Electroencephalogram‐Driven Recognition of Parkinson's Disease Through a Mycelium‐Inspired Memristive Reservoir Computing Circuit

Recognition of Parkinson's disease (PD) from neurophysiological signals remains a key challenge for early diagnosis and intervention. For real‐time monitoring, it requires advanced signal processing that operates directly on wearable or portable devices, thereby reducing latency, preserving privacy, and supporting continuous assessment. This study presents a bio‐inspired computational framework that combines electroencephalogram (EEG)‐derived stimulation with a mycelium‐inspired memristive reservoir circuit for analog pattern recognition. Resting‐state EEG recordings from individuals with PD and healthy controls undergo artifact removal and spectral decomposition. Dominant frequency components are transformed into discrete voltage pulse sequences that emulate cortical activity patterns and drive a fixed mycelial lattice. Electrical connectivity and branching density within the mycelium‐inspired network determine nonlinear signal propagation, producing frequency‐dependent activation dynamics. The memristive reservoir functions as a dynamic medium that implicitly encodes temporal and spectral information through state transitions in its conductive pathways. Voltage integration across selected nodes enables analog separability between Parkinsonian and control conditions without explicit digital processing or trained classifiers. The proposed system is presented as a proof‐of‐concept hybrid neuromorphic platform that bridges EEG dynamics, living‐material architectures, and memristive computation, highlighting its potential for analog bio‐inspired recognition of neurodegenerative disorders.