2019年
Comparison of Classifiers for the Transfer Learning of Affective Auditory P300-Based BCIs
2019 41ST ANNUAL INTERNATIONAL CONFERENCE OF THE IEEE ENGINEERING IN MEDICINE AND BIOLOGY SOCIETY (EMBC)
- ,
- 巻
- 2019
- 号
- 開始ページ
- 6766
- 終了ページ
- 6769
- 記述言語
- 英語
- 掲載種別
- 研究論文(国際会議プロシーディングス)
- DOI
- 10.1109/EMBC.2019.8856320
- 出版者・発行元
- IEEE
The auditory P300-based BCI was improved by changing stimuli. However, the current method needed time for recording training data. The time can be saved by the subject-to-subject transfer learning. However, the suitable classifier for the learning remains unknown. As a first step, this study compared the classifiers for the transfer learning of the BCI. They were evaluated on the dataset of a five-class affective auditory P300-based BCI. EEG data from sixteen subjects were assigned for the training, then data from the other six subjects were used for the testing. Classifiers such as the linear support-vector machine (SVM lin.), the kernel SVM (SVM RBF), the quadratic discriminant analysis were applied and compared. As a result, the SVM lin. and the SVM RBF were suitable for this problem. The best mean classification accuracy was achieved by the SVM lin. (68.7%), and a subject showed 86% accuracy at best. These results suggest that some subjects can operate the BCI without recording his/her training data.
- リンク情報
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- DOI
- https://doi.org/10.1109/EMBC.2019.8856320
- DBLP
- https://dblp.uni-trier.de/rec/conf/embc/OnishiN19
- PubMed
- https://www.ncbi.nlm.nih.gov/pubmed/31947394
- Web of Science
- https://gateway.webofknowledge.com/gateway/Gateway.cgi?GWVersion=2&SrcAuth=JSTA_CEL&SrcApp=J_Gate_JST&DestLinkType=FullRecord&KeyUT=WOS:000557295307046&DestApp=WOS_CPL
- URL
- https://www.wikidata.org/entity/Q92701305
- URL
- https://dblp.uni-trier.de/conf/embc/2019
- URL
- https://dblp.uni-trier.de/db/conf/embc/embc2019.html#OnishiN19
- ID情報
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- DOI : 10.1109/EMBC.2019.8856320
- ISSN : 1557-170X
- eISSN : 1558-4615
- ISBN : 9781538613115
- DBLP ID : conf/embc/OnishiN19
- PubMed ID : 31947394
- Web of Science ID : WOS:000557295307046