Motor Imagery Classification of Single-Arm Tasks Using Convolutional Neural Network based on Feature Refining

02/04/2020
by   Byeong-Hoo Lee, et al.
Korea University
0

Brain-computer interface (BCI) decodes brain signals to understand user intention and status. Because of its simple and safe data acquisition process, electroencephalogram (EEG) is commonly used in non-invasive BCI. One of EEG paradigms, motor imagery (MI) is commonly used for recovery or rehabilitation of motor functions due to its signal origin. However, the EEG signals are an oscillatory and non-stationary signal that makes it difficult to collect and classify MI accurately. In this study, we proposed a band-power feature refining convolutional neural network (BFR-CNN) which is composed of two convolution blocks to achieve high classification accuracy. We collected EEG signals to create MI dataset contained the movement imagination of a single-arm. The proposed model outperforms conventional approaches in 4-class MI tasks classification. Hence, we demonstrate that the decoding of user intention is possible by using only EEG signals with robust performance using BFR-CNN.

READ FULL TEXT

page 2

page 3

12/10/2022

A Hybrid Brain-Computer Interface Using Motor Imagery and SSVEP Based on Convolutional Neural Network

The key to electroencephalography (EEG)-based brain-computer interface (...
01/24/2021

EEG-Inception: An Accurate and Robust End-to-End Neural Network for EEG-based Motor Imagery Classification

Classification of EEG-based motor imagery (MI) is a crucial non-invasive...
07/29/2023

Feature Reweighting for EEG-based Motor Imagery Classification

Classification of motor imagery (MI) using non-invasive electroencephalo...
01/18/2021

Motor-Imagery-Based Brain Computer Interface using Signal Derivation and Aggregation Functions

Brain Computer Interface technologies are popular methods of communicati...

Please sign up or login with your details

Forgot password? Click here to reset