Feature Extraction and Classification of Motor Imagery EEG Signals in Motor Imagery for Sustainable Brain–Computer Interfaces

Yuyi Lu, Wenbo Wang (), Baosheng Lian and Chencheng He
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Yuyi Lu: College of Science, Wuhan University of Science and Technology, Wuhan 430081, China
Wenbo Wang: College of Science, Wuhan University of Science and Technology, Wuhan 430081, China
Baosheng Lian: College of Science, Wuhan University of Science and Technology, Wuhan 430081, China
Chencheng He: College of Science, Wuhan University of Science and Technology, Wuhan 430081, China

Sustainability, 2024, vol. 16, issue 15, 1-24

Abstract: Motor imagery brain–computer interface (MI-BCI) systems hold the potential to restore motor function and offer the opportunity for sustainable autonomous living for individuals with a range of motor and sensory impairments. The feature extraction and classification of motor imagery EEG signals related to motor imagery brain–computer interface systems has become a research hotspot. To address the challenges of difficulty in feature extraction and low recognition rates of motor imagery EEG signals caused by individual variations in EEG signals, a classification algorithm for EEG signals based on multi-feature fusion and the SVM-AdaBoost algorithm was proposed to improve the recognition accuracy of motor imagery EEG signals. Initially, the electroencephalography (EEG) signals are preprocessed using Finite Impulse Response (FIR) filters, and a multi-wavelet framework is constructed based on the Morlet wavelet and the Haar wavelet. Subsequently, the preprocessed signals undergo multi-wavelet decomposition to extract energy features, Common Spatial Patterns (CSP) features, Autoregressive (AR) features, and Power Spectral Density (PSD) features. The extracted features are then fused, and the fused feature vector is normalized. Following that, classification is implemented within the SVM-AdaBoost algorithm. To enhance the adaptability of SVM-AdaBoost, the Grid Search method is employed to optimize the penalty parameter and kernel function parameter of the SVM. Concurrently, the Whale Optimization Algorithm is utilized to optimize the learning rate and number of weak learners within the AdaBoost ensemble, thereby refining the overall performance. In addition, the classification performance of the algorithm is validated using a brain-computer interface (BCI) dataset. In this study, it was found that the classification accuracy reached 95.37%. Via the analysis of motor imagery electroencephalography (EEG) signals, the activation patterns in different regions of the brain can be detected and identified, enabling the inference of user intentions and facilitating communication and control between the human brain and external devices.

Keywords: sustainable living; motor imagery EEG signal; multi-wavelet decomposition; feature fusion; SVM-AdaBoost algorithm (search for similar items in EconPapers)
JEL-codes: O13 Q Q0 Q2 Q3 Q5 Q56 (search for similar items in EconPapers)
Date: 2024
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