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

• Published in: Medical & biological engineering & computing Vol. 57; no. 12; pp. 2705 - 2715
• Main Authors: Atum, Yanina, Pacheco, Marianela, Acevedo, Rubén, Tabernig, Carolina, Biurrun Manresa, José
• Format: Journal Article
• Language: English
• Published: Berlin/Heidelberg Springer Berlin Heidelberg 01.12.2019; Springer Nature B.V
• 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; Brain-computer interface; Recursive feature elimination; Genetic algorithm; P300; Subject-independent channel selection
• ISSN: 0140-0118; 1741-0444; 1741-0444
• DOI: 10.1007/s11517-019-02065-z

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.