Abstract:
This project aims to develop an EEG-controlled robotic arm for individuals with motor
impairments, offering them increased autonomy and functionality in their daily lives. The
system utilizes a dataset of pre-recorded EEG signals associated with motor imagery and
leverages a Convolutional Neural Network (CNN) model to classify incoming EEG
signals, enabling users to control the robotic arm through their thoughts. The dataset
consists of EEG signals generated by the imagination of performing specific hand and arm
movements, allowing users to manipulate the robotic arm's motion and perform various
tasks simply by thinking about the movements. The CNN model will be trained and
evaluated using the collected EEG dataset, and subsequently deployed on a portable
Raspberry Pi 4B platform. This implementation ensures the EEG-controlled robotic arm is
lightweight, adaptable, and easily accessible to users. By allowing individuals with motor
impairments to control a robotic arm using their brain signals, this project holds
tremendous potential to enhance their quality of life, empowering them to perform tasks
independently and with greater ease.
