We have previously demonstrated that an EEG-controlled web browser based on 
    self-regulation of slow cortical potentials (SCPs) enables severely paralyzed patients to
     browse the internet independently of any voluntary muscle control. However, this system
      had several shortcomings, among them that patients could only browse within a limited number 
      of web pages and had to select links from an alphabetical list, causing problems if the link names 
      were identical or if they were unknown to the user (as in graphical links). Here we describe a new 
      EEG-controlled web browser, called Nessi, which overcomes these shortcomings. In Nessi, the 
      open source browser, Mozilla, was extended by graphical in-place markers, whereby different brain 
      responses correspond to different frame colors placed around selectable items, enabling the user 
      to select any link on a web page. Besides links, other interactive elements are accessible to the 
      user, such as e-mail and virtual keyboards, opening up a wide range of hypertext-based 
      applications.

Michael Bensch

Ahmed A. Karim

Jürgen Mellinger

Thilo Hinterberger

Michael Tangermann

Martin Bogdan

Wolfgang Rosenstiel

Niels Birbaumer

Nessi: An EEG-Controlled Web Browser for  
      Severely Paralyzed Patients

The aim of the present study was to demonstrate for the first time that brain waves can be used by
a tetraplegic to control movements of his wheelchair in virtual reality (VR). In this case study, the spinal
cord injured (SCI) subject was able to generate bursts of beta oscillations in the electroencephalogram
(EEG) by imagination of movements of his paralyzed feet. These beta oscillations were used for 
a self-paced
(asynchronous) brain-computer interface (BCI) control based on a single bipolar EEG recording.
The subject was placed inside a virtual street populated with avatars. The task was 
to “go” from avatar
to avatar towards the end of the street, but to stop at each avatar and talk to them. In average, the
participant was able to successfully perform this asynchronous experiment with a performance 
of 90%,
single runs up to 100%.

Robert Leeb

Doron Friedman

Gernot R. Müller-Putz

Reinhold Scherer

Mel Slater

Gert Pfurtscheller

Self-Paced (Asynchronous) BCI Control of a Wheelchair in Virtual Environments: A Case Study with a Tetraplegic

To be correctly mastered, brain-computer interfaces (BCIs) need an uninterrupted flow 
    of feedback to the user. This feedback is usually delivered through the visual channel. 
    Our aim was to explore the benefits of vibrotactile feedback during users' training 
    and control of  EEG-based BCI applications. A protocol for delivering vibrotactile feedback, 
    including specific hardware and software arrangements, was specified. In three studies 
    with 33 subjects (including 3 with spinal cord injury), we compared vibrotactile and visual 
    feedback, addressing: (I) the feasibility of subjects' training to master their EEG rhythms 
    using tactile feedback; (II) the compatibility of this form of feedback in presence of a visual 
    distracter; (III) the performance in presence of a complex visual task on the same (visual) 
    or different (tactile) sensory channel. The stimulation protocol we developed supports a general 
    usage of the tactors; preliminary experimentations. All studies indicated that the vibrotactile channel 
    can function as a valuable feedback modality with reliability comparable to the classical visual 
    feedback. Advantages of using a vibrotactile feedback emerged when the visual channel was 
    highly loaded by a complex task. In all experiments, vibrotactile feedback felt, after some training, 
    more natural for both controls and SCI users.

Febo Cincotti

Laura Kauhanen

Fabio Aloise

Tapio Palomäki

Nicholas Caporusso

Pasi Jylänki

Donatella Mattia

Fabio Babiloni

Gerolf Vanacker

Marnix Nuttin

Maria Grazia Marciani

José del R. Millán

Vibrotactile Feedback for Brain-Computer Interface Operation

In order to analyze whether the use of the cortical activity, estimated from noninvasive EEG 
    recordings, could be useful to detect mental states related to the imagination of limb movements, we
     estimate cortical activity from high-resolution EEG recordings in a group of  healthy subjects by 
     using realistic head models. Such cortical activity was estimated in region of interest associated 
     with the subject's Brodmann areas by using a depth-weighted minimum norm technique. 
     Results showed that the use of the cortical-estimated activity instead of the unprocessed EEG 
     improves the recognition of the mental states associated to the limb movement imagination in the
      group of normal subjects. The BCI methodology presented here has been used in a group of 
      disabled patients in order to give 
them a suitable control of several electronic devices disposed in a three-room environment devoted
 to the neurorehabilitation. Four of six patients were able to control several electronic devices in this 
 domotic context with the BCI system.

F. Babiloni

F. Cincotti

M. Marciani

S. Salinari

L. Astolfi

A. Tocci

F. Aloise

F. De Vico Fallani

S. Bufalari

D. Mattia

The Estimation of Cortical Activity for Brain-Computer Interface: Applications in a Domotic Context

Brain-computer interface (BCI) systems create a novel communication channel from 
    the brain to an output device bypassing conventional motor output pathways of nerves and 
    muscles. Modern BCI technology is essentially based on techniques for the classification of
     single-trial brain signals. With respect to the topographic patterns of brain
rhythm modulations, the common spatial patterns (CSPs) algorithm has been proven to be very 
useful to produce
subject-specific and discriminative spatial filters; but it didn't consider temporal structures
 of event-related potentials which may be very important for single-trial EEG classification. In 
 this paper, we propose a new framework of
feature extraction for classification of hand movement imagery EEG. Computer simulations on
 real experimental data
indicate that independent residual analysis (IRA) method can provide efficient temporal
 features. Combining IRA
features with the CSP method, we obtain the optimal spatial and temporal features with which 
we achieve the best
classification rate. The high classification rate indicates that the proposed method is promising 
for an EEG-based
brain-computer interface.

Qibin Zhao

Liqing Zhang

Temporal and Spatial Features of Single-Trial  EEG for Brain-Computer Interface

We present the self-paced 3-class Graz brain-computer interface (BCI) which is based on the 
    detection of sensorimotor 
    electroencephalogram (EEG) rhythms induced by motor imagery. Self-paced operation means 
    that the BCI is able to determine 
    whether the ongoing brain activity is intended as control signal (intentional control) or not 
    (non-control state). The presented 
    system is able to automatically reduce electrooculogram (EOG) artifacts, to detect 
    electromyographic (EMG) activity, and uses 
    only three bipolar EEG channels. Two applications are presented: the freeSpace virtual 
    environment (VE) and the Brainloop 
    interface. The freeSpace is a computer-game-like application where subjects have to navigate 
    through the environment and 
    collect coins by autonomously selecting navigation commands. Three subjects participated in
     these feedback experiments 
    and each learned to navigate through the VE and collect coins. Two out of the three succeeded 
    in collecting all three coins. The 
    Brainloop interface provides an interface between the Graz-BCI and Google Earth.

Alois Schloegl

Felix Lee

Horst Bischof

Janez Janša

The Self-Paced Graz Brain-Computer Interface: Methods and Applications

We propose a technique based on independent component analysis (ICA) with 
    constraints, applied to the rhythmic electroencephalographic (EEG) data recorded from a 
    brain-computer interfacing (BCI) system. ICA is a technique that can decompose the recorded 
    EEG into its underlying independent components and in BCI involving motor imagery, the aim
     is to isolate rhythmic activity over the sensorimotor cortex. We demonstrate that, through the 
     technique of spectrally constrained ICA, we can learn a spatial filter suited to each individual 
     EEG recording. This can effectively extract discriminatory information from two types of single-trial
      EEG data. Through the use of the ICA algorithm, the classification accuracy is improved by about 
      25%, on average, compared to the performance on the unpreprocessed data. This implies that
       this ICA technique can be reliably used to identify and extract BCI-related rhythmic activity 
       underlying the recordings where a particular filter is learned for each subject. The high 
       classification rate and low computational cost make it a promising algorithm for application to an 
       online BCI system.

Suogang Wang

Christopher J. James

Extracting Rhythmic Brain Activity for  Brain-Computer Interfacing through Constrained  Independent Component Analysis

We present an ambulatory cognitive state classification system to assess the subject's 
    mental load based on EEG measurements. The ambulatory cognitive state estimator is utilized in 
    the context of a real-time augmented cognition (AugCog) system that aims to enhance the cognitive
     performance of a human user through computer-mediated assistance based on assessments of 
     cognitive states using physiological signals including, but not limited to, EEG. This paper focuses
      particularly on the offline channel selection and feature projection phases of the design and aims 
      to present mutual-information-based techniques that use a simple sample estimator for this 
      quantity. Analyses conducted on data collected from 3 subjects performing 2 tasks (n-back/Larson) 
      at 2 difficulty levels (low/high) demonstrate that the proposed mutual-information-based 
      dimensionality reduction scheme can achieve up to 94% cognitive load estimation accuracy.

Tian Lan

Deniz Erdogmus

Andre Adami

Santosh Mathan

Misha Pavel

Channel Selection and Feature Projection for Cognitive Load Estimation Using Ambulatory EEG

While conventional approaches of BCI feature extraction are based on the power spectrum, we
have tried using nonlinear features for classifying BCI data. In this paper, we report our test results
and findings, which indicate that the proposed method is a potentially useful addition to current
feature extraction techniques.

W. L. Woon

A. Cichocki

Novel Features for Brain-Computer Interfaces

One of the key properties of intelligent behaviors is the capability to learn and adapt to changing environmental conditions. These features are the result of the continuous and intense interaction of the brain with the external world, mediated by the body. For this reason x201C;embodiment” represents an innovative and very suitable experimental paradigm when studying the neural processes underlying learning new behaviors and adapting to unpredicted situations. To this purpose, we developed a novel bidirectional neural interface. We interconnected in vitro neurons, extracted from rat embryos and plated on a microelectrode array (MEA), to external devices, thus allowing real-time closed-loop interaction. The novelty of this experimental approach entails the necessity to explore different computational schemes and experimental hypotheses. In this paper, we present an open, scalable architecture, which allows fast prototyping of different modules and where coding and decoding schemes and different experimental configurations can be tested. This hybrid system can be used for studying the computational properties and information coding in biological neuronal networks with far-reaching implications for the future development of advanced neuroprostheses.

A. Novellino

P. D'Angelo

L. Cozzi

M. Chiappalone

V. Sanguineti

S. Martinoia

Connecting Neurons to a Mobile Robot: An In Vitro Bidirectional Neural Interface

About this journal

Computational Intelligence and Neuroscience is a forum for the interdisciplinary field of neural computing, neural engineering and artificial intelligence. The journal’s focus is on intelligent systems for computational neuroscience.

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Chief Editor, Professor Cichocki, engages in world-leading research in the field of artificial intelligence and biomedical applications of advanced data analytics technologies.

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Computational Intelligence and Neuroscience | Hindawi

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Computational Intelligence and Neuroscience is a forum for the interdisciplinary field of neural computing, neural engineering and artificial intelligence. The journal’s focus is on intelligent systems for computational neuroscience.

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Computational Intelligence and Neuroscience

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Call for papers

Andrzej Cichocki

Shangkai Gao