Electroencephalogram grading and prognosis FPGA decoding system based on neural manifold

Abstract

The invention provides an electroencephalogram grading and prognosis FPGA decoding system based on neural manifold. The system comprises an electroencephalogram acquisition device, an FPGA decoding calculation device, a liquid crystal touchable display screen and a stimulation device. The FPGA decoding calculation device is composed of a manifold extraction subsystem, a cognitive optimization parameter calculation subsystem, an electroencephalogram grading processing subsystem and an electroencephalogram prognosis evaluation processing subsystem which are used for extracting manifolds, calculating stimulation parameters needed for optimizing cognitive functions and conducting grading and prognosis evaluation on electroencephalogram signals respectively. Electroencephalogram grading is carried out, prognosis evaluation is carried out through the change trend of the difference value between the actual neural manifold and the target manifold, the two manifolds, the stimulation parameters and the grading prognosis result are transmitted to a display screen through wires, and the optimal stimulation parameters are transmitted to a stimulation device through a wire for control. According to the system, electroencephalogram automatic grading, prognosis evaluation and brain cognitive function optimization can be achieved.

Description

technical field [0001] The invention relates to biomedical engineering technology, in particular to an FPGA decoding system for EEG classification and prognosis based on neural manifolds. Background technique [0002] The brain is the most complex and active organ of the human body. Various neurological diseases of the brain may cause damage to brain tissue and cause functional impairment. The choice of treatment methods often requires rapid and accurate diagnosis of the current degree of brain damage. The grading of brain damage is convenient for evaluating the injury. Accurate and objective evaluation and prediction of prognosis for acute severe brain damage are the key to the treatment work. The key is to effectively improve the survival rate of patients. Clinically, the detection of brain damage in patients basically relies on manual diagnosis by doctors based on experience. The diagnosis results are often subjective, lacking objective and quantitative indicators, and t...

AI Technical Summary

Problems solved by technology

However, due to the high-dimensional and complex characteristics of EEG, the neural manifold of brain activity is not a simple linear plane, and the ordinary linear dimensionality reduction method (PCA) cannot accurately find the neural manifold of the brain

The traditional nonlinear dimensionality reduction method (t-SNE) can extract the nonlinear manifold, but because its mapping function is a nonlinear mapping, there is no explicit expression form, and the neural manifold cannot be reconstructed back to the EEG data, so the neural flow cannot be guaranteed. shape reliability

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