SVM classification study of code-modulated visual evoked potentials

Daiki Aminaka, Shoji Makino, Tomasz M. Rutkowski*

*この研究の対応する著者

研究成果: Conference contribution

6 被引用数 (Scopus)

抄録

We present a study of a support vector machine (SVM) application to brain-computer interface (BCI) paradigm. Four SVM kernel functions are evaluated in order to maximize classification accuracy of a four classes-based BCI paradigm utilizing a code-modulated visual evoked potential (cVEP) response within the captured EEG signals. Our previously published reports applied only the linear SVM, which already outperformed a more classical technique of a canonical correlation analysis (CCA). In the current study we additionally test and compare classification accuracies of polynomial, radial basis and sigmoid kernels, together with the classical linear (non-kernel-based) SVMs in application to the cVEP BCI.

本文言語English
ホスト出版物のタイトル2015 Asia-Pacific Signal and Information Processing Association Annual Summit and Conference, APSIPA ASC 2015
出版社Institute of Electrical and Electronics Engineers Inc.
ページ1065-1070
ページ数6
ISBN(電子版)9789881476807
DOI
出版ステータスPublished - 2016 2 19
外部発表はい
イベント2015 Asia-Pacific Signal and Information Processing Association Annual Summit and Conference, APSIPA ASC 2015 - Hong Kong, Hong Kong
継続期間: 2015 12 162015 12 19

出版物シリーズ

名前2015 Asia-Pacific Signal and Information Processing Association Annual Summit and Conference, APSIPA ASC 2015

Other

Other2015 Asia-Pacific Signal and Information Processing Association Annual Summit and Conference, APSIPA ASC 2015
国/地域Hong Kong
CityHong Kong
Period15/12/1615/12/19

ASJC Scopus subject areas

  • 人工知能
  • モデリングとシミュレーション
  • 信号処理

フィンガープリント

「SVM classification study of code-modulated visual evoked potentials」の研究トピックを掘り下げます。これらがまとまってユニークなフィンガープリントを構成します。

引用スタイル