The University of Southampton
University of Southampton Institutional Repository

Adaptive BCI based on variational Bayesian Kalman filtering: an empirical evaluation

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
0018-9294
719 - 727
Sykacek, P.
42669570-7a15-4e78-9b14-5615c0a5fcd4
Roberts, S.J.
d4ee9d51-aa0b-4385-92a0-68d9e2d895ae
Stokes, M.
71730503-70ce-4e67-b7ea-a3e54579717f
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), 719 - 727. (doi:10.1109/TBME.2004.824128).

Record type: Article

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.

This record has no associated files available for download.

More information

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

Identifiers

Local EPrints ID: 18072
URI: http://eprints.soton.ac.uk/id/eprint/18072
ISSN: 0018-9294
PURE UUID: f543cfae-cfcc-4821-a186-c8031c33d3db
ORCID for M. Stokes: ORCID iD orcid.org/0000-0002-4204-0890

Catalogue record

Date deposited: 25 Nov 2005
Last modified: 16 Mar 2024 03:30

Export record

Altmetrics

Contributors

Author: P. Sykacek
Author: S.J. Roberts
Author: M. Stokes ORCID iD

Download statistics

Downloads from ePrints over the past year. Other digital versions may also be available to download e.g. from the publisher's website.

View more statistics

Atom RSS 1.0 RSS 2.0

Contact ePrints Soton: eprints@soton.ac.uk

ePrints Soton supports OAI 2.0 with a base URL of http://eprints.soton.ac.uk/cgi/oai2

This repository has been built using EPrints software, developed at the University of Southampton, but available to everyone to use.

We use cookies to ensure that we give you the best experience on our website. If you continue without changing your settings, we will assume that you are happy to receive cookies on the University of Southampton website.

×