Decoding Asynchronous Reaching in Electroencephalography Using Stacked Autoencoders

Decoding Asynchronous Reaching in Electroencephalography Using Stacked Autoencoders

Electroencephalography (EEG)-based brain-computer interfaces (BCIs) that decode cortical activity in reaching and grasping movements can enable natural upper limb motor control. In this paper, we studied the performance of stacked autoencoders in decoding asynchronous reaching movements in the domin...

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Bibliographic Details
Main Authors: Dingyi Pei, Martin Burns, Rajarathnam Chandramouli, Ramana Vinjamuri
Format: Article
Language:English
Published: IEEE 2018-01-01
Series:IEEE Access
Subjects:
Electroencephalography
arm reaching movement
stacked autoencoders
machine learning
deep learning
classification
Online Access:https://ieeexplore.ieee.org/document/8466111/

Internet

https://ieeexplore.ieee.org/document/8466111/

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