Adaptive BCI based on variational Bayesian Kalman filtering: an empirical evaluation
Adaptive BCI based on variational Bayesian Kalman filtering: an empirical evaluation
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.
algorithms, article, bayes theorem, brain, cognition, communication aids for disabled, diagnosis computer-assisted, electroencephalography, evoked potentials, feedback, human, methods, models anatomic, models statistical, physiology, signal processing computer-assisted, stochastic processes, support non-us governmentt, systems theory, user-computer interface
719 - 727
Sykacek, P.
42669570-7a15-4e78-9b14-5615c0a5fcd4
Roberts, S.J.
d4ee9d51-aa0b-4385-92a0-68d9e2d895ae
Stokes, M.
71730503-70ce-4e67-b7ea-a3e54579717f
May 2004
Sykacek, P.
42669570-7a15-4e78-9b14-5615c0a5fcd4
Roberts, S.J.
d4ee9d51-aa0b-4385-92a0-68d9e2d895ae
Stokes, M.
71730503-70ce-4e67-b7ea-a3e54579717f
Sykacek, P., Roberts, S.J. and Stokes, M.
(2004)
Adaptive BCI based on variational Bayesian Kalman filtering: an empirical evaluation.
IEEE Transactions on Biomedical Engineering, 51 (5), .
(doi:10.1109/TBME.2004.824128).
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.
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Published date: May 2004
Keywords:
algorithms, article, bayes theorem, brain, cognition, communication aids for disabled, diagnosis computer-assisted, electroencephalography, evoked potentials, feedback, human, methods, models anatomic, models statistical, physiology, signal processing computer-assisted, stochastic processes, support non-us governmentt, systems theory, user-computer interface
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Local EPrints ID: 18072
URI: http://eprints.soton.ac.uk/id/eprint/18072
ISSN: 0018-9294
PURE UUID: f543cfae-cfcc-4821-a186-c8031c33d3db
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Date deposited: 25 Nov 2005
Last modified: 16 Mar 2024 03:30
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Author:
P. Sykacek
Author:
S.J. Roberts
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