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A novel deep learning approach to predict subject arm movements from EEG-based signals

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Abstract

Around 3 million people worldwide have an arm amputation. These people face a lot of trouble in their everyday life whilst performing basic tasks. This paper proposes a novel deep learning-based approach for predicting arm movements using EEG-based signals. We plan to design and develop an active exoskeleton controlled by the same EEG-based signals to rehabilitate the amputees. The architecture design is intended to build an exoskeleton arm with at least 3 degrees of freedom that can perform complex movements and is sophisticated enough to substitute for a real arm. This prosthetic arm will be controlled using electroencephalogram (EEG) signals gathered by different devices/headsets and processed using deep learning models. The results show that our proposed approach gives excellent results.

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Data availability

The datasets analysed during the current study are available in the [BNCI Horizon 2020] repository, [http://bnci-horizon-2020.eu/database/data-sets].

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Acknowledgements

We hereby acknowledge the support of the Computer Science Engineering Department, Thapar Institute of Engineering Technology, Patiala, Punjab, for providing the facility.

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Authors and Affiliations

  1. Department of Computer Science and Engineering, Thapar Institute of Engineering Technology, Patiala, Punjab, India

    Sachin Kansal, Dhruv Garg, Aditya Upadhyay, Snehil Mittal & Guneet Singh Talwar

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  1. Sachin Kansal
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  2. Dhruv Garg
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  3. Aditya Upadhyay
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  4. Snehil Mittal
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  5. Guneet Singh Talwar
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Correspondence to Sachin Kansal.

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Kansal, S., Garg, D., Upadhyay, A. et al. A novel deep learning approach to predict subject arm movements from EEG-based signals. Neural Comput & Applic 35, 11669–11679 (2023). https://doi.org/10.1007/s00521-023-08310-9

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