EM-CSP: An efficient multiclass common spatial pattern feature method for speech imagery EEG signals recognition
Biomedical Signal Processing and Control, ISSN: 1746-8094, Vol: 84, Page: 104933
2023
- 3Citations
- 21Captures
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Example: if you select the 1-year option for an article published in 2019 and a metric category shows 90%, that means that the article or review is performing better than 90% of the other articles/reviews published in that journal in 2019. If you select the 3-year option for the same article published in 2019 and the metric category shows 90%, that means that the article or review is performing better than 90% of the other articles/reviews published in that journal in 2019, 2018 and 2017.
Citation Benchmarking is provided by Scopus and SciVal and is different from the metrics context provided by PlumX Metrics.
Article Description
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.
Bibliographic Details
http://www.sciencedirect.com/science/article/pii/S174680942300366X; http://dx.doi.org/10.1016/j.bspc.2023.104933; http://www.scopus.com/inward/record.url?partnerID=HzOxMe3b&scp=85152623273&origin=inward; https://linkinghub.elsevier.com/retrieve/pii/S174680942300366X; https://dx.doi.org/10.1016/j.bspc.2023.104933
Elsevier BV
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