Convolutional neural network architecture and input volume matrix design for ERP classifications in a tactile P300-based Brain-Computer Interface

Takumi Kodama, Shoji Makino

研究成果: Conference contribution

3 被引用数 (Scopus)

抄録

In the presented study we conduct the off-line ERP classification using the convolutional neural network (CNN) classifier for somatosensory ERP intervals acquired in the full-body tactile P300-based Brain-Computer Interface paradigm (fbBCI). The main objective of the study is to enhance fbBCI stimulus pattern classification accuracies by applying the CNN classifier. A 60 × 60 squared input volume transformed by one-dimensional somatosensory ERP intervals in each electrode channel is input to the convolutional architecture for a filter training. The flattened activation maps are evaluated by a multilayer perceptron with one-hidden-layer in order to calculate classification accuracy results. The proposed method reveals that the CNN classifier model can achieve a non-personal-training ERP classification with the fbBCI paradigm, scoring 100 % classification accuracy results for all the participated ten users.

本文言語English
ホスト出版物のタイトル2017 39th Annual International Conference of the IEEE Engineering in Medicine and Biology Society
ホスト出版物のサブタイトルSmarter Technology for a Healthier World, EMBC 2017 - Proceedings
出版社Institute of Electrical and Electronics Engineers Inc.
ページ3814-3817
ページ数4
ISBN(電子版)9781509028092
DOI
出版ステータスPublished - 2017 9月 13
外部発表はい
イベント39th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBC 2017 - Jeju Island, Korea, Republic of
継続期間: 2017 7月 112017 7月 15

出版物シリーズ

名前Proceedings of the Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBS
ISSN(印刷版)1557-170X

Other

Other39th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBC 2017
国/地域Korea, Republic of
CityJeju Island
Period17/7/1117/7/15

ASJC Scopus subject areas

  • 信号処理
  • 生体医工学
  • コンピュータ ビジョンおよびパターン認識
  • 健康情報学

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