Subject and class specific frequency bands selection for multiclass motor imagery classification

Abstract

AbstractEEG‐based discrimination among motor imagery states has been widely studied for brain‐computer interfaces (BCIs) due to the great potential for real‐life applications. However, in terms of designing a motor imagery‐based BCI system, a lot of research in the literature either uses a frequency band of interest selected manually based on the visual analysis of EEG data or is set to a general broad band, causing performance degradation in classification. In this article, we propose a novel method of selecting subject and class specific frequency bands based on the analysis of a channel‐frequency matrix, which we call a channel‐frequency map. We operate the classification process for each frequency band individually, i.e., spatial filtering, feature extraction, and classification, and determine a class label for an input EEG by considering the outputs from multiple classifiers together at the end. From our experiments on a public dataset of BCI Competition IV (2008) II‐a that includes four motor imagery tasks from nine subjects, the proposed algorithm outperformed the common spatial pattern (CSP) algorithm in a broad band and a filter bank CSP algorithm on average in terms of cross‐validation and session‐to‐session transfer rate. Furthermore, a considerable increase of classification accuracy has been achieved for certain subjects. We also would like to note that the proposed data‐driven frequency bands selection method is applicable to other kinds of single‐trial EEG classifications that are based on modulations of brain rhythms, by no means limited to motor imagery‐based BCI applications. © 2011 Wiley Periodicals, Inc. Int J Imaging Syst Technol, 21, 123–130, 2011