Quote from
jogugil on 08.09.2024, 23:08
It's a very interesting field that can help a lot of people. On my GitHub, I have a repository (MyRC) where I conduct a study on EEG signal processing using RC-ESN. I provide an API or framework for both the preprocessing of these signals and their subsequent processing with the RC model. Specifically, it focuses on studying the aging process and identifying distinctive patterns between young, adult, and older subjects. It corroborates all the hypotheses and shows structural and cognitive functional deterioration with aging. Additionally, it reveals that each subject's brain activity is unique, with greater heterogeneity among younger individuals due to higher plasticity and diverse studies and experiences during growth.
The website is: https://github.com/jogugil/MyRC
It is in Spanish, but if you're interested, I could look into translating parts into English.
I am currently examining which parameters are most important, using optimization methods to obtain hyperparameter values, which is quite challenging, and determining which ML models better utilize the information returned by RC-ESN.
The advantage of RC-ESN is that it eliminates the need for real-time feature extraction in time, frequency, etc. However, the framework does include a library and function where you can provide an EEG signal dataset, and it will return a three-dimensional matrix for each subject with features per EEG signal.
It's a very interesting field that can help a lot of people. On my GitHub, I have a repository (MyRC) where I conduct a study on EEG signal processing using RC-ESN. I provide an API or framework for both the preprocessing of these signals and their subsequent processing with the RC model. Specifically, it focuses on studying the aging process and identifying distinctive patterns between young, adult, and older subjects. It corroborates all the hypotheses and shows structural and cognitive functional deterioration with aging. Additionally, it reveals that each subject's brain activity is unique, with greater heterogeneity among younger individuals due to higher plasticity and diverse studies and experiences during growth.
The website is: https://github.com/jogugil/MyRC
It is in Spanish, but if you're interested, I could look into translating parts into English.
I am currently examining which parameters are most important, using optimization methods to obtain hyperparameter values, which is quite challenging, and determining which ML models better utilize the information returned by RC-ESN.
The advantage of RC-ESN is that it eliminates the need for real-time feature extraction in time, frequency, etc. However, the framework does include a library and function where you can provide an EEG signal dataset, and it will return a three-dimensional matrix for each subject with features per EEG signal.
