title: Adaptive BCI based on variational Bayesian Kalman filtering: an empirical evaluation.
abstract: This paper proposes the use of variational Kalman filtering as an inference technique for adaptive classification in a brain computer interface (BCI). The proposed algorithm translates electroencephalogram segments adaptively into probabilities of cognitive states. It, thus, allows for nonstationarities in the joint process over cognitive state and generated EEG which may occur during a consecutive number of trials. Nonstationarities may have technical reasons (e.g., changes in impedance between scalp and electrodes) or be caused by learning effects in subjects. We compare the performance of the proposed method against an equivalent static classifier by estimating the generalization accuracy and the bit rate of the BCI. Using data from two studies with healthy subjects, we conclude that adaptive classification significantly improves BCI performance. Averaging over all subjects that participated in the respective study, we obtain, depending on the cognitive task pairing, an increase both in generalization accuracy and bit rate of up to 8%. We may, thus, conclude that adaptive inference can play a significant contribution in the quest of increasing bit rates and robustness of current BCI technology. This is especially true since the proposed algorithm can be applied in real time.
authors.affiliation: Department of Engineering Science, University of Oxford, Parks Road, OX1 3PJ Oxford, UK. peter@sykacek.net
authors.forename: Peter
authors.initials: P
authors.lastname: Sykacek
authors.forename: Stephen J
authors.initials: SJ
authors.lastname: Roberts
authors.forename: Maria
authors.initials: M
authors.lastname: Stokes
mesh_terms: D000465:Algorithms; D001499:Bayes Theorem; D001921:Brain / Q000502:physiology*; D003071:Cognition / Q000502:physiology*; D003143:Communication Devices for People with Disabilities; D003936:Diagnosis, Computer-Assisted / Q000379:methods*; D004569:Electroencephalography / Q000379:methods*; D005071:Evoked Potentials / Q000502:physiology; D005246:Feedback; D006801:Humans; D008953:Models, Anatomic; D015233:Models, Statistical; D012815:Signal Processing, Computer-Assisted; D013269:Stochastic Processes; D013598:Systems Theory; D014584:User-Computer Interface