Extended common spatial and temporal pattern (ECSTP): A semi-blind approach to extract features in ERP detection

作者：

Highlights：

• ECSP, ECTP and ECSTP can be used in applications that we have some prior knowledge about the two conditions to be classified.

• The performance of the proposed methods ECSP, ECTP, and ECSTP is evaluated on P300 speller data of BCI competitions II and III. In both data sets, our proposed methods significantly outperforms the conventional CSP and CTP methods.

• ECSTP reached average character detection accuracy of 98.5% on BCI competition II, which outperforms almost all the other state of the art methods.

• An advantage of our proposed methods over many of the other P300 speller classification methods is its significantly less training time (compared to approaches such as eSVM that have an extra channel selection step).

摘要

•ECSP, ECTP and ECSTP can be used in applications that we have some prior knowledge about the two conditions to be classified.•The performance of the proposed methods ECSP, ECTP, and ECSTP is evaluated on P300 speller data of BCI competitions II and III. In both data sets, our proposed methods significantly outperforms the conventional CSP and CTP methods.•ECSTP reached average character detection accuracy of 98.5% on BCI competition II, which outperforms almost all the other state of the art methods.•An advantage of our proposed methods over many of the other P300 speller classification methods is its significantly less training time (compared to approaches such as eSVM that have an extra channel selection step).

论文关键词：Feature extraction,Event-Related potential,P300 Speller,Common spatial pattern (CSP)

论文评审过程：Received 4 July 2018, Revised 14 May 2019, Accepted 30 May 2019, Available online 31 May 2019, Version of Record 15 June 2019.

论文官网地址：https://doi.org/10.1016/j.patcog.2019.05.039