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- Publisher
- Springer Journals
- Copyright
- Copyright © Korean Society of Medical and Biological Engineering 2020
- ISSN
- 2093-9868
- eISSN
- 2093-985X
- DOI
- 10.1007/s13534-020-00152-x
- Publisher site
- See Article on Publisher Site
Abstract
The objective of the proposed research is to classify electroencephalography (EEG) data of covert speech words. Six subjects were asked to perform covert speech tasks i.e mental repetition of four different words i.e ‘left’, ‘right’, ‘up’ and ‘down’. Fifty trials for each word recorded for every subject. Kernel-based Extreme Learning Machine (kernel ELM) was used for multiclass and binary classification of EEG signals of covert speech words. We achieved a maximum multiclass and binary classification accuracy of (49.77%) and (85.57%) respectively. The kernel ELM achieves significantly higher accuracy compared to some of the most commonly used classification algorithms in Brain–Computer Interfaces (BCIs). Our findings suggested that covert speech EEG signals could be successfully classified using kernel ELM. This research involving the classification of covert speech words potentially leading to real-time silent speech BCI research.
Journal
Biomedical Engineering Letters – Springer Journals
Published: May 3, 2020