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

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 electroencep...

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Bibliographic Details
Published in:Medical & biological engineering & computing 2019-12, Vol.57 (12), p.2705-2715
Main Authors: Atum, Yanina, Pacheco, Marianela, Acevedo, Rubén, Tabernig, Carolina, Biurrun Manresa, José
Format: Article
Language:English
Subjects:
Adult
Algorithms
Biomedical and Life Sciences
Biomedical Engineering and Bioengineering
Biomedicine
Brain
Brain - physiology
Brain-Computer Interfaces
Channels
Cognitive ability
Computer Applications
Configuration management
EEG
Electroencephalography
Electroencephalography - methods
Event-related potentials
Event-Related Potentials, P300 - physiology
Evoked Potentials - physiology
Female
Genetic algorithms
Human Physiology
Human-computer interface
Humans
Imaging
Implants
Interfaces
Male
Original Article
Performance indices
Radiology
Young Adult
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Summary: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.
ISSN:0140-0118
1741-0444
DOI:10.1007/s11517-019-02065-z