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. However, the lack of availability of EEG monitoring system capable of high-definition recording, online signal processing and artifact cancellation, without use of conductive gels applied to the scalp, has long thwarted both military and civilian applications of EEG monitoring in the workplace. For several years, we have made important progresses in developing signal processing methods to extract EEG correlates of cognitive state changes, attention, event perception and response. We have also collaborated with leading experts in electrical and neural engineering at Brain Research Center, National Chiao Tung University, Taiwan to design, fabricate and test dry biosensors and miniaturiized bio-amp, ADC and wireless chips that allow a radically new vision of dynamic brain imaging - development of non-invasive, mobile, high-definition brain imaging (HDBI) of cortical electromagnetic and energy dynamics in human subjects freely moving within their 3-D environments. In particular, we have successfully consolidated our expertises and achievements in circuit design, micro-fabrication, signal processing algorithms, control & robotic engineering, real-time embedded and DSP software development and virtual-reality techniques to build a BCI platform that we expect to have disruptive future impacts on clinical research and practice in neurology, psychiatry, gerontology, and rehabilitation medicine.
A photo of a mobile wireless EEG system that incorporates novel dry MEMS electrodes that do not require any skin preparation or conductive pastes and miniaturized battery-powered bioamps, filters, analog-to-digital converters and wireless telemetry circuits to enable imaging of participants actively performing ordinary tasks in natural body positions and situations in operational environments.
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.
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.
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.
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.