Open AccessMulticlass covert speech classification using extreme learning machine
Author(s) -
Dipti Pawar,
Sudhir Dhage
Publication year - 2020
Publication title -
biomedical engineering letters
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.709
H-Index - 26
eISSN - 2093-985X
pISSN - 2093-9868
DOI - 10.1007/s13534-020-00152-x
Subject(s) - covert , binary classification , extreme learning machine , speech recognition , computer science , electroencephalography , multiclass classification , support vector machine , artificial intelligence , brain–computer interface , pattern recognition (psychology) , kernel (algebra) , binary number , psychology , artificial neural network , mathematics , arithmetic , philosophy , linguistics , combinatorics , psychiatry
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.