Feature Extraction and Simulation of EEG Signals During Exercise-Induced Fatigue

Abstract

Accurate extraction of EEG signal characteristics during exercise fatigue can provide a scientific basis for sports fatigue detection and exercise fatigue injury treatment. In this paper, based on multivariate empirical mode decomposition (MEMD) and Hilbert-Huang (HHT) algorithm, feature extraction of EEG signals during exercise fatigue is performed. MEMD extends standard experience mode to multi-channel signal processing and solves traditional algorithms. It is not suitable for self-adaptability, modal aliasing, and scale alignment. It is suitable for analyzing multi-time sequence; multi-channel and multi-scale EEG signal decomposition. After the original EEG signal passes through the MEMD, the energy mean, median and standard deviation of the EEG bands in different levels are calculated and used to form the feature set. Then the support vector machine (SVM) classifier is used to classify the extract the extracted features. The simulation results show that the proposed method can effectively extract the features of EEG signals during exercise fatigue.

Keywords

• Exercise fatigue
• EEG signal
• multivariate empirical mode decomposition
• Hilbert-Huang transform