Empowering Brain-Computer Interface (BCI): Al-Based Interpretation of Motor Imagery in EEG Signals

Abstract

Brain-Computer Interface(BCI) technology facilitates direct interaction between individuals and computer systems by capturing brain activities, specifically through electro en cephalogram(EEG),without relying onphys-ical movement.Our research delves in to there alm of’Motor Imagery,’a BCI modality where users simulate movements mentally,by passing the need for physical execution.This methodology harnesses brain signals evoked by imagined actions to command external devices.Particularly beneficial for individual safflicted by conditions like cerebral palsy,stroke, amyotrophicla teralscleros is,orspinalc ordin juries,BCI saimto restore impaired neural path ways,there by reinstatin glostmotor functions.More- over,leveraging artificial intelligence,not ably via Artificial intelligence model enabled data collection,extraction,and signal classification,opens avenues for innovative applications suc has’Thought-Controlled Driving’ or ’Mind-Powered Device Operation,’seamlessly translating classification outcomes in to actionable commands,Additionally,web elieve these tech- nologies canbefurtherenhancedbyincorporatingtheresultsandperfor- mance oftherandomforestmodelusedinourprototypes,achievingan accuracy of82.0%,arecallrateof81.8%,andanF1scoreof81.4%.