bilibili啥都会一点的研究生:https://space.bilibili.com/46880349
The author's officially unofficial Pytorch EvolutionaryGAN implementation.
This repo contains code for classification of alzheimer's disease from EEG signal.
Alzheimer’s Disease (AD) is the most common neurodegenerative disease. It is typically late onset and can develop substantially before diagnosable symptoms appear. Electroencephalogram (EEG) could potentially serve as a noninvasive diagnostic tool for AD. Machine learning can be helpful in making inferences about changes in frequency bands in EEG data and how these changes relate to neural function. The EEG data was sourced from 2014 paper titled Alzheimer’s disease patients classification through EEG signals processing by Fiscon et al. There were patients with AD, mild cognitive impairment (MCI), and healthy controls. The data was already preprocessed using a fast fourier transform (FFT) to take the data from the time domain to the frequency domain. There were differing levels of effectiveness in terms of classification but generally, Fisher’s discriminant analysis (FDA), relevance vector machine, and random forest approaches were most successful. Due to inconsistent feature importances in different models, conclusions about important frequency bands for classification were not able to be made at this time. Similarly, different frequencies were not able to be localized to different regions of the brain. Further research is necessary to develop more interpretable models for classification.
EEG/ERP portal enables community researchers to store, update, download and search data and metadata from EEG/ERP experiments. The system is developed as a standalone product; the database access is available through a web interface.
Project aim is to design a non-invasive diagnosis tool for Alzheimer's disease using software implementation in MATLAB.
A project on improving Neural Networks performance by using Genetic Algorithms.
