Stacked recurrent neural network for decoding of reaching movement using local field potentials and single-unit spikes
(2017) 2017 8th International IEEE/EMBS Conference on Neural Engineering (NER) — Location: Shanghai, China (25.May.2017)
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- Authors
- Abstract
- Decoding intended movement trajectory from neural activity is crucial for developing neuroprosthetic devices. In this study, we propose a processing framework to combine different information from two types of neural activities: action potentials (spikes) and local field potentials (LFPs). For this purpose, we proposed a stacked generalization approach based on recurrent neural network to enhance decoding accuracy of movement kinematics. We examined decoding performance of the proposed stacked recurrent neural network (SRNN) on decoding of reaching movement using intracortical datasets. The results show that the stacked generalization approach can enhance the decoding performance and can be used as an information fusion tool for multi-modal neuroprosthetic devices.
- Affiliations
Iran University of Science and Technology, Tehran, Iran Department of Biomedical Engineering, School of Electrical Engineering
Citations
Fathi Arateh, Y., & Erfanian, A. (2017). Stacked recurrent neural network for decoding of reaching movement using local field potentials and single-unit spikes. 2017 8th International IEEE/EMBS Conference on Neural Engineering (NER). Published. 2017 8th International IEEE/EMBS Conference on Neural Engineering (NER), Shanghai, China. https://doi.org/10.1109/ner.2017.8008440