Extended common spatial and temporal pattern (ECSTP): A semi-blind approach to extract features in ERP detection. (November 2019) - Jalilpour Monesi, Mohammad; Hajipour Sardouie, Sepideh
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). Abstract: Common spatial pattern (CSP) analysis and its extensions have been widely used as feature extraction approaches in the brain-computer interfaces (BCIs). However, most of the CSP-based approaches do not use any prior knowledge that might be available about the two conditions (classes) to be classified. Therefore, their applications are limited to datasets that contain enough variance information about the two conditions. For example, in some event-related potential (ERP) detection applications, such as P300 speller, the information is in the time domain but not in the variance of spatial components. To address this problem, first, we present a novel feature extraction method termed extended common spatial pattern (ECSP) analysis, which uses prior knowledge available from data to produce a broader range of features than that of conventional CSP analysis. Then, similarly, we introduce the extended common temporal pattern (ECTP) analysis. Finally, to exploit both spatial and temporal information, we propose extended common spatial and temporal pattern (ECSTP) analysis. We have used BCI competition III, dataset II as our main dataset to evaluate our proposed methods. In addition, we used two other datasets, namely BCI competition II, dataset IIb and BCI competition IV, dataset IIb, to further evaluate the performance of the proposed methods. In All the datasets, the proposed methods significantly outperform the conventional CSP, CTP, and CSTP methods. More specifically, ECSTP has the best performance among the proposed methods. Moreover, classification results show that the proposed methods are competitive with other state of the art methods applied to these datasets.