Keywords: ICA on EEG; ICA on fMRI; EEG analysis; ERP; Human-computer interface/interaction; alertness monitoring

Research Projects

1. EEG artifact removal using Blind Source Separation.
   Severe contamination of EEG activity by eye movements, blinks, muscle, heart and line noise is a serious problem for EEG interpretation and analysis. We propose to apply ICA to multichannel EEG recordings and remove a wide variety of artifacts from EEG records by eliminating the contributions of artifactual sources onto the scalp sensors. Our results show that ICA can effectively detect, separate and remove activity in EEG records from a wide variety of artifactual sources, with results comparing favorably to those obtained using regression-based and Principal Component Analysis methods.
   
   ICA separates underlying brain and artifactual sources.

2. Extracting single-trial evoked responses from spontaneous EEG
   It is widely suspected, though poorly documented, that in single stimulus epochs the evoked response activity may vary widely in both time course and scalp distribution. The major difficulty in comparing single trials is that the spontaneous EEG activity may obscure response-evoked activity, since spontaneous EEG is typically much larger than the evoked response. ICA constructs spatial filters that can separate ERPs from EEG and artifactual sources.

3. Event-related Potentials
   ICA provides a new means of separation of multichannel EEG/ERP data into spatially-fixed and temporally independent components, and opens a new and potentially useful window into complex event-related brain data that can complement other analysis techniques.
   A sub-component of P300.

4. function Magnetic Resonance Imaging (fMRI) analysis.
   In the case of fMRI analysis, ICA decomposes the fMRI data sets into spatially independent fMRI ``sources'' independently modulating the fMRI Blood Oxygenation Level Dependent (BOLD) signal and summing to the observed data, without a priori knowledge of the time course of signal changes or spatial distribution.

5. Neural human-machine interface
   It has also been known for more than half a century that signal changes related to alertness, arousal, sleep, and cognition are present in EEG recordings. For several years, we have been working to develop a method for objective EEG-based alertness monitoring. We believe the development of alertness monitoring capabilities is a first step in the emergence of a new human-computer interaction technology - the neural human-computer interface/interaction.