Multi-Time and Multi-Band CSP Motor Imagery EEG Feature Classification Algorithm

Abstract

The effective decoding of motor imagination EEG signals depends on significant temporal, spatial, and frequency features. For example, the motor imagination of the single limbs is embodied in the μ (8–13 Hz) rhythm and β (13–30 Hz) rhythm in frequency features. However, the significant temporal features are not necessarily manifested in the whole motor imagination process. This paper proposes a Multi-Time and Frequency band Common Space Pattern (MTF-CSP)-based feature extraction and EEG decoding method. The MTF-CSP learns effective motor imagination features from a weak Electroencephalogram (EEG), extracts the most effective time and frequency features, and identifies the motor imagination patterns. Specifically, multiple sliding window signals are cropped from the original signals. The multi-frequency band Common Space Pattern (CSP) features extracted from each sliding window signal are fed into multiple Support Vector Machine (SVM) classifiers with the same parameters. The Effective Duration (ED) algorithm and the Average Score (AS) algorithm are proposed to identify the recognition results of multiple time windows. The proposed method is trained and evaluated on the EEG data of nine subjects in the 2008 BCI-2a competition dataset, including a train dataset and a test dataset collected in other sessions. As a result, the average cross-session recognition accuracy of 78.7% was obtained on nine subjects, with a sliding window length of 1 s, a step length of 0.4 s, and the six windows. Experimental results showed the proposed MTF-CSP outperforming the compared machine learning and CSP-based methods using the original signals or other features such as time-frequency picture features in terms of accuracy. Further, it is shown that the performance of the AS algorithm is significantly better than that of the Max Voting algorithm adopted in other studies.

Motor Imagery EEG Classification Based on a Weighted Multi-Branch Structure Suitable for Multisubject Data

“…First, EEG signals must be preprocessed, such as removing artifacts [7] and bandpass filtering [8]. Second, as a crucial step in the classification of EEG signals, handcrafted feature extraction is used to obtain concise information and reduce data dimensionality, such as power spectral density (PSD) [9], entropy feature sets [10], autoregressive (AR) models [11], common spatial pattern (CSP) [12] and its variants including filter band CSP (FBCSP) [13], and multi-time and multi-band CSP [14]. Finally, these features are fed into classifiers such as support vector machine (SVM) [15], linear discriminant analysis (LDA) [16], k-nearest neighbours (KNN) [17], and random forest [18].…”

A novel scheme based on information theory and transfer learning for multi classes motor imagery decoding

“…To improve the SNR rate and better distinguish between task-related classes in MI, spatial filtering is commonly used in the preprocessing stage. Common Spatial Pattern (CSP) Algorithm is one of the most famous and widely used spatial filtering algorithms [9,10]. In recent years, many methods based on CSP and extended CSP algorithms have been developed to improve the capacity of extracting useful information from the EEG signal, along with various classifiers for decoding the MI signal [11][12][13][14][15][16][17].…”