# Multi-Time and Multi-Band CSP Motor Imagery EEG Feature Classification Algorithm
###### Abstract
The frequency features of MI-EEG are mainly manifested in \(\upmu\) rhythm (8-13 Hz) and \(\upbeta\) rhythm (13-30 Hz); that is, when performing unilateral limb MI, the signal energy in the corresponding frequency band of contralateral brain electrodes decreases [22; 23; 24]. Extracting effective MI features plays a vital role in the further identification of EEG signals. Many studies have been conducted to find the event-related synchronization (ERD) characteristics of contralateral brain electrodes on the frequency domain when performing unilateral limb MI [25; 26; 27]. Short-time Fourier transform (STFT) was explored to consider time and frequency features simultaneously, which converted the original signal into time-frequency domains [28; 29]. The Common Space Pattern (CSP) algorithm has been widely studied due to its advantages in the extraction of significant MI-EEG features [30; 31]. Many studies have used the CSP algorithm to extract significant EEG features in order to achieve good classification accuracy. In [32; 33], the CSP algorithm was combined with the LDA classifier for MI-EEG decoding. In [34; 35], the performance of five different features based on the CSP + SVM model was analyzed. Significant EEG signal features extracted by the CSP algorithm were able to improve the recognition accuracy. Many studies have improved CSP in order to obtain better EEG characteristics. Several studies have used different frequency bands to improve CSP, including sub-band CSP (SBCSP) [36; 37], Filter Bank CSP (Filter Bank CSP, FBCSP [25; 38], and Discriminative FBCSP (DFBCSP) [39]. The single frequency band and single time window method lose detail features and thus cannot fully reflect the complete features of MI-EEG. Despite some efforts to fuse different frequency characteristics, efficient and detailed time features have been ignored. The influence of the interception of different fixed time windows on the CSP algorithm has been explored [40]. However, the effective MI starting time and the effective time window length vary from person to person. Additionally, the generalization of a single fixed-length window method is challenging. Only a few studies have investigated an effective MI time window. In [41], an automatic selection of the best time window was proposed. In [42], the features of three time-windows were extracted based on a multi-band CSP algorithm. For the simultaneous extraction of the effective time window and frequency features, this paper proposes a CSP algorithm based on multi-time window and multi-frequency band (MTF-CSP). Multiple Support Vector Machines (SVM) are employed to classify the multi-frequency band features extracted from multiple time windows. The ED and AS algorithm are used to make the final output decision. The proposed MTF-CSP methods were compared to state-of-the-art methods in terms of different extracted features, CSP-based methods, and the final decision algorithms and performed better than previous methods. The classification accuracy of the two decision outputs obtained from two decision algorithms is compared, and the influence of different window lengths and numbers on the classification accuracy is analyzed. The major contributions of the paper are highlighted as follows:  
* The MTF-CSP features extracted by us achieved better classification accuracy in the classification process by comparing it with the original signal and time-frequency features. * The strategy for intercepting multiple sliding window EEG data for analysis demonstrated better performance than direct full-window EEG data analysis. * We compared our MTF-CSP method with traditional CSP-based models, and the results demonstrated that the multi-band and multi-time strategy could obviously improve the recognition performance of the CSP-based models.