This task is centered around the analysis of EEG data to classify different cognitive states using advanced deep learning techniques.

Dataset

Choice of Models:

1. EEGNet as it was specifically designed for EEG signal classification. It is lightweight, making it suitable for real-time applications and deployment on devices with limited computational power.
2. TSCeption as it can handle the temporal and spectral characteristics of EEG signals
3. ATCNet because it is designed to handle temporal dependencies in EEG data.

PSD Inference:

The rest state shows a higher power across all frequencies than the task state. These results seem to support notions I read that brain activity is better during rest/sleep. Be it retention, dreams, healing etc. more

Classification Results:

Model	Accuracy	Precision	Recall	F1-Score
EEGNet	0.8000	0.7000	1.0000	0.8235
TSCeption	0.8000	0.8100	0.8000	0.8000
ATCNet	0.4667	0.4667	1.0000	0.6364

While fine-tuning the hyperparameters and experimenting with the architecture, preference was given to Higher Recall. Because we are dealing with EEG signals. In cases like medical diagnostics missing a positive case is critical.