Performance Evaluation of Machine Learning Models for EEG-Based Motor Imagery Classification Control

This project explores the integration of electroencephalography (EEG) with machine learning (ML) and Arduino microcontrollers to classify and execute motor commands based on brain signals. EEG signals are collected from users performing specific motor imagery tasks, such as imagining left- or right-hand movements. These signals are preprocessed to remove noise and segmented into feature sets that are then input into a machine learning model trained to differentiate motor intentions. The classified output from the ML model is subsequently transmitted to an Arduino, which controls physical hardware to demonstrate the intended movement, such as rotating a motor left or right. This interface has potential applications in assistive technology, enabling individuals with motor impairments to control devices through thought alone. The project demonstrates an innovative approach to brain-computer interfacing (BCI), highlighting the feasibility of real-time EEG signal processing for practical hardware applications.