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Title: Automatically Identified EEG Signals of Movement Intention Based on CNN Network (End-To-End)
Authors: Shahini, N
Bahrami, Z
Sheykhivand, S
Marandi, S
Danishvar, M
Danishvar, S
Roosta, Y
Keywords: electroencephalogram;movement intention;brain-computer interface;convolutional neural network
Issue Date: 13-Oct-2022
Publisher: MDPI
Citation: Shahini, N. et al. (2022) 'Automatically Identified EEG Signals of Movement Intention Based on CNN Network (End-To-End)', Electronics (Switzerland), 11 (20), 3297, pp. 1 - 15. doi: 10.3390/electronics11203297.
Abstract: Copyright © 2022 by the authors. Movement-based brain–computer Interfaces (BCI) rely significantly on the automatic identification of movement intent. They also allow patients with motor disorders to communicate with external devices. The extraction and selection of discriminative characteristics, which often boosts computer complexity, is one of the issues with automatically discovered movement intentions. This research introduces a novel method for automatically categorizing two-class and three-class movement-intention situations utilizing EEG data. In the suggested technique, the raw EEG input is applied directly to a convolutional neural network (CNN) without feature extraction or selection. According to previous research, this is a complex approach. Ten convolutional layers are included in the suggested network design, followed by two fully connected layers. The suggested approach could be employed in BCI applications due to its high accuracy.
Description: Data Availability Statement: The data is private due to the lack of permission from the ethics committee.
Other Identifiers: ORCID iDs: Sobhan Sheykhivand; Morad Danishvar; Sebelan Danishvar
Appears in Collections:Dept of Computer Science Research Papers
Dept of Civil and Environmental Engineering Research Papers

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