In this repository, two solutions are proposed and implemented for the HK EEG + AI competition.
Environment for Method 1:
python==3.8
pytorch>=1.12.1
numpy>=1.21.5
scipy>=1.7.3Environment for Method 2:
python==3.8
tensorflow>=2.10.0
numpy>=1.21.5
scipy>=1.7.3Please put the evaluation dataset in the folder dataset and rename it as data.mat. The whole dataset will be used for evaluation.
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For method 1 please run
python ./method1/main.py --eval -
For method 2 please run
python ./method2/main.py --eval 2> .error.log
Link to code: [Method 1]
We build two transformers to encode information from both time (temporal) and channel (spatial) domain of the EEG data. Embeddings from these two views are concatenated and fed into an MLP for the final classification task. To tackle the noise problem of EEG data and introduce more supervision signal, we further consider the temporal and spatial embeddings as two contrastive views and perform contrastive learing between them. Additionally, to further reduce noise in the data, we only choose the 0~2600ms part of every sample.
Link to code: [Method 2]
We also build an classifier based on EEGNet, the state-of-the-art model for EEG classification task. Experiments on model structure and hyperparameter are performed to better apply the model to our dataset. Additionally, to further reduce noise in the data, we only choose the 0~2600ms part of every sample.
Method 1:
python method1/main.py --dropout 0.3
Method 2:
python method2/main.py --reg 1e-6 --reg_cont 1e-8 --len_time 650
We use filter-bank:
The code for EEGNet is based on [this repo].