Universal Joint Feature Extraction for P300 EEG Classification Using Multi-Task Autoencoder (IEEE Access)

A tool for teaching P300 by showing the ongoing averaging process and classification

The Emotion 300 Project: An Emotion Classification Messaging App w/ P300 Speller.

A research project that explores the potential of DL models in detecting ERP (p300) in EEG signals. From EDA to modeling, we developed an end-to-end training and evaluation workflow that achieved an accuracy of 98% on the EPFL dataset. We utilized the achieved results in building a Neurophone and using the famous EMOTIV headset for EEG measurement.

P300 Matrix for brain computer interfaces using html, CSS and JavaScript with mean error 1 millisecond

Implementation of Correlation function and signal averaging method for detecting P300.

A zero-shot learning pipeline to predict spelling intention from raw EEG signals

A project on developing a machine learning classification for recognizing the P300 waveform in the EEG signal and recognizing stimulus misrepresentation.