For each epoch in the Testing.zip file, participants need to make the corresponding prediction for each epoch according to different usage in the Testing_Info.csv file and submit result_name_date.csv file

* The usage 3 indicates the Identification mode. Participants need to judge whether the epoch is from an intruder. If yes, returns 0. If not, return the predicted subject ID.
* The usage 4 indicates the verification mode. Participants needs to judge whether the epoch is from subject as he or she claimed. If yes, return 1. If not, return 0.

A baseline method is provided with the following link,
[Baseline.zip](https://storage.googleapis.com/kaggle-competitions-data/kaggle-v2/37941/4046133/compressed/Baseline.zip?GoogleAccessId=web-data@kaggle-161607.iam.gserviceaccount.com&Expires=1659170547&Signature=xCow0WpxZ%2FJLcy48CSpMBPpRxFLiJtduvJ1NPrv6jo0Uw80OPCAmODe5OfnWOTD2l40cPiIQRAq8A16FPm2GuV%2FuvQtDZ2S%2F6MQNR7IBcbxG9vBFX3xzzE%2Ffw2EarkZmsENkFLQkC%2FNQ9EsD0vBeZqsoI80WWV19u47iOJN5dkrvGBfF7kh%2B2z5VUVzVHjdCIyIHfrgx8dc3SeRs6d1%2B9IosVGpVG2%2B5olkhABafafYjGQI1wf%2BclQQwlnAPX2v7F0Z5ei3uOcTAGzUHPMBhGh5MLTnNwAh38rRD%2BNux%2F5QuvU6p6ZX1D6RS8Q5t%2FNb%2BYBFj%2F%2BbEbzgZ734ahhKCXw%3D%3D&response-content-disposition=attachment%3B+filename%3DBaseline.zip)

The mean recognition accuracy for all the identification and verification tasks is used as the performance metric for this competiution.. 


## Submission Format

**For every author in the dataset**, submission files should contain two columns: `EpochID` and `Prediction`. `EpochID` should be the ID of the epoch. `Prediction` shoule be the prediction result for each epoch.


The file should contain a header and have the following format:
```
EpochID,Prediction
epoch057852,43
epoch057853,43
epoch057854,1
epoch057855,6
epoch057856,0
epoch057857,0
epoch057858,0
epoch057859,52
```

An example submission is also provided,
[Result\_example\_0909\_232857.csv](https://storage.googleapis.com/kagglesdsdata/competitions/38725/4244254/Result_example_0909_232857.csv?GoogleAccessId=web-data@kaggle-161607.iam.gserviceaccount.com&Expires=1663128258&Signature=EiMYqH3wVjkKSVtxlz7DskJJNnq3eYz%2BAO7zm6srrIdmTh8J6JhiP4gtErgUKzYmyeVAi7DLIy6c4JVo%2BWi3TOUI975oO2j0MKPzMDJaFH3xL7bUvpv7pYSgb3lh7UCUQgA%2B%2BY96%2FSNB%2FhXtz0x05uRziqgATJxYCxHlWc8lZ7J8%2Bzx3fYCMci5rr5F1Lvq5xVORTxm0lp1ckivSxh2IwU6OA5ZVWOJwJ9Rra1Gx7A3tzpZTUXi9M7ZijUzyyZNPFlBz90%2Fcr4aNvlQsrNunxrQsQOJU1coL3YdMzLi4IFH3JVzp068Z58PTavtGZKwfSK1BYbMloCIkcBx6ZIuIGA%3D%3D&response-content-disposition=attachment%3B+filename%3DResult_example_0909_232857.csv)

## Reference:
Gan Huang, Zhenxing Hu, Weize Chen, Zhen Liang, Linling Li, Li Zhang, Zhiguo Zhang. M3CV:A Multi-subject, Multi-session, and Multi-task database for EEG-based Biometrics Challenge. doi: [https://doi.org/10.1101/2022.06.28.497624](https://www.biorxiv.org/content/10.1101/2022.06.28.497624v1)[[PDF](https://www.biorxiv.org/content/10.1101/2022.06.28.497624v1.full.pdf)]