F.  Babiloni

A.  Cichocki

S.  Gao

Brain-Computer Interfaces: Towards Practical Implementations and Potential Applications

Classification of multichannel EEG recordings during motor imagination has been exploited successfully for brain-computer
interfaces (BCI). In this paper, we consider EEG signals as the outputs of a networked dynamical system (the cortex), and exploit
synchronization features from the dynamical system for classification. Herein, we also propose a new framework for
learning optimal filters automatically from the data, by employing a Fisher ratio criterion. Experimental evaluations comparing the
proposed dynamical system features with the CSP and the AR features reveal their competitive performance during
classification. Results also show the benefits of employing the spatial and the temporal filters optimized using the proposed learning approach.

Le Song

Julien Epps

Classifying EEG for Brain-Computer Interface: Learning Optimal Filters for Dynamical System Features

The aim of the contribution is to analyze possibilities of high-resolution movement classification using human EEG. For this purpose, a database of the EEG recorded during right-thumb and little-finger fast flexion movements of the experimental subjects was created. The statistical analysis of the EEG was done on the subject's basis instead of the commonly used grand averaging. Statistically significant differences between the EEG accompanying movements of both fingers were found, extending the results of other so far published works. The classifier based on hidden Markov models was able to distinguish between movement and resting states (classification score of 94–100%), but it was unable to recognize the type of the movement. This is caused by the large fraction of other (nonmovement related) EEG activities in the recorded signals. A classification method based on advanced EEG signal denoising is being currently developed to overcome this problem.

Jakub Štastný

Pavel Sovka

High-Resolution Movement EEG Classification

Brain-computer interfaces based on functional magnetic resonance imaging (fMRI-BCI) allow volitional control of anatomically specific regions of the brain. Technological advancement in higher field MRI scanners, fast data acquisition sequences, preprocessing algorithms, and robust statistical analysis are anticipated to make fMRI-BCI more widely available and applicable. This noninvasive technique could potentially complement the traditional neuroscientific experimental methods by varying the activity of the neural substrates of a region of interest as an independent variable to study its effects on behavior. If the neurobiological basis of a disorder (e.g., chronic pain, motor diseases, psychopathy, social phobia, depression) is known in terms of abnormal activity in certain regions of the brain, fMRI-BCI can be targeted to modify activity in those regions with high specificity for treatment. In this paper, we review recent results of the application of fMRI-BCI to neuroscientific research and psychophysiological treatment.

Ranganatha Sitaram

Andrea Caria

Ralf Veit

Tilman Gaber

Giuseppina Rota

Andrea Kuebler

Niels Birbaumer

fMRI Brain-Computer Interface: A Tool for Neuroscientific Research and Treatment

Modern electrophysiological studies in animals show that the spectrum of neural 
oscillations encoding relevant information is broader than 
previously thought and that many diverse areas are engaged for very simple tasks. However, 
EEG-based brain-computer interfaces 
(BCI) still employ as control modality relatively 
slow brain rhythms or features derived from preselected 
frequencies and scalp locations. Here, we describe the 
strategy and the algorithms we have developed for the analysis of 
electrophysiological data and demonstrate their capacity to 
lead to faster accurate decisions based on linear classifiers. 
To illustrate this strategy, we analyzed two typical BCI tasks. (1) Mu-rhythm control of a cursor 
movement by a paraplegic patient. For this data, we show that although the patient received 
extensive training in mu-rhythm control, valuable information about movement imagination is present 
on the untrained high-frequency rhythms. This is the first demonstration of the importance of high-frequency
 rhythms in imagined limb movements. (2) Self-paced finger tapping task in three healthy subjects including
  the data set used in the BCI-2003 competition. We show that by selecting electrodes and frequency ranges
   based on their discriminative power, the classification rates can be systematically improved with respect to
    results published thus far.

Rolando Grave de Peralta Menendez

Quentin Noirhomme

Febo Cincotti

Donatella Mattia

Fabio Aloise

Sara González Andino

Modern Electrophysiological Methods for  Brain-Computer Interfaces

We propose a combination of blind source separation (BSS) and independent component 
analysis (ICA) (signal decomposition into artifacts and nonartifacts) with support vector machines (SVMs) 
(automatic classification)
that are designed for online usage. In order to select a suitable BSS/ICA method, three ICA algorithms 
(JADE, Infomax, and FastICA) and one BSS algorithm (AMUSE) are evaluated to determine their ability to
 isolate electromyographic (EMG) and electrooculographic (EOG) artifacts into individual components. An
  implementation of the selected BSS/ICA method with SVMs trained to classify EMG and EOG artifacts, which 
  enables the usage of the method as a filter in measurements with online feedback, is described. This filter is
   evaluated on three BCI datasets as a proof-of-concept of the method.

Sebastian Halder

Michael Bensch

Jürgen Mellinger

Martin Bogdan

Andrea Kübler

Wolfgang Rosenstiel

Online Artifact Removal for Brain-Computer Interfaces Using Support Vector Machines and Blind Source Separation

Most existing brain-computer interfaces (BCIs) detect specific mental activity in
          a so-called synchronous paradigm. Unlike synchronous systems which are operational at
         specific system-defined periods, self-paced (asynchronous) interfaces have the advantage of
           being operational at all times. The low-frequency asynchronous switch design (LF-ASD) is a
        2-state self-paced BCI that detects the presence of a specific finger movement in the ongoing EEG.
     Recent evaluations of the 2-state LF-ASD show an average true positive rate of 41&#37; at the fixed false
      positive rate of 1&#37;. This paper proposes two designs for a 3-state self-paced BCI that is capable
       of handling idle brain state. The two proposed designs aim at detecting right- and left-hand
     extensions from the ongoing EEG. They are formed of two consecutive detectors. The first detects
the presence of a right- or a left-hand movement and the second classifies the detected movement
 as a right or a left one. In an offline analysis of the EEG data collected from four able-bodied
     individuals, the 3-state brain-computer interface shows a comparable performance with a 2-state
  system and significant performance improvement if used as a 2-state BCI, that is, in detecting the
  presence of a right- or a left-hand movement (regardless of the type of movement). It has an average
  true positive rate of 37.5&#37; and 42.8&#37; (at false positives rate of 1&#37;) in detecting right- and left-hand
   extensions, respectively, in the context of a 3-state self-paced BCI and average detection rate
 of 58.1&#37; (at false positive rate of 1&#37;) in the context of a 2-state self-paced BCI.

Ali  Bashashati

Rabab K. Ward

Gary E. Birch

Towards Development of a 3-State Self-Paced Brain-Computer Interface

This paper describes a concept for the extension of constraint-induced movement therapy 
   (CIMT) through the use of feedback of primary motor cortex activity. CIMT requires residual
    movement to act as a source of feedback to the patient, thus preventing its application to those
     with no perceptible movement. It is proposed in this paper that it is possible to provide feedback 
     of the motor cortex effort to the patient by measurement with near infrared spectroscopy 
     (NIRS). Significant changes in such effort may be used to drive rehabilitative robotic actuators, for 
     example. This may provide a possible avenue for extending CIMT to patients hitherto excluded as 
     a result of severity of condition. In support of such a paradigm, this paper details the current status 
     of CIMT and related attempts to extend rehabilitation therapy through the application of 
     technology. An introduction to the relevant haemodynamics is given including a description of
      the basic technology behind a suitable NIRS system. An illustration of the proposed therapy is 
      described using a simple NIRS system driving a robotic arm during simple upper-limb unilateral
       isometric contraction exercises with healthy subjects.

Tomas E. Ward

Christopher J. Soraghan

Fiachra Matthews

Charles Markham

A Concept for Extending the Applicability of Constraint-Induced Movement Therapy through Motor Cortex Activity Feedback Using a Neural Prosthesis

Movement-disabled persons typically require a long practice time to learn how to use a brain-computer
interface (BCI). Our aim was to develop a BCI which tetraplegic subjects could
control only in 30 minutes. Six such subjects (level of injury C4-C5) operated a 6-channel
EEG BCI. The task was to move a circle from the centre of the computer screen to its right or
left side by attempting visually triggered right- or left-hand movements. During the training
periods, the classifier was adapted to the user's EEG activity after each movement attempt in a
supervised manner. Feedback of the performance was given immediately after starting the BCI
use. Within the time limit, three subjects learned to control the BCI. We believe that fast initial
learning is an important factor that increases motivation and willingness to use BCIs. We have
previously tested a similar single-trial classification approach in healthy subjects. Our new
results show that methods developed and tested with healthy subjects do not necessarily work
as well as with motor-disabled patients. Therefore, it is important to use motor-disabled persons
as subjects in BCI development.

Laura Kauhanen

Pasi Jylänki

Janne Lehtonen

Pekka Rantanen

Hannu Alaranta

Mikko Sams

EEG-Based Brain-Computer Interface for Tetraplegics

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.

Ahmed A. Karim

Thilo Hinterberger

Michael Tangermann

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

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Computational Intelligence and Neuroscience

Brain-Computer Interfaces: Towards Practical Implementations and Potential Applications

Brain-Computer Interfaces: Towards Practical Implementations and Potential Applications

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