Devlaminck, Dieter

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Ghent University Academic Bibliography

Developing a Brain-Computer Interface: principlesand techniquesDieter DevlaminckSupervisor(s): Prof. Dr. Ir. L. Boullart, Dr. G. Otte, Prof. Dr. P. Santens, Dr. B. WynsI. INTRODUCTIONBrain-Computer Interfaces (BCI) are a newkind of human-machine interfaces emerging onthe horizon. They form a communication path-way between the brain and a machine. Thiscan be achieved by measuring brain signals andtranslate them directly into control commands.Such a system allows people with severe motordisabilities to manipulate their environment inan altnerative way. However there’s still a lot ofwork to be done to make it usable in daily life.This article presents our prototype BCI. Thereare a lot of different types of BCI systems, butours is based on Event-Related Synchronisationand Desynchronisation (ERS and ERD), bothphysiological phenomena that occur in the brainwhen performing motor tasks (see section II and[1]). In order to detect these effects, we firstconstruct spatial filters (see section IV and [2])before building our classifier.II. NEUROPHYSIOLOGYIn our setup we want to discriminate sin-gle trial electroencephalographic (EEG) signalsgenerated by left and right hand motor tasks.It’s no coincidence we use these tasks becausethey are represented by different regions of themotor area of the cortex. For example, whenwe move our right hand the left hemisphere be-comes active and starts to desynchronize whilethe neurons of the right hemisphere keeps fir-ing at a synchronized idle rhythm. It’s thisrhythm that is attenuated in the contralateral—————————————————–D. Devlaminck is with the Department of Elec-trical Energy, Systems and Automation, Ghent Uni-versity (UGent), Gent, Belgium. E-mail: Di-eter.Devlaminck@UGent.be .hemisphere when we start moving our hands.The rhythm varies strongly from person to per-son but is mostly situated around 10 and 20 Hz.The attenuation in power at these frequenciescorresponds to a lower variance in the time do-main. We use this property in the section IV toextract good features.It’s important to note that ERD and ERS effectsnot only occur in real movement tasks, but alsoin imaginary motor tasks. Herein conceals thepower of the method to use it as alternative con-trol input.III. PRE- AND POSTPROCESSINGBefore calculating the spatial filters (see sec-tion IV) we go through two preprocessing steps.First we reject trials based on their outlierness.Outlierness is specified by the variance of thetrial because artefacts induce higher variancethan normal trials.Second, we carefully select the passband of thetemporal filter. Therefore we implemented asimple heuristic searching for frequencies withsignificantly different power between classes.After calculating the spatial filters we selectthose that discriminate best between the twoclasses, figure 1 shows two such filters, focus-ing on the respective motor areas. In a last stepwe select the model trained on the time win-dow which shows the best classification accu-racy. In order to do that, we divide each epoch(typically one second before the movement on-set and three seconds after) in overlapping win-dows of one second. The features and the classi-fication model are then computed for each timewindow. The model and the time window withthe best results are saved and used for validationon the test set.IV. COMMON SPATIAL PATTERNS (CSP)To detect these ERD/ERS effects in singletrial EEG signals we construct spatial filtersthat maximize the variance (ERS) for one classand at the same time minimize it (ERD) for theother class. This can be formulated as follows,WΣ1WT = D and WΣ2WT = I −DWith the rows of W the filters, Σ1 the co-variance matrix of the signals from the firstclass and Σ2 the covariance matrix for the sec-ond class. D is a diagonal matrix of which thenon-zero elements correspond with the relativevariances of the signals. We use this insight inthe postprocessing step, mentioned in sectionIII, to select good spatial filters. For example, afilter that corresponds with a diagonal elementnear one for a particular class will produce asignal having a relative high variance for thatclass. At the same time a signal of the otherclass will have a relative low variance after ap-plication of this filter. The variances of thesespatially filtered signals are then used as fea-tures for the classifier.Figure 1. Two of the most discriminative spatial fil-ters for the ’Day 2’ set, selected based on theirrelative variance.V. RESULTSWe test the procedure on three differentdatasets, one from the BCI Competition [3] andtwo of a colleague recorded on different days.Table 1 shows the average results on the diffe-rent test sets. Each set is divided in two, thefirst half is used for training, the second halffor testing. The training set itself is randomlydivided in 20% validation trials and 80% train-ing trials. We run the algorithm five times perset. This results in five models and thus five dif-ferent classification results which we average.We also reject 5% of the trials based on theiroutlierness. A suprising result, but confirmedby the findings of other researchers, is the rangeof the selected time windows: a lot of them liebefore the movement onset, meaning the brainprepares the movement before we actually exe-cute it.Manually HeuristicDay 1 83% 88%Day 2 82% 91%BCI Competition 95% 95%Table 1. The first column presents the results whenwe manually fix the passband between 8-12 Hzand 18-22 Hz (neurophysiologically most plau-sible), the second column presents the resultswhen the passband boundaries are selected by theheuristic.VI. CONCLUSIONS AND FUTURE WORKWe presented the usefulness of CSP for BCIand showed the importance of selecting the cor-rect passband. However there’s still a lot ofroom for improvement. Removing a certainpercentage of the trials can have a negative im-pact on the performance because it’s also possi-ble that prototype trials are removed. Thereforewe will try to remove these artefacts instead ofrejecting the whole trial, which is obviously notpossible in realtime applications of BCI.REFERENCES[1] G. Pfurtscheller and F. H. Lopes da Silva, “Event-related eeg/meg synchronization and desynchro-nization: basic principles,” Clin Neurophysiol,vol. 110, pp. 1842–1857, November 1999.[2] H. Ramoser, J. Muller-gerking, andG. Pfurtscheller, “Optimal spatial filteringof single trial eeg during imagined hand move-ment,” IEEE Trans. Rehab. Eng, vol. 8, pp.441–446, 2000.[3] G. Dornhege, B. Blankertz, G. Curio, and K. R.Muller, “Boosting bit rates in noninvasive eegsingle-trial classifications by feature combinationand multiclass paradigms,” IEEE Trans. Biomed.Eng., vol. 51, no. 6, pp. 993–1002, 2004.

Developing a brain-computer interface: principles and techniques

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Developing a brain-computer interface: principles and techniques

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