Energy extraction method for EEG channel selection

• Published in: TELKOMNIKA (Telecommunication Computing Electronics and Control) Vol. 17; no. 5; pp. 2561 - 2571
• Main Authors: Fauzi, Hilman, Azzam, M. Abdullah, Shapiai, Mohd. Ibrahim, Kyoso, Masaki, Khairuddin, Uswah, Komura, Tadayasu
• Format: Journal Article
• Language: English; Japanese
• Published: Yogyakarta Universitas Ahmad Dahlan 01.10.2019; Ahmad Dahlan University
• Subjects: Algorithms; Brain research; channel selection; Channels; Classification; Datasets; EEG-based BCI; Electroencephalography; Energy; energy calculation; energy extraction; energy selection; Energy value; Engineering; International conferences; Methods; Process selection; Research methodology; Signal processing; Studies
• ISSN: 1693-6930; 2302-9293
• DOI: 10.12928/telkomnika.v17i5.12805

[...]one drawback of EEG signals is that over fit to noise increases with the number of task-irrelevant features [6, 7]. Fourthly, it is crucial to reduce the number of EEG channels as well as to maintain good reliability to improve the portability and practicability of BCI systems [12]. [...]this study introduced a channel selection method to improve BCI performance, namely the energy extraction method. [...]the HV method finds the best combination of selected channels by highest energy without calculating the difference in energy value between the closest energy values in the selected channels. [...]the HV method will yield the highest accuracy without being affected by the number of channels. 4.1.2.Automatic Selection The automatic energy selection method selects the best energy and combination of channels in one process selection. In the future, this framework could use datasets with a larger number of channels or electrodes. Besides that, because of the results of the test that yielded better and improved performance, the energy extraction method could also define the common channels in different subjects or sessions in a dataset to improve upon the results.