The CSP-Based New Features Plus Non-Convex Log Sparse Feature Selection for Motor Imagery EEG Classification

Abstract

The common spatial pattern (CSP) is a very effective feature extraction method in motor imagery based brain computer interface (BCI), but its performance depends on the selection of the optimal frequency band. Although a lot of research works have been proposed to improve CSP, most of these works have the problems of large computation costs and long feature extraction time. To this end, three new feature extraction methods based on CSP and a new feature selection method based on non-convex log regularization are proposed in this paper. Firstly, EEG signals are spatially filtered by CSP, and then three new feature extraction methods are proposed. We called them CSP-wavelet, CSP-WPD and CSP-FB, respectively. For CSP-Wavelet and CSP-WPD, the discrete wavelet transform (DWT) or wavelet packet decomposition (WPD) is used to decompose the spatially filtered signals, and then the energy and standard deviation of the wavelet coefficients are extracted as features. For CSP-FB, the spatially filtered signals are filtered into multiple bands by a filter bank (FB), and then the logarithm of variances of each band are extracted as features. Secondly, a sparse optimization method regularized with a non-convex log function is proposed for the feature selection, which we called LOG, and an optimization algorithm for LOG is given. Finally, ensemble learning is used for secondary feature selection and classification model construction. Combing feature extraction and feature selection methods, a total of three new EEG decoding methods are obtained, namely CSP-Wavelet+LOG, CSP-WPD+LOG, and CSP-FB+LOG. Four public motor imagery datasets are used to verify the performance of the proposed methods. Compared to existing methods, the proposed methods achieved the highest average classification accuracy of 88.86, 83.40, 81.53, and 80.83 in datasets 1–4, respectively. The feature extraction time of CSP-FB is the shortest. The experimental results show that the proposed methods can effectively improve the classification accuracy and reduce the feature extraction time. With comprehensive consideration of classification accuracy and feature extraction time, CSP-FB+LOG has the best performance and can be used for the real-time BCI system.

Evaluating the Motor Imagery Classification Performance of a Double-Layered Feature Selection on Two Different-Sized Datasets

“…Hence the authors hope to establish a robust decoder for real-time motor imagery training with feedbacks. In future investigations, comparison with other feature selection methods or combination with optimization methods [26,27,29] while minimizing computation cost should be carried out.…”

“…In the first method, because frequency information is obtained via a time-frequency analysis, the EEG signals of each channel must be decomposed, which involves numerous calculations and takes considerable computation time. The second method is challenging to implement and effortlessly leads to local solutions [29]. The drawback of the fourth method is the requirement of a lengthy training phase of models, which is insufficient for practical BCI.…”

Motor Imagery EEG Decoding Based on New Spatial-Frequency Feature and Hybrid Feature Selection Method

“…In order to verify the effectiveness of the proposed feature extraction method, the proposed method is compared with the other four CSP methods, which are the traditional CSP method [6,19], CSP-FB [11], SFBCSP [9], SBLFB [10], and CSP-LBP [7]. If there is no special instruction, the pair number of spatial filters for CSP and its improvement methods are set as follows: m � 3 for data set 1 and data set 3; m � 1 for data set 2; SVM is used for classification.…”

“…e comparison algorithms and their parameter settings are as follows: CSP: CSP feature extraction refers to literature [6,19]. CSP-FB: the parameter setting of the CSP-FB algorithm refers to literature [11]. F-score-h is used to select features.…”

Classification of EEG Using Adaptive SVM Classifier with CSP and Online Recursive Independent Component Analysis

“…Sparse Bayesian learning, which has been applied for feature selection in a variety of applications, has also lately attracted more interest. The decomposition of the EEG signal into several sub-bands and extraction of CSP characteristics [ 18 ]. The Bayesian learning technique is utilized to create sparse features, and the SVM classifier is then employed for classification.…”