A Hybrid EEG-EMG Framework for Humanoid Control using Deep Learning Transformers
Issued Date
2024-01-01
Resource Type
ISSN
29943566
eISSN
29943574
Scopus ID
2-s2.0-105001508423
Journal Title
IEEE International Conference on Robotics and Biomimetics, ROBIO
Issue
2024
Start Page
899
End Page
904
Rights Holder(s)
SCOPUS
Bibliographic Citation
IEEE International Conference on Robotics and Biomimetics, ROBIO No.2024 (2024) , 899-904
Suggested Citation
Olikkal P., Pillai B.M., Suthakorn J., Ali H., Vinjamuri R. A Hybrid EEG-EMG Framework for Humanoid Control using Deep Learning Transformers. IEEE International Conference on Robotics and Biomimetics, ROBIO No.2024 (2024) , 899-904. 904. doi:10.1109/ROBIO64047.2024.10907308 Retrieved from: https://repository.li.mahidol.ac.th/handle/20.500.14594/109342
Title
A Hybrid EEG-EMG Framework for Humanoid Control using Deep Learning Transformers
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Author's Affiliation
Corresponding Author(s)
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Abstract
Brain-computer interfaces (BCIs) offer promising solutions for upper limb rehabilitation. Despite advancements in deep learning, traditional models for motor rehabilitation using electroencephalography (EEG) or electromyography (EMG) to control assistive devices require enhancement. This study aims to enhance motor control capabilities by integrating EEG and EMG signals using a Transformer-based deep learning model. Ten able-bodies subjects performed center-out tasks on a low-cost upper limb rehabilitation table, capturing 2D kinematic data, EEG, and EMG signals simultaneously. The tasks varied in complexity across four levels. Preprocessed EEG and EMG signals were fused and given as input to the proposed model, which was evaluated using three performance metrics. Results showed that the EEG-EMG combined model achieved 87.27% accuracy across all the four levels. Furthermore, the model's output successfully controlled a humanoid robot to replicate similar movements. These findings highlight the efficacy of combined EEG-EMG data in improving accuracy and performance in BCI applications, advancing assistive technologies and neurorehabilitation interventions.