seed数据集相关代码整理

# Spatio-Temporal EEG Representation Learning on Riemannian Manifold and Euclidean Space (TF v1.14.0) [Spatio-Temporal EEG Representation Learning on Riemannian Manifold and Euclidean Space](https://arxiv.org/abs/2008.08633) This repository contains the source code of our paper, using following datasets: - Emotion Recoginition: - [SEED](https://bcmi.sjtu.edu.cn/~seed/seed.html): 15 subjects participated experiments with videos as emotion stimuli (positive/negative/neutral) and EEG was recorded with 62 channels at sampling rate of 1000Hz. - [SEED-VIG](https://bcmi.sjtu.edu.cn/~seed/seed-vig.html): Vigilance estimation using EEG data in a simulated driving task. 23 subjects participated experiments and 17 EEG channels were recorded at sampling rate of 1000Hz. - Motor Imagery: - [BCI-IV 2a](https://www.bbci.de/competition/iv/#dataset1): 9 subjects were involved in motor-imagery experiment (left hand, right hand, feet and tongue). 22 EEG recordings were collected at sampling rate of 250Hz. - [BCI-IV 2b](https://www.bbci.de/competition/iv/#dataset1): 9 subjects were involved in motor-imagery experiment (left hand and right hand). 3 EEG channels were recorded at sampling rate of 250Hz. ## Prerequisites Please follow the steps below in order to be able to train our models: 1 - Install Requirements ``` pip3 install -r ./requirements.txt ``` 2 - Download dataset, then [load data](./code/load_data.py), proprocessing data through [filter bank](./code/library/signal_filtering.py), and perfom [feature extraction](./code/library/feature_extraction.py). 3 - Save the preprocessed data and EEG into separate folders (e.g., '/train/EEG/' and '/train/Extracted Features'). Move data and corresponding labels to the address shown in functions 'load_dataset_signal_addr' and 'load_dataset_feature_addr' from [utils](./code/utils.py). 4 - Perform hyper-parameters search for each individual stream. (1) For spatial information stream, run `python3 ./main_spatial_val.py --dataset datasetname` to search the rank of EEG covariance matrices. For example, run the following command ``` python3 ./main_spatial_val.py --dataset BCI_IV_2a --cpu-seed 0 --gpu-seed 12345 --lr 0.001 --batch-size 32 --epochs 200 --early-stopping 20 --riemannian_dist ``` for BCI_IV_2a dataset using riemannian projection. (2) For temporal information stream, run `python3 ./main_temporal_val.py --dataset datasetname` to obtain the result for different LSTM settings. For example, run the following command ``` python3 ./main_temporal_val.py --dataset SEED --cpu-seed 0 --gpu-seed 12345 --lr 0.001 --batch-size 8 --epochs 200 --early-stopping 20 -- BiLSTM --layer-num 2 ``` for SEED dataset using two bidirectional LSTM layers. Validation results will be automatically saved in the adddress in functions 'save_spatial_val_result' and 'save_temporal_val_result' from [utils](./code/utils.py). The paramaters are saved and updated in [dataset_params](./code/dataset_params.yaml). 5 - Run the experiments for test data. For example, run the following command ``` python3 ./main.py --dataset BCI_IV_2b --cpu-seed 0 --gpu-seed 12345 --lr 0.001 --batch-size 32 --epochs 200 --early-stopping 100 --riemannian_dist ``` for BCI-IV 2b dataset. ## Document Description - `\code\library`: Riemannian embedding estimation, feature preprocessing and extraction files - `\code\model`: Models for spatial, temporal and spatio-temporal streams of our architecture. If you find this material useful, please cite the following article: ## Citation ``` @article{zhang2020rfnet, title={Spatio-Temporal EEG Representation Learning on Riemannian Manifold and Euclidean Space}, author={Zhang, Guangyi and Etemad, Ali}, journal={arXiv preprint arXiv:2008.08633}, year={2020} } ``` <img src="/doc/architecture.jpg" width="1100" height="350"> ## Contact Should you have any questions, please feel free to contact me at [guangyi.zhang@queensu.ca](mailto:guangyi.zhang@queensu.ca). <!-- <img src="/doc/architecture.pdf" width="400" height="200"> -->