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Medical & Biological Engineering & Computing

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14.11.2019 | Original Article

A comparison of subject-dependent and subject-independent channel selection strategies for single-trial P300 brain computer interfaces

verfasst von: Yanina Atum, Marianela Pacheco, Rubén Acevedo, Carolina Tabernig, José Biurrun Manresa

Erschienen in: Medical & Biological Engineering & Computing | Ausgabe 12/2019

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Abstract

Brain computer interfaces (BCI) represent an alternative for patients whose cognitive functions are preserved, but are unable to communicate via conventional means. A commonly used BCI paradigm is based on the detection of event-related potentials, particularly the P300, immersed in the electroencephalogram (EEG). In order to transfer laboratory-tested BCIs into systems that can be used by at homes, it is relevant to investigate if it is possible to select a limited set of EEG channels that work for most subjects and across different sessions without a significant decrease in performance. In this work, two strategies for channel selection for a single-trial P300 brain computer interface were evaluated and compared. The first strategy was tailored specifically for each subject, whereas the second strategy aimed at finding a subject-independent set of channels. In both strategies, genetic algorithms (GAs) and recursive feature elimination algorithms were used. The classification stage was performed using a linear discriminant. A dataset of EEG recordings from 18 healthy subjects was used test the proposed configurations. Performance indexes were calculated to evaluate the system. Results showed that a fixed subset of four subject-independent EEG channels selected using GA provided the best compromise between BCI setup and single-trial system performance.

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Titel: A comparison of subject-dependent and subject-independent channel selection strategies for single-trial P300 brain computer interfaces
verfasst von: Yanina Atum
Marianela Pacheco
Rubén Acevedo
Carolina Tabernig
José Biurrun Manresa
Publikationsdatum: 14.11.2019
Verlag: Springer Berlin Heidelberg
Erschienen in: Medical & Biological Engineering & Computing / Ausgabe 12/2019
Print ISSN: 0140-0118
Elektronische ISSN: 1741-0444
DOI: https://doi.org/10.1007/s11517-019-02065-z

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