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.
Highlights
Electroencephalograms (EEG) are a method used to record electrical information from the cerebral cortex, reflecting part of brain activity
Where x represents the data that need to be mapped into higher dimensions, l represents the features of all of the samples, γ is an adjustable parameter that represents the complexity of the transformation, exp computes exponential functions based on natural numbers, and k x − l k2 represents the similarity between the data
4, typical time–fremaps and original signalssignals without featurefeature extraction are used input to be fed quency maps and original without extraction areas used as features input features to into the typical models to compare them with our proposed be fed into the typical Linear Discriminant Analysis (LDA) and Support Vector Machines (SVM) models to compare them with our proposed MTFmethod
Summary
Electroencephalograms (EEG) are a method used to record electrical information from the cerebral cortex, reflecting part of brain activity. In [21], the Common Spatial Patterns (CSP) and Linear Discriminant Analysis (LDA) machine learning algorithms were applied to EEG signals for feature extraction and classification, respectively. Many studies have used the CSP algorithm to extract significant EEG features in order to achieve good classification accuracy. Significant EEG signal features extracted by the CSP algorithm were able to improve the recognition accuracy. 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.
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