The comparison of EEG features in specific Mandarin words’ speech

Tsai, W-S; Huang, S-G; Huang, K-C; Chen, J-C; Lin, C-T

The comparison of EEG features in specific Mandarin words’ speech

Tsai, W-S Huang, S-G Huang, K-C Chen, J-C Lin, C-T

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Publisher:: IEEE
Publication Type:: Conference Proceeding
Citation:: 2023 International Conference on Fuzzy Theory and Its Applications (iFUZZY), 2023, 00, pp. 1-4
Issue Date:: 2023-11-22

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Field	Value	Language
dc.contributor.author	Tsai, W-S
dc.contributor.author	Huang, S-G
dc.contributor.author	Huang, K-C
dc.contributor.author	Chen, J-C
dc.contributor.author	Lin, C-T
dc.date	2023-10-26
dc.date.accessioned	2024-03-05T03:50:04Z
dc.date.available	2024-03-05T03:50:04Z
dc.date.issued	2023-11-22
dc.identifier.citation	2023 International Conference on Fuzzy Theory and Its Applications (iFUZZY), 2023, 00, pp. 1-4
dc.identifier.isbn	9798350305791
dc.identifier.uri	http://hdl.handle.net/10453/176127
dc.description.abstract	Due to advancements in brain signal application technology there has been a growing focus on the human speech Brain Computer Interface BCI in recent years An essential initial phase in crafting speech recognition systems is comprehending the inherent features of nature speech processing To ensure the decoding capability of human speech BCI researchers have to investigate the differences between overt speech and imagined speech and they also need to find the best paradigm to build a reliable BCI Thus this study compared the differences in brain dynamics between overt speech and imagined speech while using the four Mandarin words up down left and right We first calculated the EEG alpha and beta band power for multiple channels situated in the regions of Broca s and Wernicke s areas specifically F5 F7 FC3 FC5 and C5 to analyze the brain activity changes while speaking or imagining speech Second we used Daubechies 4 db4 wavelet to do a 7 level decomposition to the EEG signal The corresponding discrete wavelet transform DWT coefficients were further calculated with six statistical features for support vector machine SVM classification We found that the speak overt speech task and the imagine imagined speech task resulted in significantly different alpha band power changes and the beta band shows the same phenomenon The result of average classification accuracy is 71 97 for speak and imagine distinction In conclusion we analyzed the brain dynamics and find that overt speech and imagined speech could be differentiate through DWT coefficients features This may pave the way to establish a Mandarin imagined speech BCI
dc.language	en
dc.publisher	IEEE
dc.relation.ispartof	2023 International Conference on Fuzzy Theory and Its Applications (iFUZZY)
dc.relation.ispartof	2023 International Conference on Fuzzy Theory and Its Applications
dc.relation.isbasedon	10.1109/ifuzzy60076.2023.10324150
dc.rights	info:eu-repo/semantics/restrictedAccess
dc.title	The comparison of EEG features in specific Mandarin words’ speech
dc.type	Conference Proceeding
utslib.citation.volume	00
utslib.location.activity	Penghu, Taiwan
pubs.organisational-group	University of Technology Sydney
pubs.organisational-group	University of Technology Sydney/Faculty of Engineering and Information Technology
pubs.organisational-group	University of Technology Sydney/Strength - AAII - Australian Artificial Intelligence Institute
pubs.organisational-group	University of Technology Sydney/Faculty of Engineering and Information Technology/School of Computer Science
utslib.copyright.status	recently_added	*
dc.date.updated	2024-03-05T03:50:03Z
pubs.finish-date	2023-10-29
pubs.publication-status	Published
pubs.start-date	2023-10-26
pubs.volume	00

Abstract:

Due to advancements in brain signal application technology there has been a growing focus on the human speech Brain Computer Interface BCI in recent years An essential initial phase in crafting speech recognition systems is comprehending the inherent features of nature speech processing To ensure the decoding capability of human speech BCI researchers have to investigate the differences between overt speech and imagined speech and they also need to find the best paradigm to build a reliable BCI Thus this study compared the differences in brain dynamics between overt speech and imagined speech while using the four Mandarin words up down left and right We first calculated the EEG alpha and beta band power for multiple channels situated in the regions of Broca s and Wernicke s areas specifically F5 F7 FC3 FC5 and C5 to analyze the brain activity changes while speaking or imagining speech Second we used Daubechies 4 db4 wavelet to do a 7 level decomposition to the EEG signal The corresponding discrete wavelet transform DWT coefficients were further calculated with six statistical features for support vector machine SVM classification We found that the speak overt speech task and the imagine imagined speech task resulted in significantly different alpha band power changes and the beta band shows the same phenomenon The result of average classification accuracy is 71 97 for speak and imagine distinction In conclusion we analyzed the brain dynamics and find that overt speech and imagined speech could be differentiate through DWT coefficients features This may pave the way to establish a Mandarin imagined speech BCI

Please use this identifier to cite or link to this item:

http://hdl.handle.net/10453/176127