REVOLUTIONARY ADVANCEMENTS IN BRAIN-COMPUTER INTERFACE TECHNOLOGIES FOR NEUROLOGICAL REHABILITATION: INTEGRATING MACHINE LEARNING, NEUROPLASTICITY, AND PERSONALIZED THERAPEUTIC APPLICATIONS

Abstract

BCI technology enables direct brain connection to external systems thus creating an innovation for neurorehabilitation. This research studied machine learning and neuroplasticity as well as therapy approaches to enhance BCI in motor and cognitive rehabilitation. The procedure consisted of three fundamental phases which included EEG data acquisition followed by motor image classifier development and neurofeedback implementation. The sequential actions enabled customized therapeutic delivery. The convolutional neural network (CNN) achieved outstanding results with more than 87% correctness on testing and validation data. All treatment strategies focused on supporting neuroplasticity and specifically designed based on each participant's brain activity patterns. The participants underwent motor function tests at both intervention start and finish which demonstrated a 20% improvement in their functional scores. The analysis through PSD of power spectral density revealed heightened alpha and beta frequency bands which imply enhanced cortical recovery together with remodeling processes. An adaptive BCI system based on machine learning algorithms with principles from neuroplasticity research has the potential to establish personalized rehabilitation programs which achieve better outcomes. These research results demonstrate that patient-tailored rehabilitation treatments and real time monitoring provide the best possible positive outcomes for therapy recovery. Upcoming research needs to focus primarily on both lasting effectiveness and combination BCI systems alongside procedures that extend device accessibility for broader clinical implementations.