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| 題名: | Improvement of classification accuracy in a phase-tagged steady-state visual evoked potential-based brain computer interface using multiclass support vector machine |
| 作者: | 李柏磊;Yeh, Chia-Lung;Lee, Po-Lei;Chen, Wei-Ming;Chang, Chun-Yen;Wu, Yu-Te;Lan, Gong-Yau |
| 貢獻者: | 資訊電機學院電機工程學系 |
| 關鍵詞: | Adult;Attention;Biomarkers;Biomaterials;Biomedical Engineering and Bioengineering;Biomedical Engineering/Biotechnology;Biotechnology;Brain;Brain-Computer Interfaces;Calibration;Computer engineering;Design;Electrodes;Electroencephalography;Engineering;Equipment Design;Evoked Potentials, Visual;Female;Fixation, Ocular - physiology;Hospitals;Humans;Male;Nonlinear Dynamics;Physiological aspects;Science education;Studies;Support Vector Machine;Visual evoked response;Young Adult |
| 日期: | 2013-05-21 |
| 上傳時間: | 2026-04-23 14:15:45 (UTC+8) |
| 出版者: | BioMed Central Ltd.;London: BioMed Central |
| 摘要: | 摘要： Background Brain computer interface (BCI) is an emerging technology for paralyzed patients to communicate with external environments. Among current BCIs, the steady-state visual evoked potential (SSVEP)-based BCI has drawn great attention due to its characteristics of easy preparation, high information transfer rate (ITR), high accuracy, and low cost. However, electroencephalogram (EEG) signals are electrophysiological responses reflecting the underlying neural activities which are dependent upon subject’s physiological states (e.g., emotion, attention, etc.) and usually variant among different individuals. The development of classification approaches to account for each individual’s difference in SSVEP is needed but was seldom reported. Methods This paper presents a multiclass support vector machine (SVM)-based classification approach for gaze-target detections in a phase-tagged SSVEP-based BCI. In the training steps, the amplitude and phase features of SSVEP from off-line recordings were used to train a multiclass SVM for each subject. In the on-line application study, effective epochs which contained sufficient SSVEP information of gaze targets were first determined using Kolmogorov-Smirnov (K-S) test, and the amplitude and phase features of effective epochs were subsequently inputted to the multiclass SVM to recognize user’s gaze targets. Results The on-line performance using the proposed approach has achieved high accuracy (89.88 ± 4.76%), fast responding time (effective epoch length = 1.13 ± 0.02 s), and the information transfer rate (ITR) was 50.91 ± 8.70 bits/min. Conclusions The multiclass SVM-based classification approach has been successfully implemented to improve the classification accuracy in a phase-tagged SSVEP-based BCI. The present study has shown the multiclass SVM can be effectively adapted to each subject’s SSVEPs to discriminate SSVEP phase information from gazing at different gazed targets.
其他題名： BioMed Eng OnLine
其他題名： Biomed Eng Online
出版者： London: BioMed Central
出版日期： 2013-05-21
出處： Biomedical engineering online, 2013-05, Vol.12 (1), p.46-46, Article 46
資源來源： Publicly available content database
版權： Yeh et al.; licensee BioMed Central Ltd. 2013 This article is published under license to BioMed Central Ltd. This is an Open Access article distributed under the terms of the Creative Commons Attribution License ( ), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
版權： COPYRIGHT 2013 BioMed Central Ltd.
版權： 2013 Yeh et al.; licensee BioMed Central Ltd. This is an Open Access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/2.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
版權： Copyright © 2013 Yeh et al.; licensee BioMed Central Ltd. 2013 Yeh et al.; licensee BioMed Central Ltd.
識別號： ISSN: 1475-925X
識別號： EISSN: 1475-925X
識別號： DOI: 10.1186/1475-925X-12-46
識別號： PMID: 23692974 |
| 顯示於類別: | [電機工程學系] 期刊論文
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