailed to obtain the position of joint is represented in Figure 4.

Figure 4: The intersection of spheres (left) and (right) define the circle , from which we find the position of joint .
2.2.4. Position of Joint

The procedure to find the position of joint is straight forward once the position of joint is calculated. For that, two spheres and are defined using (6), centered at and , with radii equal to the length of links and , respectively. Both spheres are intersected to obtain circle using (7). With (9), is then intersected to plane to obtain point pair . From , is easily obtained using (10). A representation of the procedure to find joint is shown in Figure 5.

Figure 5: The intersection of spheres (bottom) and (top) define the circle , from which it is possible to find the position of joint .
2.2.5. Angle Calculation

In order to calculate the angles formed by two vectors α and β, their corresponding unit vectors are defined as and . The normalized bivector spanning the space formed by those vectors is expressed as

As explained in [32], the angle θ between α and β is given bywhere corresponds to the four-quadrant inverse tangent. This operator gathers information on the signs of its two arguments in order to return the appropriate quadrant of the computed angle [34]. Such result is not possible to be obtained from the conventional single-argument function. Also, note that the plus sign in (11) applies if the rotation from α to β is counter-clockwise, while the minus sign applies in the opposite rotation.

In order to find the joint angles using (12), vectors formed by the links of the robot need to be calculated. First, lines representing each link are defined:

The previous expressions define lines passing through links , , , and , respectively (see Figure 1). was considered a straight line from joint to the final effector , i.e., we ignored wrist rotation and hand open-close joints.

In (12), the parameters α and β need to be directional vectors for the purpose of computing our joint angles. Therefore, the directional vectors of plane , as well as lines and , were calculated, which represent the base and links of the robot, respectively. From a given line l, its directional vector can be obtained asand the directional vector normal to a plane π is given by

Based on all the previously defined elements, the vectors involved in the calculation of joint angles , for , are summarized in Table 2. Then, α and β in (11) and (12) are replaced by and , respectively, to calculate . Note that, as joint is fixed, does not need to be calculated.

Table 2: Parameters for joint angles calculation.
2.3. Artificial Vision Algorithm

An AV algorithm was implemented to calculate the positions of items on a table, so the robotic arm could perform the desired manipulation tasks. An ATW-1200 Acteck web camera was used to record images at 30 fps with a resolution of pixels. The acquired images were processed and analyzed in real time using the OpenCV library (https://www.opencv.org) from Python.

The robotic arm was fixed on a white table, centered at one end of it. A plane was delimited on the table, defined as . Four markers of different colors (cyan, orange, magenta, and yellow) were placed inside the delimited square, one at each corner. A blue disk with height of 6 mm and radius of 13 mm was used as the item to be picked, while two stickers with radius of 42 mm (green and red) were used to indicate target areas. The camera was fixed in a high angle so that all markers and items were inside its field of view. The setup of the robotic arm and items in the table are shown in Figure 6.

Figure 6: Robot and items on the table as seen by the camera during semiautonomous BCI trials.

In order to perform object manipulation tasks, the real-world coordinates of the plane (in reference to robot frame) had to be obtained from the image coordinates obtained by the camera. To achieve this, a homography transformation was performed on the acquired images. In general, a two-dimensional point in an image can be represented as a three-dimensional vector by letting and . This is called the homogeneous representation of a point, and it lies on the projective plane [35]. Homography is invertible mapping of points and lines on the projective plane , thus allowing to obtain the real-world coordinates of features in an image from its image coordinates.

In our case, the desired transformation is such that the image obtained from the camera is turned into a two-dimensional view of the same setup. In this transformation, the image shows a planar representation of the original view, as if the camera was placed directly above the delimited square. In order to obtain this representation, the following homography transformation was applied [35]:where vectors and represent the positions of selected points in the image and their corresponding positions in real-world coordinates, respectively, is the homography matrix that defines the desired perspective change to be performed on the image, and K is the calibration matrix which contains the intrinsic parameters of the camera, while R and t are, respectively, the rotation matrix and translation vector applied on the camera in order to perform this transformation view. In (16), z is ignored as all items are considered to be at .

In order to compute matrix H, both real-world and image coordinates of the centroids of the square markers were obtained. First, markers were detected through color segmentation and binarization, as shown in Figure 7(a). This process was performed separately on each marker, and their contours were detected. After that, the centroids of the markers in the image were calculated. The contours and centroids of each marker are shown in Figure 7(b).

Figure 7: Required steps of the AV algorithm. (a) Segmentation and binarization. (b) Centroid calculation. (c) Homography transformation.

Since the markers have known dimensions (), the positions of their centroids in real-world coordinates relative to the plane are known as well. These positions were defined as cyan at , orange at , magenta at , and yellow at , all inside the available area of the table. Then, both sets of coordinates are used to obtain H with OpenCV’s command findHomography, and the resulting matrix is applied to transform the image, as shown in Figure 7(c).

Then, using the same procedure as with the markers, the centroids of the disk and targets in the new image were calculated. However, the reference frame from the image is different from the reference frame from the robot. Therefore, the first was transformed by applying the following rotation matrix:

Furthermore, a translation vector was applied as well as a sign switching of the x axis to obtain the desired positions. In robot frame, the x axis of the delimited square goes from to 20, while the y axis goes from 0 to 40, and the robot is located at the origin. After applying all those transformations, the centroids of all items are finally expressed in robot frame, and they can be detected by the AV system together with the contours of all items. This is shown in Figure 8.

Figure 8: Visual representation of contours and centroids of the items in the table calculated by the AV algorithm in order to obtain their real-world coordinates.

3. Implementation of BCI Systems

As proof-of-concept, four participants volunteered in this study (two females and two males, with average age of 22.25 years, ). The experimental protocol was divided into three stages for both the process-control and goal-selection BCIs: (i) training, (ii) cued manipulation, and (iii) uncued manipulation. Both BCIs were MI-based; therefore, users were trained to control the corresponding μ band desynchronization at will. In all trials, volunteers sat in front of a computer screen first showing a black screen (baseline) in which the user was meant to be in a resting state. Then, different types of stimulus were presented to the user, representing each a different command. The duration for the baseline (15 seconds) and stimulus presentation (4 seconds) was the same for all trials and stages. During stimulus presentation, users were expected to react accordingly, either by imagining the movement of either left or right hand, or by remaining in a resting state. In training trials, EEG signals were acquired and analyzed offline to build and evaluate the performance of classifiers, which were then used online during the manipulation trials. In cued manipulation trials, the user was expected to manipulate the device as indicated by the stimuli. On the other hand, the user was encouraged to manipulate the device at will during uncued manipulation trials.

3.1. Training Trials

The training protocol was identical for both the process-control and goal-selection BCIs. Three types of stimuli were presented to the user: right hand imaginary movement (RHIM), left hand imaginary movement (LHIM), and rest. A total of 30 stimuli (10 for each command) were randomly presented to the user. Stimuli were represented in the computer screen with a red arrow pointing to the right (for RHIM), pointing to the left (for LHIM), and a black screen for rest. A 2 s green cross appeared before all stimuli as a prestimulus, and there was a variable interstimulus resting period of 2–4 seconds between stimuli. Users underwent three training sessions on different days, each comprising five repetitions of the mentioned experimental protocol, while EEG recordings were obtained.

3.2. Signal Acquisition

EEG signals were recorded with the Mobita equipment from TMSi systems, using a measuring cap of 19 channels: FP1, FP2, F3, F4, C3, C4, P3, P4, O1, O2, F7, F8, T3, T4, T5, T6, Cz, Fz, and Pz. Impedance of all electrodes was set below 5 kΩ for all experiments. Signals were acquired with a sampling frequency of 1000 Hz. Recordings were band-pass filtered with a fourth-order 1–100 Hz Butterworth filter and a 60 Hz notch filter to eliminate power line interference. The OpenViBE software was used for the BCI design and implementation. More information about this software can be found in [36].

3.3. Classification Algorithm

Feature extraction was performed using the BCI2000 offline analysis tool (https://www.bci2000.org/mediawiki/index.php/User_Reference:BCI2000_Offline_Analysis), where the value was calculated. A higher value is related to a higher discrimination of a signal under two stimulus conditions. More details about the statistic meaning of can be found at https://www.bci2000.org/mediawiki/index.php/Glossary. After each training session, signals from the five training trials were used to calculate . Three maps (one per stimulus combination) were obtained per training session, showing the values in the 19 available channels and frequencies ranging from 1 to 70 Hz. Each map, as the one shown in Figure 9, represents the channels and frequencies which, for a specific combination of conditions, showed higher discrimination. Through this procedure, the selected channels and frequencies were used as features for the classification algorithm.

Figure 9: Representative map obtained during one training session. values shown here were measured under conditions RHIM-Rest for all channels and frequencies.

Signals were spatially filtered using a Laplacian filter on the selected channels as well as through a fourth-order Butterworth band-pass filter tuned to the selected frequencies. Power values were then obtained from the filtered signals to build the feature vectors, which then became the input for a linear discriminant analysis (LDA) classifier, which separates data representing different classes by finding a hyperplane which maximizes the distances between the means of the classes, while minimizing the variance within classes [37].

In our case, three pairwise classifiers per training session were obtained using this procedure: LHIM versus RHIM, LHIM versus rest, and RHIM versus rest. The three classifiers were tested online on the recorded signals to evaluate their performance as a percentage of correctly classified stimuli. The classification was performed on each four-second stimulus, divided into overlapped subepochs using a window function. Each four-second epoch was formed by 64 subepochs of two seconds, separated by 0.0625 seconds. One pairwise classifier labeled each subblock as one of the two possible classes, and the four-second epoch was classified as the mode of the classification result for all its subblocks. Then, one general classifier was built, based on the results of the three pairwise classifiers. Here, the four-second epoch of each stimuli was labeled as class (LHIM, rest, or RHIM, respectively), if two out of the three pairwise classifiers labeled the same epoch identically. The mean performance of the general classifiers across trials is shown in Table 3 for all subjects and training sessions as well as their selected features. After training sessions, each user proceeded to perform the subsequent trials using the classifier with the highest performance obtained at the last training session.

Table 3: Features: EEG channels and frequency range (in Hz) and mean accuracy of LDA classifiers for all subjects and training sessions.
3.4. Process-Control BCI

The process-control BCI was designed in such a manner that users were able to perform three-dimensional movements to complete reaching tasks. In this system, the position of the final effector as well as the desired axis in which the effector moves can be controlled through low-level MI-based commands. To achieve this, the user has two choices: moving along a selected axis (y-axis at the initial step) or change between axes. In the design of this BCI, the classification of a LHIM results in a −10 mm displacement, while the classification of a RHIM results in a +10 mm displacement on the selected axis. The classification of a rest event holds the position of the final effector with no displacement. The consecutive classification of two rest events in a row allowed the user for a change of axis. This change of axis takes place in the following sequence: , , and .

3.4.1. Cued Manipulation

In these trials, users sat in front of a computer showing three windows on the screen. The first window was used for stimulus presentation, the second was used to display in which axis the movement of the robot took place, and the third was used to visualize the robot and its movements. The setup for these experiments is shown in Figure 10. After the baseline period, 15 random stimuli (5 for each type) were presented to the user. Prestimulus, stimulus, and interstimulus duration was the same as in training trials (see Section 3.1). After the stimulus was presented, the user was expected to emit the instructed command through the BCI. Then, the robot performed a specific movement based on the classification result. In these trials, performance was evaluated as the percentage of correctly classified stimuli. The intention of these trials was to get the users acquainted with the BCI, and they were performed immediately before the uncued manipulation trials. Users performed three sessions on different days, each formed by three repetitions of this protocol.

Figure 10: Setup of the process-control BCI. The windows shown in the screen are used for visualization of stimuli, indicating the current axis of the movement, and viewing of the robot performing the manipulation tasks.
3.4.2. Uncued Manipulation

The same screen display was used as in cued trials, but here subjects were asked to complete reaching tasks on their own. At the start of each trial, the final effector was fixed at home position and a target was placed at . At this initial step, the distance of the final effector to the target was 360 mm. Note that the target is placed at , as the robot base is 49 mm above the table. A baseline period was followed by the presentation of 20 stimuli showing the word “Imagine,” in which the user was expected to emit MI commands through the BCI. The duration of prestimulus, stimulus, and interstimulus periods was the same as in training trials (see Section 3.1). The user was instructed to move the final effector as close as possible to the target within the 20 stimuli, using the protocol described in Section 3.4. Performance was evaluated as the percentage of stimuli where the user moved the final effector closer to the target and changed successfully to the y-axis. Users performed three sessions on different days, each formed by five repetitions of the described protocol.

3.5. Goal-Selection BCI

The goal-selection BCI was designed to perform in a semiautonomous way pick-and-place tasks with the disk and two possible targets. Users were able to perform these tasks for any position of the items (randomly chosen before a trial), inside of the robot workspace. The centroids of the two target stickers were calculated in these trials by the AV algorithm. In this case, the classification of three types of events resulted in different manipulation tasks:(i)If an event was classified as RHIM, the robot reached for the disk, placed it on the target located to the right (greater component), and returned to home position(ii)If an event was classified as LHIM, the robot reached for the disk, placed it on the target located to the left (smaller component), and returned to home position(iii)If an event was classified as rest, the robot remained at home position

After the robot performed a manipulation task, all the items in the table were manually changed to random positions, in preparation for the next trial.

3.5.1. Cued Manipulation Trials

In these trials, the subject sat in front of a computer screen which showed two screens. The first one was used for stimulus presentation, while the second was used to present the transformed image, as shown in Figure 11. After the baseline period, a stimulus (RHIM, LHIM, or rest) was randomly presented. A total of 15 stimuli (5 for each type) were presented in each trial. A one-second beep sound followed a two-second green cross as prestimulus, with a 27–29 seconds interstimulus period. Manipulation tasks were performed according to the result of the classification, and performance was evaluated as the percentage of correctly classified stimuli. The total duration of these trials was considerably longer than in the low-level BCI. This is mainly due to the longer interstimulus period, in which the manipulation tasks took place. Users underwent three sessions on different days, performing five trials in each session.

Figure 11: Setup of the semiautonomous goal-selection BCI, as seen by the user. The windows are used for stimulus presentation and visualization of the manipulation tasks.
3.5.2. Uncued Manipulation Trials

For uncued manipulation trials, all stimuli were replaced with the word “Imagine,” and the user freely decided the task to perform, as explained in Section 3.5. A total of 15 stimuli were presented in each trial. The stimulus, prestimulus, and interstimulus duration was the same as in the goal-selection BCI cued manipulation trials (see Section 3.5.1). Immediately after the classification was performed, and before the robot executed the task, the user was asked the type of intended stimulus to emit. In these trials, performance was evaluated as the percentage of coincidences between the intended and the classified stimulus type.

3.6. Analysis of Data through P300 Estimation

Reported assessments of mental fatigue through P300 amplitude and latency can be found in [38] and [19]. In [19], mental fatigue was evaluated through EEG measurements. Participants’ P300 were measured during a modified Eriksen flanker task, replacing word stimuli with arrows, before and after performing mental arithmetic tasks. A decreased P300 amplitude and an increased latency were observed after performing arithmetic tasks, when users were mentally fatigued. Statistical analysis revealed the most significant changes in amplitude and latency at channels O1, O2 and Pz, probably as a reflection of visual processing during stimulus presentation of arrows. Similar to the protocol used in [19] to assess mental fatigue, signals were segmented into 1 s stimulus-locked EEG epochs from 200 ms before and 800 ms after stimulus presentation. These epochs were obtained for the presentation of the word “Imagine” during uncued manipulation trials for both the process-control and goal-selection BCIs. For each trial, a representative waveform was obtained by averaging the epochs from all stimuli. Then, the averaged waveforms were band-pass filtered at 1–10 Hz and were used to calculate P300 amplitude and latency. The amplitude was considered as the most positive peak within a 200–500 ms window immediately after stimulus presentation. Latency was obtained as the time this peak appeared. Amplitude and latency values were obtained through this procedure for all trials, sessions, and subjects, in channels O1, O2, and Pz. A representation of an obtained P300 waveform is shown in Figure 12 for these three channels.

Figure 12: Representation of a P300 waveform calculated for channels O1, O2, and Pz.

In order to examine the differences of mental fatigue within and between users in relationship with the use of our two different BCI schemes, two-way ANOVA tests were performed on all users: one for amplitude and one for latency. In these tests, influence of trial repetition (1–5), channel location (O1, O2, and Pz), and their interaction were analyzed on both P300 features. The number of replications was considered as three, representing the three uncued manipulation sessions performed by the users. To further analyze mental fatigue related to continuous BCI manipulation, one-way ANOVA () tests were performed on each subject. Six one-way ANOVA tests were performed per subject: three channels (O1, O2, and Pz)  two P300 features (amplitude and latency). These tests were performed in order to find which channel showed significant relationship to the trial repetition factor. Then, amplitude and latency values of all users were compared using the most significant channel from this analysis.

4. Results

A preliminary validation of our CGA model and AV algorithm can be found in [26] and [27], respectively; hence, we omit those details here. Therefore, this section shows the results of evaluating the whole system in the context of our BCI implementations for four subjects (two on each BCI type). Performance values were obtained for all subjects in training, cued, and uncued trials, according to the particularities of each experimental protocol. For training trials, performance values correspond to the classifier accuracies shown in Table 3. Performance for cued and uncued manipulation trials was obtained as explained in Sections 3.4 and 3.5. Performance values included in these results represent the average across trials for each session.

4.1. Performance of Process-Control BCI

Subject reached an accuracy level of at its first training session, at the second, and by the third. During cued manipulation trials, performance started at and then increased to and by the second and thirds sessions, respectively. For uncued manipulation trials, the user only moved far from the target at the first session (). For the second and third sessions, user obtained performances of and . Subject showed a similar behavior to during training trials, starting at and decreasing to and by the second and third sessions. In cued manipulation trials, performance started at and then increased to by the second session and by the third. For uncued manipulation trials, performance values started at for the first session, then decreased to at the second, and increased to by the third. Results for process-control BCI performance are shown in Figure 13 for users and , respectively.

Figure 13: Performance for users (top) and (bottom) in process-control BCI during training, cued, and uncued manipulation trials (left, middle, and right columns, respectively). Bars indicate one standard deviation. (a) S1, training. (b) S1, cued manipulation. (c) S1, uncued manipulation. (d) S2, training. (e) S2, cued manipulation. (f) S2, uncued manipulation.
4.2. Performance of Goal-Selection BCI

Subject started the training sessions at of accuracy, at the second session, and reached at the third session. Performance for the cued manipulation trials started at , increasing to at the second session and decreasing to by the third. During uncued manipulation trials, performance started at accuracy by the first session, at the second, and by the third. User obtained performance values of at the first session, at the second, and decreased to at the third. During cued manipulation trials, the subject obtained performance values of for the first session, 41% for the second, and 46% for the third. For uncued manipulation trials, user performance started at and increased to and by the second and third sessions, respectively. Results for goal-selection BCI performance are shown in Figure 14 for users and , respectively.

Figure 14: Performance for users (top) and (bottom) in semiautonomous goal-selection BCI during training, cued, and uncued manipulation trials (left, middle, and right columns, respectively). Bars indicate one standard deviation. (a) S3, training. (b) S3, cued manipulation. (c) S3, uncued manipulation. (d) S4, training. (e) S4, cued manipulation. (f) S4, uncued manipulation.
4.3. P300 Analysis

The results for the two-way ANOVA tests are presented in Table 4. The results for the P300 latency two-way ANOVA showed statistical significance for subjects () and () in the trial factor, but no significance was observed for channel and interaction factors. Users and showed no statistical significance for any of the analyzed factors. For the P300 amplitude two-way ANOVA, users showed smaller values in trial when compared to channel and interaction. However, our tests did not show statistical significance for any factor or interaction.

Table 4: Two-way ANOVA results for P300 latency and amplitude.

The results for the one-way ANOVA tests are shown in Table 5. The results for P300 latency one-way ANOVA showed statistical significance for user at channel O1 () and for user at channel O2 (). Regarding users and , values were not significant at any channel. For the P300 amplitude one-way ANOVA, user showed statistical significance at channel Pz (). The tests for , , and revealed no statistical significance at none of the three analyzed channels.

Table 5: One-way ANOVA results for P300 latency and amplitude on channels O1, O2, and Pz.

The results of the performed statistical tests allowed to observe differences between analyzing latency and amplitude. Among all tests, greater changes were found in latency rather than in amplitude. Based on these results, an evaluation and comparison on amplitude and latency values was performed. These values were considered as those corresponding to the channel with the lowest value on the one-way latency ANOVA results. The selected channels were O1 for , Pz for and , and O2 for .

Amplitude values calculated for all uncued manipulation trials are shown in Figure 15 for each session and user. Users and showed a similar behavior: a decreasing P300 amplitude trend in all sessions. In this case, the amplitude observed at the first trial was higher than that of the last one. showed a decreasing trend as well for the first and second sessions, yet the opposite was observed during the third session. presented an increasing amplitude trend for all sessions. Here, the amplitude obtained at the last trial was higher than the one at the first trial.

Figure 15: Amplitude of P300 waveform during all trials and experiments for all subjects during uncued manipulation trials. (a) Subject S1, session 1. (b) Subject S1, session 2. (c) Subject S1, session 3. (d) Subject S2, session 1. (e) Subject S2, session 2. (f) Subject S2, session 3. (g) Subject S3, session 1. (h) Subject S3, session 2. (i) Subject S3, session 3. (j) Subject S4, session 1. (k) Subject S4, session 2. (l) Subject S4, session 3.

Latency values can be observed in Figure 16 for all users and sessions. Subjects and showed an increasing P300 latency trend during the first and third sessions. A decreasing trend was observed during the second session for these users. User presented an increasing latency trend for all sessions. User showed an increase in latency during the first and second sessions and a decrease at the third.

Figure 16: Latency of P300 waveform during all trials and experiments for all subjects during uncued manipulation trials. (a) Subject S1, session 1. (b) Subject S1, session 2. (c) Subject S1, session 3. (d) Subject S2, session 1. (e) Subject S2, session 2. (f) Subject S2, session 3. (g) Subject S3, session 1. (h) Subject S3, session 2. (i) Subject S3, session 3. (j) Subject S4, session 1. (k) Subject S4, session 2. (l) Subject S4, session 3.

5. Discussion

The implementation and integration of the CGA model and the AV algorithm allowed to successfully design a MI-based semiautonomous BCI for manipulation tasks. When compared against a low-level system, both BCIs were similar in terms of training protocol and control commands; however, the complexity of the executed tasks was different. The semiautonomous goal-selection BCI was superior in task complexity when compared to the process-control BCI, even though both systems used the same control commands as input. While the process-control BCI might be used to perform more general tasks, it demands a continuous awareness state from the user. Its output are discrete low-level commands which in the long run might lead the user to a mental fatigue condition. Although the semiautonomous BCI is goal specific, it requires user attention only during short time periods, making it theoretically less fatiguing. The semiautonomous goal-selection BCI works, in essence, in a more natural way to the user than the process-control BCI. This is because when performing reaching tasks, people think on the main goal and the cerebellum processes the necessary information to successfully achieve it, rather than executing several discrete low-level movements [39].

The selected features for the general classifiers of the users were mainly frontal, central, and parietal electrodes in the μ (8–13 Hz) and β (13–30 Hz) brain rhythms, which are known to be physiologically involved in the imaginary movement process. The selected channels for the classifiers are consistent with reports of central activity as a reflection of motor cortex contralateral desynchronization during imaginary movement [10] and frontoparietal activation related to control of spatial attention and motor planning during reaching tasks [40, 41].

Even though all users underwent the same training protocol, differences among them were observed. Across training sessions, and maintained a relatively constant performance, while showed a more notorious improvement. showed a relatively high performance at the first and second session, but it decreased at the third. During cued manipulation trials, all users obtained low performance levels and none of them showed a significant improvement across sessions. obtained below chance level () performance during all sessions. Performance of users , , and was in general above chance level, but always remained below . During uncued manipulation trials, users and presented the lowest performance values, close to and below chance level. This indicates that these users were faced with difficulty while controlling the process-control BCI. Performance of and during uncued manipulation trials was higher (around 40–60%) when compared to and . Mean performance values across trials of users and failed to reach considered as the theoretical threshold for practical MI-BCI use [42]. However, their performance was evidently higher than the one obtained by users performing on the process-control BCI. This might suggest that the designed semiautonomous goal-selection BCI was easier to manipulate than the process-control BCI. Future research will address classification optimization to increase system accuracy and ease of use.

As shown in Table 3, selected channels and frequencies for feature extraction showed changes across sessions for all users. This might suggest that the used channel/frequency selection method is sensitive to intra- and intersubject brain variability. After training trials, a classifier with fixed parameters was selected per subject and used in all BCI trials. Yet, constant adaptation of the classifier parameters is required for optimal operation. Hence, an optimized feature selection algorithm should be implemented to address this issue and increase efficiency in our proposed semiautonomous BCI. Such optimization was out of the scope of our work, but reports on how optimized correlation-based feature selection methods are used in MI-BCIs can be found in [43, 44].

Another efficient approach for feature selection is the partial directed coherence (PDC) analysis, which could help identify relevant channels and features. Recently, a PDC-based analysis was proposed in [45] to identify relevant features for MI tasks, and efficient classifiers were built based on this procedure. Even more recently, a review on EEG classification algorithms highlights Riemannian geometry-based classifiers as promising, as well as adaptive classification algorithms [46]. A simple implementation of an adaptive classifier for MI tasks was described in [47], which showed an encouraging increase on classification accuracy. More novel classifiers based on Riemannian geometry have shown good results in classifying brain-related MI tasks [48].

In regards to our selection of the P300 component to evaluate mental fatigue, such component is not exclusively presented during nonfrequent stimulus, rather its amplitude is enhanced, which makes it a suitable control command for BCIs. P300 amplitude is larger during nonfrequent stimuli, and it is typically used/analyzed based on this argument. However, it has been demonstrated that P300 responses can be observed to both frequent and nonfrequent stimuli [49, 50]. In fact, under a reaction-time regime, P300 is elicited on both predictable and unpredictable stimuli. Task demands increase in this scenario, as users must decide when to respond in a fast and correct manner. This leads to an enhancement of P300, independently of stimulus predictability [49]. In our study, users were instructed to perform MI commands after stimulus presentation of the word Imagine, and P300 components were analyzed immediately after stimulus onset. Although stimulus presentation during uncued manipulation trials could be considered as predictable, P300 analysis holds validity, as it was executed under a reaction-time regime.

Under those conditions, the results of the two-way ANOVA and one-way ANOVA tests showed statistically significant changes in P300 latency for users , , and . Except for , the tests revealed no statistical significance for P300 amplitude. When comparing the amplitude and latency values from Figures 15 and 16, a general trend was found among users: a decrease in amplitude and an increase in latency. These trends in P300 features were presented along trial repetition, that is, after continuous manipulation of the BCI. These changes in amplitude and latency might be related to the generation of mental fatigue, as they are presented after a continuous execution of manipulation tasks through the BCI. It has been shown that a decrease in P300 amplitude and an increase in latency reflect decreased cognitive processing and lower attention levels [19]. Similar results have been found on a P300-BCI evaluated under different levels of mental workload and fatigue [51]. When comparing subjects performing on the same BCI type, the user with the lowest performance exhibited lower amplitude and higher latency values than the user with the highest performance (although it was more evident for amplitude values). This was observed when comparing both - and -. Subject showed an interesting behavior: an increasing amplitude trend as well as being the only subject which did not show statistical significance on any P300 test. At the same time, it was the subject with the highest performance values on uncued manipulation trials. A possible explanation to this particular case is that after performing manipulation trials on the BCI, mental fatigue affected differently user than the rest of the users. This difference in mental fatigue generation was reflected as nonsignificant changes in P300 parameters during the tests as well as higher performance values.

6. Conclusions

Two BCI systems, a process-control BCI and semiautonomous goal-selection BCI, were implemented and compared in terms of performance and mental fatigue. The process-control BCI allowed users to perform three-dimensional movements on a robotic arm to reach for a target. The semiautonomous BCI allowed the user to execute manipulation tasks, using the same robotic arm, which include reaching, picking, and placing movements successfully. The increase of task complexity represented by the semiautonomous BCI was achieved without compromising the simplicity of the control procedure, as both BCIs were controlled through MI commands. Users performing on semiautonomous BCI obtained higher performance values when compared to users performing on low-level BCI. The difference in task complexity also represented a difference in the mental fatigue experienced by the users on different systems. A P300 amplitude decrease and a latency increase were found as users performed continuous BCI trials, which is consistent with reports of mental fatigue detection on EEG.

We also present strong evidence of the advantages of semiautonomous BCI in terms of performance and mental fatigue. It is also important to address the potential use of the P300 waveform as an indicator of mental fatigue during BCI testing, training, and evaluation. Techniques to further reduce mental fatigue while using BCI systems might provide an increase in BCI patient acceptance rate as well as a possible path to tackle BCI illiteracy. It is of great importance that the user finds the system as nonfatiguing and easy to use in order to provide a more comfortable and efficient assistance. This also facilitates the user in the process of learning how to control the BCI, which can be used together with different strategies to further personalize the system (see, e.g., a previous work by our group in how to select a feedback modality that better enhances the volunteer’s capacity to operate a BCI system [52]).

The development of more advanced semiautonomous BCI systems which provide information about the environment during specific tasks will allow to further enhance performance and usability. Semiautonomous BCIs offer users the possibility to perform more complex tasks in a simple, less fatiguing way. In our system, the integration of the AV and CGA algorithms provided a real-time calculation of the robot’s inverse model, offering flexibility to implement more complex object manipulation tasks in a dynamic environment. The use of a higher DOF robotic arm as well as the implementation of object recognition techniques might improve the complexity of the manipulation tasks to be performed while using the same MI commands to control the BCI, ensuring control simplicity to the users.

Data Availability

The electroencephalography datasets used to support the findings of this study are available from the corresponding author upon request.

Conflicts of Interest

The authors declare that they have no conflicts of interest.

Supplementary Materials

Video #1 (file: VID_20180629_133018082∼2.mp4): this video shows the operation of the semiautonomous BCI system, specifically the part of the robotic arm performing the process of picking the disk and replacing it over one of the target areas. The pick-and-replace process is repeated once the position of all items is changed randomly. Incidentally, the robot places the disk in the green (left) target in both occasions, as that was the one selected by the user in the two trials shown here. Video #2 (file: VID_20180629_133317483∼2.mp4): this video shows the user, in a contiguous room, interacting with the BCI system. The brain activity of the user is measured with the EEG system, and a stimulus is provided through a red arrow in the screen which indicates the MI that the user has to perform. Also, the user receives video feedback of the robot movement. Video #3 (file: VID_20180629_133853176∼2.mp4): this video shows the screen of the computer that processes all the data that are being acquired both by the EEG system and the AV algorithm. The screen also shows the CGA model that is adjusted on the fly based on the positions of the items detected by the AV algorithm and the selected target area where the disk will be placed. (Supplementary Materials)

References

  1. L. Bi, X.-A. Fan, and Y. Liu, “EEG-based brain-controlled mobile robots: a survey,” IEEE Transactions on Human-Machine Systems, vol. 43, no. 2, pp. 161–176, 2013. View at Publisher · View at Google Scholar · View at Scopus
  2. H. Cecotti, “A self-paced and calibration-less SSVEP-based brain-computer interface speller,” IEEE Transactions on Neural Systems and Rehabilitation Engineering, vol. 18, no. 2, pp. 127–133, 2010. View at Publisher · View at Google Scholar · View at Scopus
  3. C. Wang, B. Xia, J. Li et al., “Motor imagery BCI-based robot arm system,” in Proceedings of the 2011 Seventh International Conference on Natural Computation, pp. 181–184, IEEE, Shanghai, China, July 2011. View at Publisher · View at Google Scholar · View at Scopus
  4. D. P. Murphy, O. Bai, A. S. Gorgey et al., “Electroencephalogram-based brain-computer interface and lower-limb prosthesis control: a case study,” Frontiers in Neurology, vol. 8, no. 696, 2017. View at Publisher · View at Google Scholar · View at Scopus
  5. C. J. Bell, P. Shenoy, R. Chalodhorn, and R. P. N. Rao, “Control of a humanoid robot by a noninvasive brain-computer interface in humans,” Journal of Neural Engineering, vol. 5, no. 2, pp. 214–220, 2008. View at Publisher · View at Google Scholar · View at Scopus
  6. J. R. Wolpaw, N. Birbaumer, D. J. McFarland, G. Pfurtscheller, and T. M. Vaughan, “Brain-computer interfaces for communication and control,” Clinical Neurophysiology, vol. 113, no. 6, pp. 767–791, 2002. View at Publisher · View at Google Scholar · View at Scopus
  7. A. S. Royer, M. L. Rose, and B. He, “Goal selection versus process control while learning to use a brain-computer interface,” Journal of Neural Engineering, vol. 8, no. 3, p. 036012, 2011. View at Publisher · View at Google Scholar · View at Scopus
  8. Z. Íşcan and V. V. Nikulin, “Steady state visual evoked potential (SSVEP) based brain-computer interface (BCI) performance under different perturbations,” PLOS ONE, vol. 13, no. 1, Article ID e0191673, 2018. View at Publisher · View at Google Scholar · View at Scopus
  9. R. Fazel-Rezai, B. Z. Allison, C. Guger, E. W. Sellers, S. C. Kleih, and A. Kübler, “P300 brain-computer interface: current challenges and emerging trends,” Frontiers in Neuroengineering, vol. 5, no. 14, pp. 1–14, 2012. View at Publisher · View at Google Scholar · View at Scopus
  10. D. J. McFarland, L. A. Miner, T. M. Vaughan, and J. R. Wolpaw, “Mu and beta rhythm topographies during motor imagery and actual movements,” Brain Topography, vol. 12, no. 3, pp. 177–186, 2000. View at Google Scholar
  11. R. Leeb, D. Friedman, G. R. Muller-Putz, R. Scherer, M. Slater, and G. Pfurtscheller, “Self-paced (asynchronous) BCI control of a wheelchair in virtual environments: a case study with a tetraplegic,” Computational Intelligence and Neuroscience, vol. 2007, Article ID 79642, 8 pages, 2007. View at Publisher · View at Google Scholar · View at Scopus
  12. N. Birbaumer, N. Ghanayim, T. Hinterberger et al., “A spelling device for the paralysed,” Nature, vol. 398, no. 6725, pp. 297-298, 1999. View at Publisher · View at Google Scholar · View at Scopus
  13. J. R. Wolpaw and D. J. McFarland, “Control of a two-dimensional movement signal by a noninvasive brain-computer interface in humans,” Proceedings of the National Academy of Sciences, vol. 101, no. 54, pp. 17849–17854, 2004. View at Publisher · View at Google Scholar · View at Scopus
  14. G. Pfurtscheller, C. Neuper, G. R. Muller et al., “Graz-BCI: state of the art and clinical applications,” IEEE Transactions on Neural Systems and Rehabilitation Engineering, vol. 11, no. 2, pp. 1–4, 2003. View at Publisher · View at Google Scholar · View at Scopus
  15. D. J. McFarland, D. J. Krusienski, W. A. Sarnacki, and J. R. Wolpaw, “Emulation of computer mouse control with a noninvasive brain-computer interface,” Journal of Neural Engineering, vol. 5, no. 2, pp. 101–110, 2008. View at Publisher · View at Google Scholar · View at Scopus
  16. E. V. C. Friedrich, D. J. McFarland, C. Neuper, T. M. Vaughan, P. Brunner, and J. R. Wolpaw, “A scanning protocol for a sensorimotor rhythm-based brain-computer interface,” Biological Psychology, vol. 80, no. 2, pp. 169–175, 2009. View at Publisher · View at Google Scholar · View at Scopus
  17. D. Valbuena, M. Cyriacks, O. Friman, I. Volosyak, and A. Gräser, “Brain-computer interface for high-level control of rehabilitation robotic systems,” in Proceedings of the 2007 IEEE 10th International Conference on Rehabilitation Robotics, pp. 619–625, IEEE, Noordwijk, The Netherlands, June 2007. View at Publisher · View at Google Scholar · View at Scopus
  18. B. Graimann, B. Allison, C. Mandel, T. Lüth, D. Valbuena, and A. Gräser, Non-Invasive Brain-Computer Interfaces for Semi-Autonomous Assistive Devices, Springer London, London, UK, 2008.
  19. S.-Y. Cheng and H.-T. Hsur, Mental Fatigue Measurement Using EEG, Intech Open, London, UK, 2011.
  20. J. B. Isreal, G. L. Chesney, C. D. Wickens, and E. Donchin, “P300 and tracking difficulty: evidence for multiple resources in dual-task performance,” Psychophysiology, vol. 17, no. 3, pp. 259–273, 1980. View at Publisher · View at Google Scholar · View at Scopus
  21. A. Murata, A. Uetake, and Y. Takasawa, “Evaluation of mental fatigue using feature parameter extracted from event-related potential,” International Journal of Industrial Ergonomics, vol. 35, no. 8, pp. 761–770, 2005. View at Publisher · View at Google Scholar · View at Scopus
  22. J. N. Mak, D. J. McFarland, T. M. Vaughan et al., “EEG correlates of P300-based brain-computer interface (BCI) performance in people with amyotrophic lateral sclerosis,” Journal of Neural Engineering, vol. 9, no. 2, Article ID 026014, 2012. View at Publisher · View at Google Scholar · View at Scopus
  23. X. Perrin, R. Chavarriaga, F. Colas, R. Siegwart, and J. D. R. Millán, “Brain-coupled interaction for semi-autonomous navigation of an assistive robot,” Robotics and Autonomous Systems, vol. 58, no. 12, pp. 1246–1255, 2010. View at Publisher · View at Google Scholar · View at Scopus
  24. D. Göhring, D. Latotzky, M. Wang, and R. Rojas, “Semi-autonomous car control using brain-computer interfaces,” in Intelligent Autonomous Systems 12 of Advances in Intelligent Systems and Computing, S. Lee, H. Cho, K.-J. Yoon, and J. Lee, Eds., vol. 2, pp. 393–408, Springer, Berlin, Germany, 2013. View at Google Scholar
  25. D. Hildenbrand, D. Fontijne, Y. Wang, M. Alexa, and L. Dorst, Competitive Runtime Performance for Inverse Kinematics Algorithms Using Conformal Geometric Algebra, EUROGRAPHICS, Aire-la-Ville, Switzerland, 2006.
  26. M. A. Ramírez-Moreno and D. Gutiérrez-Ruiz, “Modeling a robotic arm with conformal geometric algebra in a brain-computer interface,” in Proceedings of the 2018 International Conference on Electronics, Communications and Computers (CONIELECOMP), pp. 11–17, IEEE, Cholula, Mexico, February 2018. View at Publisher · View at Google Scholar · View at Scopus
  27. M. A. Ramírez-Moreno, S. M. Orozco-Soto, J. M. Ibarra-Zannatha, and D. Gutiérrez-Ruiz, “Artificial vision algorithm for object manipulation with a robotic arm in a semi-autonomous brain-computer interface,” in Wearable Robotics: Challenges and Trends of Biosystems & Biorobotics, S. Maria, S. Carrozza, and J. L. Pons, Eds., vol. 22, pp. 187–191, Springer, Cham, Switzerland, 2019. View at Google Scholar
  28. M. W. Spong, S. Hutchinson, and M. Vidyasagar, “Robot modeling and control,” in Chapter Forward and Inverse Kinematics, pp. 85–98, John Wiley & Sons Inc, Hoboken, NJ, USA, 1st edition, 2005. View at Google Scholar
  29. O. Carbajal-Espinosa, L. González-Jiménez, J. Oviedo-Barriga, B. Castillo-Toledo, A. Loukianov, and E. Bayro-Corrochano, “Modeling and pose control of robotic manipulators and legs using conformal geometric algebra,” Computación Y Sistemas, vol. 19, no. 3, pp. 475–486, 2015. View at Publisher · View at Google Scholar · View at Scopus
  30. D. Hildenbrand, J. Zamora, and E. Bayro-Corrochano, “Inverse kinematics computation in computer graphics and robotics using conformal geometric algebra,” Advances in Applied Clifford Algebras, vol. 18, no. 3-4, pp. 699–713, 2008. View at Publisher · View at Google Scholar · View at Scopus
  31. C. Perwass, “Geometric algebra with applications in engineering, geometry and computing,” in Chapter Introduction, pp. 1–23, Springer-Verlag, Berlin, Germany, 2009. View at Google Scholar
  32. A. L. Kleppe and O. Egeland, “Inverse kinematics for industrial robots using conformal geometric algebra,” Modeling, Identification and Control: A Norwegian Research Bulletin, vol. 37, no. 1, pp. 63–75, 2016. View at Publisher · View at Google Scholar · View at Scopus
  33. L. Griggs and F. Fahimi, “Introduction and testing of an alternative control approach for a robotic prosthetic arm,” The Open Biomedical Engineering Journal, vol. 8, no. 1, pp. 93–105, 2014. View at Publisher · View at Google Scholar · View at Scopus
  34. E. I. Organick, A FORTRAN IV Primer, Addison-Wesley, Boston, MA, USA, 1st edition, 1966.
  35. E. Dubrofsky, Homography Estimation, The University of British Columbia, Vancouver, Canada, 2009, Master’s thesis.
  36. Y. Renard, F. Lotte, G. Gibert et al., “OpenViBE: an open-source software platform to design, test, and use brain-computer interfaces in real and virtual environments,” Presence: Teleoperators and Virtual Environments, vol. 19, no. 1, pp. 35–53, 2010. View at Publisher · View at Google Scholar · View at Scopus
  37. F. Lotte, M. Congedo, A. Lécuyer, F. Lamarche, and B. Arnaldi, “A review of classification algorithms for EEG-based brain-computer interfaces,” Journal of Neural Engineering, vol. 4, no. 2, pp. R1–R13, 2007. View at Publisher · View at Google Scholar · View at Scopus
  38. A. Uetake and A. Murata, “Assessment of mental fatigue during VDT task using event-related potential (P300),” in Proceedings of the IEEE International Workshop on Robot and Human Interactive Communication, pp. 235–240, IEEE, Osaka, Japan, September 2000. View at Publisher · View at Google Scholar · View at Scopus
  39. P. J. E. Attwell, S. F. Cooke, and C. H. Yeo, “Cerebellar function in consolidation of a motor memory,” Neuron, vol. 34, no. 6, pp. 1011–1020, 2002. View at Publisher · View at Google Scholar · View at Scopus
  40. P. Praamstra, L. Boutsen, and G. W. Humphreys, “Frontoparietal control of spatial attention and motor intention in human EEG,” Journal of Neurophysiology, vol. 94, no. 1, pp. 764–774, 2005. View at Publisher · View at Google Scholar · View at Scopus
  41. J. R. Naranjo, A. Brovelli, R. Longo, R. Budai, R. Kristeva, and P. P. Battaglini, “EEG dynamics of the frontoparietal network during reaching preparation in humans,” NeuroImage, vol. 34, no. 4, pp. 1673–1682, 2007. View at Publisher · View at Google Scholar · View at Scopus
  42. C. Guger, G. Edlinger, W. Harkam, I. Niedermayer, and G. Pfurtscheller, “How many people are able to operate an EEG-based brain-computer interface (BCI)?” IEEE Transactions on Neural Systems and Rehabilitation Engineering, vol. 11, no. 2, pp. 145–147, 2003. View at Publisher · View at Google Scholar · View at Scopus
  43. J. Jin, Y. Miao, I. Daly, C. Zuo, D. Hu, and A. Cichocki, “Correlation-based channel selection and regularized feature optimization for MI-based BCI,” Neural Networks, vol. 118, pp. 262–270, 2019. View at Publisher · View at Google Scholar
  44. J. K. Feng, J. Jin, I. Daly, J. Zhou, X. Wang, and A. Cichocki, “An optimized channel selection method based on multifrequency CSP-rank for motor imagery-based BCI system,” Computational Intelligence and Neuroscience, vol. 2019, Article ID 8068357, 10 pages, 2019. View at Publisher · View at Google Scholar
  45. J. A. Gaxiola-Tirado, R. Salazar-Varas, and D. Gutiérrez, “Using the partial directed coherence to assess functional connectivity in electroencephalography data for brain–computer interfaces,” IEEE Transactions on Cognitive and Developmental Systems, vol. 10, no. 3, pp. 776–783, 2018. View at Publisher · View at Google Scholar · View at Scopus
  46. F. Lotte, L. Bougrain, A. Cichocki et al., “A review of classification algorithms for EEG-based brain-computer interfaces: a 10 year update,” Journal of Neural Engineering, vol. 15, no. 3, Article ID 031005, 2018. View at Publisher · View at Google Scholar · View at Scopus
  47. P. Shenoy, M. Krauledat, B. Blankertz, R. P. Rao, and K.-R. Mller, “Towards adaptive classification for BCI,” Journal of Neural Engineering, vol. 3, no. 1, pp. R13–R23, 2006. View at Publisher · View at Google Scholar · View at Scopus
  48. S. Guan, K. Zhao, and S. Yang, “Motor imagery EEG classification based on decision tree framework and Riemannian geometry,” Computational Intelligence and Neuroscience, vol. 2019, Article ID 5627156, 13 pages, 2019. View at Publisher · View at Google Scholar · View at Scopus
  49. E. Donchin, M. Kubovy, M. Kutas, R. Johnson, and R. I. Tterning, “Graded changes in evoked response (P300) amplitude as a function of cognitive activity,” Perception & Psychophysics, vol. 14, no. 2, pp. 319–324, 1973. View at Publisher · View at Google Scholar · View at Scopus
  50. R. Verleger and K. Śmigasiewicz, “Do rare stimuli evoke large P3S by being unexpected? A comparison of oddball effects between standard-oddball and prediction-oddball tasks,” Advances in Cognitive Psychology, vol. 12, no. 2, pp. 88–104, 2016. View at Publisher · View at Google Scholar · View at Scopus
  51. I. Käthner, S. C. Wriessnegger, G. R. Müller-Putz, A. Kübler, and S. Halder, “Effects of mental workload and fatigue on the P300, alpha and theta band power during operation of an ERP (P300) brain-computer interface,” Biological Psychology, vol. 102, pp. 118–129, 2014. View at Publisher · View at Google Scholar · View at Scopus
  52. I. N. Angulo-Sherman and D. Gutiérrez, “A link between the increase in electroencephalographic coherence and performance improvement in operating a brain-computer interface,” Computational Intelligence and Neuroscience, vol. 2015, Article ID 824175, 11 pages, 2015. View at Publisher · View at Google Scholar · View at Scopus