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Cognitive tasks for driving a brain computer interfacing system: a pilot study

Cognitive tasks for driving a brain computer interfacing system: a pilot study
Cognitive tasks for driving a brain computer interfacing system: a pilot study
Different cognitive tasks were investigated for use with a brain-computer interface (BCI). The main aim was to evaluate which two of several candidate tasks lead to patterns of electroencephalographic (EEG) activity that could be differentiated most reliably and, therefore, produce the highest communication rate. An optimal signal processing method was also sought to enhance differentiation of EEG profiles across tasks. In ten normal subjects (five male), aged 29-54 years, EEG activity was recorded from four channels during cognitive tasks grouped in pairs, and performed alternately. Four imagery tasks were: spatial navigation around a familiar environment; auditory imagery of a familiar tune; and right and left motor imagery of opening and closing the hand. Signal processing methodology included autoregressive (AR) modeling and classification based on logistic regression and a nonlinear generative classifier. The highest communication rate was found using the navigation and auditory imagery tasks. In terms of classification performance and, hence, possible communication rate, these results were significantly better (p<0.05) than those obtained with the classical pairing of motor tasks involving imaginary movements of the left and right hands. In terms of EEG data analysis, a nonlinear classification model provided more robust results than a linear model (p/spl Lt/0.01), and a lower AR model order than those used in previous work was found to be effective. These findings have implications for establishing appropriate methods to operate BCI systems, particularly for disabled people who may experience difficulty with motor tasks, even motor imagery.
brain
1534-4320
48 - 54
Curran, E.
0a86fd85-963a-4190-a608-e8a6a55b8c87
Sykacek, P.
42669570-7a15-4e78-9b14-5615c0a5fcd4
Stokes, M.
71730503-70ce-4e67-b7ea-a3e54579717f
Roberts, S.
fc6a3991-f095-4a92-8501-56faabcfbd90
Penny, W.
c9098d74-652e-467b-8b8a-56ccb5b94542
Johnsrude, I.
6cde761f-f32b-44cd-990f-2ccdfc84510e
Owen, A.
59da220a-698b-44c3-8e42-89966615677e
Curran, E.
0a86fd85-963a-4190-a608-e8a6a55b8c87
Sykacek, P.
42669570-7a15-4e78-9b14-5615c0a5fcd4
Stokes, M.
71730503-70ce-4e67-b7ea-a3e54579717f
Roberts, S.
fc6a3991-f095-4a92-8501-56faabcfbd90
Penny, W.
c9098d74-652e-467b-8b8a-56ccb5b94542
Johnsrude, I.
6cde761f-f32b-44cd-990f-2ccdfc84510e
Owen, A.
59da220a-698b-44c3-8e42-89966615677e

Curran, E., Sykacek, P., Stokes, M., Roberts, S., Penny, W., Johnsrude, I. and Owen, A. (2004) Cognitive tasks for driving a brain computer interfacing system: a pilot study. IEEE Transactions on Neural Systems and Rehabilitation Engineering, 12 (1), 48 - 54. (doi:10.1109/TNSRE.2003.821372).

Record type: Article

Abstract

Different cognitive tasks were investigated for use with a brain-computer interface (BCI). The main aim was to evaluate which two of several candidate tasks lead to patterns of electroencephalographic (EEG) activity that could be differentiated most reliably and, therefore, produce the highest communication rate. An optimal signal processing method was also sought to enhance differentiation of EEG profiles across tasks. In ten normal subjects (five male), aged 29-54 years, EEG activity was recorded from four channels during cognitive tasks grouped in pairs, and performed alternately. Four imagery tasks were: spatial navigation around a familiar environment; auditory imagery of a familiar tune; and right and left motor imagery of opening and closing the hand. Signal processing methodology included autoregressive (AR) modeling and classification based on logistic regression and a nonlinear generative classifier. The highest communication rate was found using the navigation and auditory imagery tasks. In terms of classification performance and, hence, possible communication rate, these results were significantly better (p<0.05) than those obtained with the classical pairing of motor tasks involving imaginary movements of the left and right hands. In terms of EEG data analysis, a nonlinear classification model provided more robust results than a linear model (p/spl Lt/0.01), and a lower AR model order than those used in previous work was found to be effective. These findings have implications for establishing appropriate methods to operate BCI systems, particularly for disabled people who may experience difficulty with motor tasks, even motor imagery.

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More information

Published date: 2004
Keywords: brain

Identifiers

Local EPrints ID: 17866
URI: http://eprints.soton.ac.uk/id/eprint/17866
ISSN: 1534-4320
PURE UUID: 31ec4595-4187-400d-8fa7-a177c4788a33
ORCID for M. Stokes: ORCID iD orcid.org/0000-0002-4204-0890

Catalogue record

Date deposited: 17 Nov 2005
Last modified: 03 Dec 2019 01:51

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