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.
A project on developing a machine learning classification for recognizing the P300 waveform in the EEG signal and recognizing stimulus misrepresentation.
Development of the BCI interface
P300 Matrix for brain computer interfaces using html, CSS and JavaScript with mean error 1 millisecond
A zero-shot learning pipeline to predict spelling intention from raw EEG signals
Perform participant-level data integration for EEG BCI calibration.
Implementation of Correlation function and signal averaging method for detecting P300.
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