Current trends in Graz Brain-Computer Interface (BCI) research.

This paper describes a research approach to develop a brain-computer interface (BCI) based on recognition of subject-specific EEG patterns. EEG signals recorded from sensorimotor areas during mental imagination of specific movements are classified on-line and used e.g. for cursor control. In a number of on-line experiments, various methods for EEG feature extraction and classification have been evaluated.

Frequency component selection for an EEG-based brain to computer interface.

A new communication channel for severely handicapped people could be opened with a direct brain to computer interface (BCI). Such a system classifies electrical brain signals online. In a series of training sessions, where electroencephalograph (EEG) signals are recorded on the intact scalp, a classifier is trained to discriminate a limited number of different brain states. In a subsequent series of feedback sessions, where the subject is confronted with the classification results, the subject tries to reduce the number of misclassifications. In this study the relevance of different spectral components is analyzed: 1) on the training sessions to select optimal frequency bands for the feedback sessions and 2) on the feedback sessions to monitor changes.

Feature extraction for on-line EEG classification using principal components and linear discriminants.

The study focuses on the problems of dimensionality reduction by means of principal component analysis (PCA) in the context of single-trial EEG data classification (i.e. discriminating between imagined left- and right-hand movement). The principal components with the highest variance, however, do not necessarily carry the greatest information to enable a discrimination between classes. An EEG data set is presented where principal components with high variance cannot be used for discrimination. In addition, a method based on linear discriminant analysis (LDA), is introduced that detects principal components which can be used for discrimination, leading to data sets of reduced dimensionality but similar classification accuracy.

EEG-based discrimination between imagination of right and left hand movement.

Three subjects were asked to imagine either right or left hand movement depending on a visual cue stimulus. The interval between two consecutive imagination tasks was > 10 s. Each subject imagined a total of 160 hand movements in each of 3-4 sessions (training) without feedback and 7-8 sessions with feedback. The EEG was recorded bipolarly from left and right central and parietal regions and was sampled at 128 Hz. In the feedback sessions, the EEG from both central channels was classified on-line with a neural network classifier, and the success of the discrimination between left and right movement imagination was given within 1.5 s by means of a visual feedback. For each subject, different frequency components in the alpha and beta band were found which provided best discrimination between left and right hand movement imagination. These frequency bands varied between 9 and 14 Hz and between 18 and 26 Hz. The accuracy of on-line classification was approximately 80% in all 3 subjects and did not improve with increasing number of sessions. By averaging over all training and over all feedback sessions, the EEG data revealed a significant desynchronisation (ERD) over the contralateral central area and synchronisation (ERS) over the ipsilateral side. The ERD/ERS patterns over all sessions displayed a relatively small intra-subject variability with slight differences between sessions with and without feedback.

On-line EEG classification during externally-paced hand movements using a neural network-based classifier.

EEGs of 6 normal subjects were recorded during sequences of periodic left or right hand movement. Left or right was indicated by a visual cue. The question posed was: 'Is it possible to move a cursor on a monitor to the right or left side using the EEG signals for cursor control?' For this purpose the EEG during performance of hand movement was analyzed and classified on-line. A neural network in form of a learning vector quantizertion (LVQ) with an input dimension of 16 was trained to classify EEG patterns from two electrodes and two time windows. After two training sessions on 2 different days, 4 subjects showed a classification accuracy of 89-100%. For two subjects classification was not possible. These results show that in general movement specific EEG-patterns can be found, classified in real time and used to move a cursor on a monitor to the left or right. On-line EEG classification is necessary when the EEG is used as input signal to a brain computer interface (BCI). Such a BCI can be a help for handicapped people.

EEG-based brain computer interface (BCI). Search for optimal electrode positions and frequency components.

Several laboratories around the world have recently started to investigate EEG-based brain computer interface (BCI) systems in order to create a new communication channel for subjects with severe motor impairments. The present paper describes an initial evaluation of 64-channel EEG data recorded while subjects used one EEG channel over the left sensorimotor area to control on-line vertical cursor movement. Targets were given at the top or bottom of a computer screen. Data from 3 subjects in the early stages of training were analyzed by calculating band power time courses and maps for top and bottom targets separately. In addition, the Distinction Sensitive Learning Vector Quantizer (DSLVQ) was applied to single-trial EEG data. It was found that for each subject there exist optimal electrode positions and frequency components for on-line EEG-based cursor control.

Visualization of sensorimotor areas involved in preparation for hand movement based on classification of mu and central beta rhythms in single EEG trials in man.

It is well known that mu and central beta rhythms start to desynchronize > 1 s before active hand or finger movement. To investigate whether the same cortical areas are involved in desynchronization of mu and central beta rhythms, 56-channel EEG recordings were made during right- and left-finger flexions in three normal subjects. The event-related desynchronization (ERD) was quantified in single EEG trials and classified by the Distinction Sensitive Learning Vector Quantization (DSLVQ) algorithm. This DSLVQ selects the most relevant features (electrode positions) for discrimination between the preparatory state for left- and right-finger movements. It was found that the most important electrode positions were close to the primary hand area. However, in all three subjects the focus of the central beta ERD was slightly anterior to the focus of mu desynchronization. This can be interpreted that different neural networks are involved in the generation of mu and central beta rhythms.

Selection of electrode positions for an EEG-based brain computer interface (BCI).

One major question in designing an EEG-based Brain Computer Interface to bypass the normal motor pathways is the selection of proper electrode positions. This study investigates electrode selection with a Distinction Sensitive Learning Vector Quantizer (DSLVQ). DSLVQ is an extended Learning Vector Quantizer (LVQ) which employs a weighted distance function for dynamical scaling and feature selection. The data analysed and classified were 56-channel EEG recordings over sensorimotor areas during preparation for discrete left or right index finger flexions. Data from 3 subjects are reported. It was found by DSLVQ that the most important electrode positions for differentiation between planning of left and right finger movement overlie cortical finger/hand areas over both hemispheres.