A Fusion Algorithm for EEG Signal Processing Based on Motor Imagery Brain-Computer Interface

• Published in: Wireless communications and mobile computing Vol. 2022; pp. 1 - 14
• Main Authors: Geng, Xiaozhong, Xue, Song, Yu, Ping, Li, Dezhi, Yue, Mengzhe, Zhang, Xi, Wang, Linen
• Format: Journal Article
• Language: English
• Published: Oxford Hindawi 24.03.2022; Hindawi Limited
• Subjects: Algorithms; Artificial neural networks; Brain research; Classification; Continuous wavelet transform; Datasets; Deep learning; Electroencephalography; Feature extraction; Human-computer interface; Imagery; Interfaces; Iterative methods; Nervous system; Neural networks; Neuromuscular diseases; Neurons; Power; Signal processing; Wavelet transforms
• ISSN: 1530-8669; 1530-8677
• DOI: 10.1155/2022/8935543

Electroencephalogram (EEG) signal processing is a very important module in the brain-computer interface system. As an important physiological feature of the human body, EEG signals are closely related to the functional state of the cerebral nervous system. However, the EEG signals collected on the scalp are generally weak and inevitably subject to various noise interferences. In order to remove artifacts from the EEG in brain computer interfaces (BCIs), a fusion algorithm for EEG signal preprocessing is proposed. The fusion method includes the following steps: firstly, the raw EEG signals are separated into a set of statistics independent components (ICs) by the improved FastICA algorithm. Then, each independent component is decomposed into a series of intrinsic mode functions (IMFs) by using the improved empirical mode decomposition method (EMD). Many IMFs with high-frequency noise are deleted. The rest of the IMFs are reconstructed. Furthermore, artifacts are further eliminated by iterative process of the improved FastICA algorithm, and then, the EEG signals are reconstructed again by inverse ICA. Finally, the cleaned EEG signal was obtained. The comparative experiment shows that the EMD-ICA fusion algorithm not only accurately eliminates the artifact components but also better retains the local characteristics of the raw EEG. Continuous wavelet transform was used to extract energy features of μ rhythm and β rhythmic to represent the characteristics of EEG signals under different motor imageries. These two features are normalized and used as the input data of the convolutional neural network (CNN) designed by the paper, and the two kinds of features are learned by CNN, and then, the two-classification problem of motor imagery EEG signals is completed. The experimental results show that the average classification accuracy and kappa value of the proposed method are higher than those of SVM and SAE for most subjects.