AI-BASED BIOPHYSICAL ANALYSIS OF BRAIN SIGNALS FOR EARLY NEUROLOGICAL DISORDER DETECTION

Abstract

Neurological disorders represent a growing global health burden due to their high prevalence, complex pathophysiology, and long-term impact on quality of life. Early detection of such conditions remains a critical challenge, as clinical symptoms often appear only after significant neural damage has occurred. In this context, the integration of biophysical signal analysis with artificial intelligence (AI) offers promising opportunities for improving early diagnostic accuracy. Brain signals such as electroencephalography (EEG) and other neurophysiological recordings contain rich biophysical information reflecting neuronal electrical activity, synaptic interactions, and network dynamics. However, the complexity and high dimensionality of these signals limit the effectiveness of traditional analytical approaches.