Zine Research Lab, NIT Jaipur
EEG or Electroencephalogram refers to recordings of electrical activity in the brain. P300 is a unique signal elicited by the brain on receiving a specific stimulus. It is most commonly known for being used as a EEG based speller, where the user can, simply with his focus, spell a word. Our objective was to recognize P300 event related potential in raw EEG generated while a user experiences the P300 speller.
We applied signal processing based feature extraction, which was fed to a linear classifier ensemble. We experimented with the following significant types of features:
- Time-Series features
- Frequency-Series features
- Wavelet features
- Statistical and Experiment related features
and a few more… Finally since the EEG data consisted of a lot of data to work with, we ended up with a large variety and quantity of features to experiment with. To enable modular organization, fast code execution and stacked feature vectors we setup a framework which utilises caching, parallel computing and modularity.
- Accuracy of 97% at ideal training interval.
- Complete framework for future research usage.