EEG-based recognition of hand movement and its parameter

• Published in: Journal of neural engineering Vol. 22; no. 2; pp. 26006 - 26025
• Main Authors: Yan, Yuxuan, Li, Jianguang, Yin, Mingyue
• Format: Journal Article
• Language: English
• Published: England IOP Publishing 06.03.2025
• Subjects: Adult; Brain-Computer Interfaces; deep learning; EEG signals; Electroencephalography - methods; Female; Hand - physiology; hand movement recognition; Humans; kinematic information; Male; motor execution classification; Movement - physiology; Neural Networks, Computer; Young Adult; deep learning; motor execution classification; EEG signals; hand movement recognition; kinematic information
• ISSN: 1741-2560; 1741-2552; 1741-2552
• DOI: 10.1088/1741-2552/adba8a

Objecitve . Brain–computer interface is a cutting-edge technology that enables interaction with external devices by decoding human intentions, and is highly valuable in the fields of medical rehabilitation and human-robot collaboration. The technique of decoding motor intent for motor execution (ME) based on electroencephalographic (EEG) signals is in the feasibility study stage by now. There are still insufficient studies on the accuracy of ME EEG signal recognition in between-subjects classification to reach the level of realistic applications. This paper aims to investigate EEG signal-based hand movement recognition by analyzing low-frequency time-domain information. Approach . Experiments with four types of hand movements, two force parameter (picking up and pushing) tasks, and a four-target directional displacement task were designed and executed, and the EEG data from thirteen healthy volunteers was collected. Sliding window approach is used to expand the dataset in order to address the issue of EEG signal overfitting. Furtherly, Convolutional Neural Network (CNN)-Bidirectional Long Short-Term Memory Network (BiLSTM) model, an end-to-end serial combination of a BiLSTM and (CNN) is constructed to classify and recognize the hand movement based on the raw EEG data. Main results . According to the experimental results, the model is able to categorize four types of hand movements, picking up movements, pushing movements, and four target direction displacement movements with an accuracy of 99.14% ± 0.49%, 99.29% ± 0.11%, 99.23% ± 0.60%, and 98.11% ± 0.23%, respectively. Significance . Furthermore, comparative tests conducted with alternative deep learning models (LSTM, CNN, EEGNet, CNN-LSTM) demonstrates that the CNN-BiLSTM model is with practicable accuracy in terms of EEG-based hand movement recognition and its parameter decoding.