CASIA OpenIR  > 复杂系统管理与控制国家重点实验室  > 先进机器人
NeuroCubeRehab: A pilot study for EEG classification in rehabilitation practice based on spiking neural networks
Yixiong Chen; Jin Hu; Nikola Kasabov; Zeng-Guang Hou; Long Cheng
2013
Conference Name20th International Conference on Neural Information Processing
Conference DateNov, 2013
Conference PlaceDaegu
CountrySouth Korea
AbstractOne 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.
Document Type会议论文
Identifierhttp://ir.ia.ac.cn/handle/173211/23146
Collection复杂系统管理与控制国家重点实验室_先进机器人
Recommended Citation
GB/T 7714
Yixiong Chen,Jin Hu,Nikola Kasabov,et al. NeuroCubeRehab: A pilot study for EEG classification in rehabilitation practice based on spiking neural networks[C],2013.
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