EM-CSP: An efficient multiclass common spatial pattern feature method for speech imagery EEG signals recognition

• Published in: Biomedical signal processing and control Vol. 84; p. 104933
• Main Authors: Alizadeh, Danial, Omranpour, Hesam
• Format: Journal Article
• Language: English
• Published: Elsevier Ltd 01.07.2023
• Subjects: Brain computer interface (BCI); Common spatial pattern (CSP); EEG signals; Imagined speech; KNN classification; Multiclass CSP; Multiclass CSP; Imagined speech; EEG signals; KNN classification; Common spatial pattern (CSP); Brain computer interface (BCI)
• ISSN: 1746-8094; 1746-8108
• DOI: 10.1016/j.bspc.2023.104933

Brain-computer interface (BCI) technology has many applications in various scientific fields, such as used in communication (speech recognition). The data of imagery speech has been collected in electroencephalogram (EEG) signals. In this paper, we propose an approach for EEG feature extraction of imagined speech with high accuracy and efficiency. In this way, we improve the common spatial pattern (CSP) binary algorithm to multiclass level in two parts ‘One-vs-One’ and ‘One-vs-All’. The “Kara One” dataset is used in this research that includes EEG signals of thirteen subjects with twelve trials and sixty-four channels for any four English words signals and seven English phonemes signals. We compared our proposed CSP to other imagined speech feature methods. The classification accuracy of the second part of the proposed method is 97.34% in the subject-wise overall model which is 19.97% better than the best previous result. We have obtained the highest classification accuracy for sixty-four channels, which is the highest accuracy ever achieved using this database. Our proposed model is ready to be tested with more EEG data. This proposed work, which includes an ensemble method for classifying speech imagery words, can greatly contribute to intuitive BCI development using silent speech.