EEG-based Brain-Computer Interface Using Subject-Specific Spatial Filters

G. Pfurtscheiler, C. Guger, H. Ramoser

Department of Medical Informatics, Institute of Biomedical Engng. and

Ludwig Boltzmann Institute of Medical Informatics and Neuroinformatics, University of Technology, Graz, Austria

hfffeldgasse 16a, 8010 Graz e-mail: pfu @dpmi.tu-graz.ac.at Telephone: +43-316-873-5300

Fax: +43-316-812964

Key Words: Brain-Computer Interface (BCI), single-trial EEG classification, common spatial filter, motor imagery, event-related desynchronization

Abstract. Sensorimotor EEG rhythms are affected by motor imagery and can, therefore, be used as input signals for an EEG-based brain-computer interface (BCI). Satisfactory classification rates of imagery-related EEG patterns can be activated when multiple EEG recordings and the method of common spatial patterns is used for parameter estimation. Data from 3 BCI experiments with and without feedback are reported.

1 Motor imagery and brain waves

Sensorimotor EEG rhythms such as mu and central beta rhythms display an event- related desynchronization (ERD) not only with execution of hand movement but also with imagination of the same or a similar type of movement [8]. Imagination of right and left hand movement can therefore be used as a mental strategy to realize an EEG-based brain computer interface (BCI) [10]. Examples of high-resolution ERD maps based on a realistic head model obtained from magnetic resonance imaging (MRI) during left and right hand movement imagery are displayed in Fig. 1. It can be seen that the ERD is circumscribed and localized over the contralateral sensorimotor hand area.

249

Fig. 1. ERD maps calculated for a realistic head model during imagination of left and right hand movement. The ERD focus is indicated by dense "isopotential" lines

Although the imagery-related ERD forms a focus close to the hand representation area, one or two EEG signals recorded either from one or both hemispheres are insufficient to describe the state of brain activation during motor imagery. Therefore, it is understandable that the BCI system, using either 1 or 2 EEG channels for parameter estimation and control of cursor movement in 2 directions (e.g. cursor up and down), can achieve only an accuracy of 80-90% after about 10 sessions [5,7,10]. It can be expected that the analysis and classification of a large number of EEG signals recorded over sensorimotor areas may improve the classification accuracy of a BCI.

It was shown recently by off-line analysis of 56-channel EEG data from a motor imagery experiment that EEG patterns during left and right motor imagery could be discriminated in 3 healthy subjects with an accuracy of 90.8%, 92.7% and 99.7%, respectively [11]. For this discrimination the common spatial pattern (CSP) method was used [3,6]. With this CSP-method variance-related feature vectors from 2 populations of EEG patterns are extracted and used for classification. It is therefore of interest, whether the CSP-method can be used for on-line BCI sessions with continuous feedback [7] and what classification accuracy can be achieved after e.g. only 3 days of training.

2 Common Spatial Filter

The CSP-method lead to new time series that are optimal for discriminating 2 populations of EEG patterns related to right and left motor imagery. The method is based on the simultaneous diagonalization of 2 covariance matrices [1]. The imagery-related EEG pattern (E) recorded from m electrodes is multiplied by a mapping matrix W. The first two and last two rows (time series) of the resulting matrix Z (Z=WE) are best suitable to discriminate the 2 populations of EEG patterns and are used to construct the weight vector for the classifier. The components (features) used for classification are the logarithm of the normalized variances of the time series obtained by spatial filtering (for details see [6]).

250

3 On-line EEG Classification

Three students participated in the BCI experiment, all experienced with the BCI (subjects g3, g7, i2). Each student imagined 80 left and 80 right hand movements per session whereby the side of imagination was indicated by an arrow on a monitor pointing either to the left or to the right (for details see [2,10]. The experimental paradigm is shown in Fig. 2.

Fig. 2. Experimental paradigm for EEG data collection during motor imagery without feedback.

EEG was recorded from 27 electrodes closely spaced over left and right sensorimotor areas. Amplified EEG signals filtered between 8-30 Hz, sampled at 128 Hz and cleared of artifacts were used for calculating subject-specific common spatial filters and weight vectors. All sessions with and without feedback were performed within only 3 days. A typical example for one subject (g7) is given in Fig. 3.

Feedback (FB) was given in form of the outline of a rectangle. Immediately after the arrow (cue) disappeared, the feedback stimulus appeared in the center of the screen and began to extend horizontally toward the right or left side. The subject's task was to extend this feedback bar toward the left or right boundary of the screen, depending on the direction of the arrow (cue stimulus; see also Fig. 2) presented before. During a 3.75- second period the bar was moving to the right or left side of the screen according to the results of tile on-line analysis (linear distance function as described before).

251

Fig. 3. Flowchart of 6 BCI sessions with and without feedback for subject g7 within 3 days. Altogether 3 CSP filters and 4 weight vectors (WV) were calculated.

As an example, the procedure used with subject g7 is described in detail. The experiment was started (1" day) without FB in session 1. The subject imagined 80 right and 80 left hand movements according to the paradigm shown in Fig. 2. From these 27- channel EEG data a first common spatial filter (CSPI) and a first weight vector 1 (WV1) were calculated. These CSPI and WVI were used to classify the EEG data on-line in the following session 2 with FB on the next day (2 ~ day). As a result of the classification between 2 imagination classes no discrimination (accuracy about 50%) was achieved. Therefore on the same day (2 "J day) another session (session 3 in Fig. 3) without FB was performed and a new weight vector (WV2) with the CSPI was calculated. Using CSPI and WV2 in session 4 with FB a classification accuracy of 68% was obtained. Repeating

252

the update procedure twice (see Fig. 3) in session 6 with FB on the 3 ~ day a classification accuracy of 94% was achieved. The time courses of the on-line classification for all subjects are displayed in Fig. 4.

g 3 =:

g7

60

50

40

20

0

2 2.5 3 3 5 4 4.5 5 5 5 6 6.5 7 7 5 8

Class i f ica t ion T ime Point In s e c o n d s

2 2.5 3 3.5 4 4.5 5 5.5 6 6.5 7 7 5 8

Class i f ica t ion T ime Point in s e c o n d s

i2 ~

2 2 5 3 3 5 4 4.5 5 5.5 6 6 5 7 7.5 8

Class i f ica t ion T ime Point in s e c o n d s

Fig. 4. Time courses of on-line classification error over a period of 6 seconds, starting 1 second before visual cue presentation (from second 3 to 4.25). Summarized data of all 3 subjects are shown. Subjects g7 and g3 participated in 4 and subject i2 in 5 sessions with FB. Instead of the classification accuracy, the error rate (100%: minimum classification accuracy) is displayed.

253

Subject i2 started, similar to subject g7, without any classification power (50% classification accuracy) in session 2. After calculation of 3 spatial filters and 5 weight vectors a classification accuracy of 96% was achieved in session 7 with FB.

In contrast to subjects g7 and i2, subject g3 started in the first FB-session with an accuracy close to 80%. In the last FB-session the classification rate was 98%.

4 Conclusion

The application of subject-specific spatial filters is a suitable method for on-line classification of multichannel EEG data recorded during imagination of hand movement. Important is that not only the spatial filters are updated in the course of sessions, but also the classifier. For example the reason for the 50%-accuracy in session 2 with FB in subject g7 was the biased classification results, meaning the feedback bar on the monitor was always pointing in one direction. After calculation of a new classifier (weight vector) the accuracy increase from 50% to 68%.

The CSP method is sensitive to electrode positions. Therefore, it is recommended to use the same electrode montage to calculate of the spatial filters, to set up the classifier and Ibr the next FB session. In this sense it has to be remembered that the ERD pattern of sensorimotor rhythms can be completely different on 2 electrode positions over the hand representation area, when the electrode distance over the scalp is smaller than 2.5 cm [9]. A further disadvantage is the large number of electrodes needed for the CSP-method. The problem of a large number of electrodes and the precise positioning is solved, however, when implanted electrode arrays are used for recording sensorimotor rhythms. Such electrode arrays will be available in the near future for BCI applications in patients with severe motor disabilities [4].

Most importantly it was shown for the first time that a high classification rate can be achieved within only 3 days of training when multichannel EEG data in connection with a BCI is used.

Acknowledgements

This research was supported by the "Fonds zur F6rderung der wissenschaftlichen Forschung" project PI1208MED, the "Steierm~irkische Landesregierung" and the "AIIgemeine Unfallversicherungsanstalt, AUVA" in Austria.

254

References

1. Fukunaga, K.: Introduction to statistical pattern recognition, Academic Press, (1972) 2. Guger, C., Schloegl, A., Walterspacher, D., Pfurtscheller, G.: Design of an EEG-based Brain-

Computer Interface (BCI) from standard components running in real-time under Windows. Biomed. Technik, (1999) in press.

3. Koles, Z.J.: The quantitative extraction and topographic mapping of the abnormal components in the clinical EEG. Electroenceph. Clin. Neurophysiol. 79 (1991) 440-447.

4. Maynard, E.M., Nordhausen, C.T., Normann, R.A.: The Utah intracortical electrode array: a recording structure for potential brain-computer interfaces. Electroenceph. Clin. Neurophysiol. 102 (1997) 228-239.

5. McFarland, D.J., McCane, L.M., Wolpaw, J.R.: EEG-Based communication and control: short- term role of feedback. IEEE Trans. Rehab. Engng., 6 (1998) 7-11.

6. MUller-Gerking, J., Pfurtscheller, G., Flyvbjerg, H.: Designing optimal spatial filters for single- trial EEG classification in a movement task. Electroenceph. Clin. Neurophysiol. (1999) ill press.

7. Neuper, C., SchlOgl, A., Pfurtscheller, G.: Enhancement of left-right sensorimotor EEG differences during feedback-regulated motor imagery. J. Clin. Neurophysiol. (1999) in press.

8. Pfurtscheller, G., Neuper, C.: Motor imagery activates primary sensorimotor area in humans. Neuroscience Letters, 239 (1997) 65-68.

9. Pfurtschelter, G., Neuper, C., Berger, J.: Source localization using event-related desynchronization (ERD) within the alpha band. Brain Topography. 6/4 (1994) 269-275.

10. Pfurtscheller, G., Neuper, Ch., Flotzinger, D., Pregenzer, M.: EEG-based discrimination between imagination of right and left hand movement. Electroenceph. clin. Neurophysiol. 103 (1997) 642-651.

11. Ramoser, H., Milller-Gerking, J., Pfurtscheller, G.: Optimal spatial filtering of single-trial EEG during imagined hand movements, IEEE Rehab. Engng. (1999) submitted