Channel Optimized Visual Imagery based Robotic Arm Control under the Online Environment

TL;DR

This paper presents a novel deep learning approach to decode visual imagery EEG data for controlling a BCI-based robotic arm, demonstrating promising offline and online classification performance with minimal channels.

Contribution

It introduces a deep learning architecture that effectively decodes visual imagery EEG signals using only two channels, enhancing BCI robotic arm control.

Findings

Offline classification accuracy reached 0.661 with two channels.

Online success rate achieved 0.78 using two channels (AF3-Oz).

Proves feasibility of controlling robotic arm via visual imagery EEG signals.

Abstract

An electroencephalogram is an effective approach that provides a bidirectional pathway between the user and computer in a non-invasive way. In this study, we adopted the visual imagery data for controlling the BCI-based robotic arm. Visual imagery increases the power of the alpha frequency range of the visual cortex over time as the user performs the task. We proposed a deep learning architecture to decode the visual imagery data using only two channels and also we investigated the combination of two EEG channels that has significant classification performance. When using the proposed method, the highest classification performance using two channels in the offline experiment was 0.661. Also, the highest success rate in the online experiment using two channels (AF3-Oz) was 0.78. Our results provide the possibility of controlling the BCI-based robotic arm using visual imagery data.