Journal Article

Automatically Identified EEG Signals of Movement Intention Based on CNN Network (End-To-End)

Bahrami, Zeinab, Shahini, Nahal, Sheykhivand, Sobhan, Roosta, Yousef, Danishvar, Sebelan, Danishvar, Morad, Marandi, Saba

2022

Overview

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.

• Publisher: MDPI AG
• Subject: Accuracy / Algorithms / Artificial neural networks / Brain research / Classification / Complexity / Electroencephalography / Experiments / Feature extraction / Human locomotion / Human-computer interface / Methods / Network design / Neural networks / Prostheses / Researchers / Signal processing / Support vector machines