Decoding Visual Motions from EEG Using Attention-Based RNN

Decoding Visual Motions from EEG Using Attention-Based RNN

The main objective of this paper is to use deep neural networks to decode the electroencephalography (EEG) signals evoked when individuals perceive four types of motion stimuli (contraction, expansion, rotation, and translation). Methods for single-trial and multi-trial EEG classification are both i...

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Bibliographic Details
Main Authors: Dongxu Yang, Yadong Liu, Zongtan Zhou, Yang Yu, Xinbin Liang
Format: Article
Language:English
Published: MDPI AG 2020-08-01
Series:Applied Sciences
Subjects:
electroencephalography
attention mechanisms
recurrent neural networks
data augmentation
brain–computer interface
visual motion perception
Online Access:https://www.mdpi.com/2076-3417/10/16/5662

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