Abstract
One of the most important issues among active rehabilitation technique is how to extract the voluntary intention of patient through bio-signals, especially EEG signal. This pilot study investigates the feasibility of utilizing a 3D spiking neural networks-based architecture named NeuCube for EEG data classification in the rehabilitation practice. In this paper, the architecture of the NeuCube is designed and a Functional Electrical Stimulation (FES) rehabilitation scenario is introduced which requires accurate classification of EEG signal to achieve active FES control. Three classes of EEG signals corresponding to three imaginary wrist motions are collected and classified. The NeuCube architecture provides promising classification results, which demonstrates our proposed method is capable of extracting the voluntary intention in the rehabilitation practice.
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Chen, Y., Hu, J., Kasabov, N., Hou, Z., Cheng, L. (2013). NeuCubeRehab: A Pilot Study for EEG Classification in Rehabilitation Practice Based on Spiking Neural Networks. In: Lee, M., Hirose, A., Hou, ZG., Kil, R.M. (eds) Neural Information Processing. ICONIP 2013. Lecture Notes in Computer Science, vol 8228. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-42051-1_10
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DOI: https://doi.org/10.1007/978-3-642-42051-1_10
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-42050-4
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