Recognizing Tonal and Non-Tonal Mandarin Sentences for EEG-based Brain-Computer Interface

Yang, SR; Jung, TP; Lin, CT; Huang, KC; Wei, CS; Chiueh, H; Hsin, YL; Liou, GT; Wang, LC

Recognizing Tonal and Non-Tonal Mandarin Sentences for EEG-based Brain-Computer Interface

Yang, SR Jung, TP Lin, CT Huang, KC Wei, CS Chiueh, H Hsin, YL Liou, GT Wang, LC

Permalink

Publisher:: Institute of Electrical and Electronics Engineers (IEEE)
Publication Type:: Journal Article
Citation:: IEEE Transactions on Cognitive and Developmental Systems, 2022, 14, (4), pp. 1666-1677
Issue Date:: 2022-01-01

Recently Added

	Filename	Description	Size
	Recognizing_Tonal_and_Nontonal_Mandarin_Sentences_for_EEG-Based_BrainComputer_Interface.pdf	Published version	3.22 MB	Adobe PDF	View/Open

Copyright Clearance Process

Recently Added
In Progress
Open Access

This item is new to OPUS and is not currently available.

Full metadata record

Field	Value	Language
dc.contributor.author	Yang, SR
dc.contributor.author	Jung, TP
dc.contributor.author	Lin, CT
dc.contributor.author	Huang, KC
dc.contributor.author	Wei, CS
dc.contributor.author	Chiueh, H
dc.contributor.author	Hsin, YL
dc.contributor.author	Liou, GT
dc.contributor.author	Wang, LC
dc.date.accessioned	2023-03-23T04:42:52Z
dc.date.available	2023-03-23T04:42:52Z
dc.date.issued	2022-01-01
dc.identifier.citation	IEEE Transactions on Cognitive and Developmental Systems, 2022, 14, (4), pp. 1666-1677
dc.identifier.issn	2379-8920
dc.identifier.issn	2379-8939
dc.identifier.uri	http://hdl.handle.net/10453/168208
dc.description.abstract	Most current research has focused on non-tonal languages such as English. However, more than 60world’s population speaks tonal languages. Mandarin is the most spoken tonal languages in the world. Interestingly, the use of tone in tonal languages may represent different meanings of words and reflect feelings, which is very different from non-tonal languages. The objective of this study is to determine whether a spoken Mandarin sentence with or without tone can be distinguished by analyzing electroencephalographic signals (EEG). We first constructed a new Brain Research Center Speech (BRCSpeech) database to recognize Mandarin. The EEG data of 14 participants were recorded, while they articulated pre-selected sentences. To our knowledge, this is the first study to apply the method of asymmetric feature extraction method for speech recognition using EEG signals. This study shows that the feature extraction method of Rational Asymmetry (RASM) can achieve the best accuracy in the classification of cross-subjects. In addition, our proposed Binomial Variable Algorithm methodology can achieve 98.82% accuracy in cross-subject classification. Furthermore, we demonstrate that the use of eight channels ((F7, F8), (C5, C6), (P5, P6), and (O1, O2)) can achieve an accurate of 94.44%. This study explores the neuro-physiological correlation of Mandarin pronunciation, which can help develop a tonal language synthesis system based on BCI in the future.
dc.language	en
dc.publisher	Institute of Electrical and Electronics Engineers (IEEE)
dc.relation.ispartof	IEEE Transactions on Cognitive and Developmental Systems
dc.relation.isbasedon	10.1109/TCDS.2021.3137251
dc.rights	info:eu-repo/semantics/restrictedAccess
dc.rights	This work is licensed under a Creative Commons Attribution 4.0 License.
dc.title	Recognizing Tonal and Non-Tonal Mandarin Sentences for EEG-based Brain-Computer Interface
dc.type	Journal Article
utslib.citation.volume	14
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	*
pubs.consider-herdc	false
dc.date.updated	2023-03-23T04:42:49Z
pubs.issue	4
pubs.publication-status	Published
pubs.volume	14
utslib.citation.issue	4

Abstract:

Most current research has focused on non-tonal languages such as English. However, more than 60world’s population speaks tonal languages. Mandarin is the most spoken tonal languages in the world. Interestingly, the use of tone in tonal languages may represent different meanings of words and reflect feelings, which is very different from non-tonal languages. The objective of this study is to determine whether a spoken Mandarin sentence with or without tone can be distinguished by analyzing electroencephalographic signals (EEG). We first constructed a new Brain Research Center Speech (BRCSpeech) database to recognize Mandarin. The EEG data of 14 participants were recorded, while they articulated pre-selected sentences. To our knowledge, this is the first study to apply the method of asymmetric feature extraction method for speech recognition using EEG signals. This study shows that the feature extraction method of Rational Asymmetry (RASM) can achieve the best accuracy in the classification of cross-subjects. In addition, our proposed Binomial Variable Algorithm methodology can achieve 98.82% accuracy in cross-subject classification. Furthermore, we demonstrate that the use of eight channels ((F7, F8), (C5, C6), (P5, P6), and (O1, O2)) can achieve an accurate of 94.44%. This study explores the neuro-physiological correlation of Mandarin pronunciation, which can help develop a tonal language synthesis system based on BCI in the future.

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

http://hdl.handle.net/10453/168208