NEUROPHONE: Real-Time Brain-Mobile Phone Interface
A P300-based brain-computer interface that lets people with motor disabilities operate a smartphone through visual attention alone. I contributed to EEG data collection, signal preprocessing and CNN training.
P300 BCI
A visual-attention interface that lets people with motor disabilities operate a smartphone through EEG signals rather than physical input.
My contribution
EEG data collection, signal preprocessing, and CNN training on a co-authored paper.
Published results
A lightweight CNN reached 98% average classification accuracy and a 0.95 F1 score under 5-fold cross-validation, ahead of EEGNet, ChronoNet, DCRNN and RNN baselines on both the EPFL BCI dataset and our own recordings.
Honest limitations
The in-house dataset was small and the end-to-end delay between phone and inference API was never accurately measured. Selecting an icon took three to four flash repetitions, roughly eight seconds.
Funded by ITIDA.
Authors: Norhan Abdelhafez, Manal Tantawy, Abdelrahman Sayed, Nora Ekramy, Mohammed Nour-Eldin