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The Auto-Correlation Function Aided Sparse Support Matrix Machine for EEG-Based Fatigue Detection
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DOI：10.1109/TCSII.2022.3211931.png)
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Abstract
En 中文
Due to support vector machine (SVM) methods generally vectorizing the input matrix and destroying the location dependence characteristic of EEG signals, the support matrix machine (SMM) methods have been presented to incorporate input signal with its location information into the input data matrix and to perform the classification algorithm. In this brief, we investigate an auto-correlation function based sparse support matrix machine (ACF-SSMM) algorithm to optimize and classify the EEG fatigue signals. The main contents include: 1) to input a matrix EEG signal with location information and compress the redundant features by using the sparse principle; 2) to extend the input matrix by adding data through an ACF transform, which takes into account information containing the previous/current instants, to accurately express the memory dynamics of the EEG signals; 3) to perform multiple similar classification algorithms on the EEG fatigue dataset (SEED-VIG dataset) to verify effectiveness of the presented ACF-SSMM algorithm in EEG-based fatigue detection. The experiments get better results, and show the effectiveness of the proposed algorithm.
Keywords:
Fatigue
Electroencephalography
Sparse matrices
Classification algorithms
Feature extraction
Fasteners
Support vector machines
Support matrix machine
electroencephalogram (EEG)
fatigue detection
auto-correlation function
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