The invention relates to the technical field of intelligent diagnosis systems for neurodegenerative diseases, and discloses a
system and equipment for identifying an early biomarker of Parkinson's
disease based on non-invasive electroencephalogram. The Parkinson's
disease early-stage
biomarker identification system comprises a multi-mode
signal acquisition module which is provided with a 64-lead dry
electrode electroencephalogram cap, an integrated
preamplifier and an ADC converter and is used for synchronously acquiring resting-state electroencephalogram and event-related potential, dynamically completing switching of the resting-state electroencephalogram sampling rate and transmitting acquired
original data to a preprocessing module. According to the method, revolutionary improvement is realized in three dimensions of acquisition, analysis and calculation through
system-level collaborative innovation, a nanocrystalline shielding dry
electrode is combined with a
dynamic impedance adjustment technology, so that the
signal-to-
noise ratio of a
home environment is increased to 28dB, and the
pathological correlation of key markers such as
frontal lobe-basal node loop gamma entropy is verified by
Granger causality and has a good application prospect. The cloud distributed model
server supports 2000 paths of concurrent analysis, and the real-time
bottleneck of home screening is thoroughly solved.