Publication

@article{Papakostas2017TowardsPT, title={Towards predicting task performance from EEG signals}, author={Michalis Papakostas and Konstantinos Tsiakas and Theodoros Giannakopoulos and Fillia Makedon}, journal={2017 IEEE International Conference on Big Data (Big Data)}, year={2017}, pages={4423-4425} }

EEG Dataset for the paper: Towards Predicting Task Performance from EEG Signals

DATA AVAILABLE @ : https://github.com/MikeMpapa/EEG-Dataset--Sequence-Learning

DATA FROMAT: userID_sessionID_roundID

Each file includes all the signals captured with the MUSE for one round:

1. RAW EEG

2. A,B,D,G,T Frequency Abdolute Values

3. A,B,D,G,T Frequency Relative Values

4. A,B,D,G,T Frequency Scores --> Relative Value Compared to the most Recent Data distribution

5. C metric --> Concetration Value given by MUSE's digital component

6. H metric --> Signal QUality metric

Publication

@article{Papakostas2017TowardsPT, title={Towards predicting task performance from EEG signals}, author={Michalis Papakostas and Konstantinos Tsiakas and Theodoros Giannakopoulos and Fillia Makedon}, journal={2017 IEEE International Conference on Big Data (Big Data)}, year={2017}, pages={4423-4425} }