We currently calculate several features from signal data on a nightly basis since these calculations are computationally expensive. If we can speed these calculations up, we can utilize these features within the live BCI system. This body of work aims to use Convolutional Neural Networks (CNNs) to learn to predict nightly feature values from signal data in order to quickly calculate these features.
We should start by predicting the live system features to understand how well CNNs are at this task. Once we've done that, we can move onto predicting the nightly features.
Since we currently don't have live rat ECoG signals to use, we can learn how effective CNNs are at predicting nightly features based on human ECoG data. This isn't exactly analogous, but it should give us an indication of how effective this ML system would be. We have ECoG data from humans playing a game:
Transfer Entropy between hemispheres (left and right)
Transfer Entropy sequence for sliding windows between hemispheres
CNN input features from Transfer Entropy sequence between hemispheres
RNN input features from Transfer Entropy sequence between hemispheres
Transfer Entropy between hemispherical channel pairs
Transfer Entropy between brain regions (frontal, temporal, parietal, occipital)
Full granularity Transfer Entropy between all channel pairs
Live System Features
These features are fast to calculate and are thus included in the live BCI system. Try to predict these first using CNNs to understand how effective CNNs are. You can find the features listed here:
Description
We currently calculate several features from signal data on a nightly basis since these calculations are computationally expensive. If we can speed these calculations up, we can utilize these features within the live BCI system. This body of work aims to use Convolutional Neural Networks (CNNs) to learn to predict nightly feature values from signal data in order to quickly calculate these features.
We should start by predicting the live system features to understand how well CNNs are at this task. Once we've done that, we can move onto predicting the nightly features.
Primer on CNNs
https://www.youtube.com/watch?v=8iIdWHjleIs&t=0s
Signal Dataset
Since we currently don't have live rat ECoG signals to use, we can learn how effective CNNs are at predicting nightly features based on human ECoG data. This isn't exactly analogous, but it should give us an indication of how effective this ML system would be. We have ECoG data from humans playing a game:
Dataset link - https://openneuro.org/datasets/ds004770/versions/1.0.0 Dataset Google Drive Link: https://drive.google.com/file/d/1Wh8SJ1qZ3_mBZdX_Hukz04uYbYQvfXSQ/view?usp=sharing Dataset Paper: https://assets.researchsquare.com/files/rs-3581007/v1/c27bf88d-3f89-4b8f-bf79-0fd848624f38.pdf?c=1702544255 Dataset Name: sub-01_ses-task_task-game_run-01_ieeg.edf
Nightly Features
In this section, we will list out the nightly features we want to be able to predict quickly from ECoG data. These features can be found here:
https://github.com/Metaverse-Crowdsource/EEG-Chaos-Kuramoto-Neural-Net/blob/main/Systems_and_states/Experiments.ipynb
https://github.com/Metaverse-Crowdsource/EEG-Chaos-Kuramoto-Neural-Net/blob/main/Spectral%20Analysis/Spectral%20Analysis.ipynb
https://github.com/Metaverse-Crowdsource/EEG-Chaos-Kuramoto-Neural-Net/blob/main/Transfer%20Entropy/Transfer%20Entropy.ipynb
Live System Features
These features are fast to calculate and are thus included in the live BCI system. Try to predict these first using CNNs to understand how effective CNNs are. You can find the features listed here:
https://github.com/Unlimited-Research-Cooperative/Bio-Silicon-Synergetic-Intelligence-System/blob/053ed0dc3cde103366c1de34ffbc548c4335ccc2/Software/PC/Backend/desktop_browser_app/system/constants.py#L56
Peaks:
Task List