Brain-Computer Interface Technology: Third International Meeting
June 14-19, 2005
Scott Makeig, Paul Hammon, Julie Onton , Swartz Center for Computational Neuroscience, Institute for Neural Computation University of California San Diego
Modeling mechanisms of biological control of EEG rhythms
While the exact etiology and biological functions of macroscopic brain potentials that reach the scalp of humans and can be recorded as the electroencephalogram (EEG) are unknown, increasing evidence and modeling efforts suggest that far from representing only the distant 'roar of the crowd' of cortical neurons, EEG activities, and notably oscillatory activities, play roles in the production of optimally coordinated behavior by biasing the timing of cortical communication, both within and between brain areas.
'Closing the [brain] loop' by providing near-immediate sensory feedback about one's own EEG rhythms has long been feasible, and was popularly advocated by some 30 years ago for relaxation and altering mental state, Today, research, experimentation, peri-professional practice in this area is again increasing in at least three directions. First, the field of brain-machine interface (BMI), brain-computer interface (BCI), or brain-accuated control (BAC) system design, the focus of this meeting, is attracting widespread interest from both rehabilitation specialists and engineers. Second, a growing number of unlicensed practitioners of 'neurofeedback' therapies claim to be able to treat a wide range of physical, mental, emotional and behavioral conditions, though with little formal medical research to back up their claims and observations and less understanding of the biological mechanisms involved. Finally, there are continuing efforts to use EEG feedback for mental state monitoring in transportation and other work places. Proposals range from monitoring the emergence of subject drowsiness during long pilot work shifts to near-instantly reading out early target-related brain responses during rapid image presentation.
Our laboratory is currently investigating the application of independent component analysis (ICA) to the design of BCI, mental monitoring, and/or neurofeedback interfaces. While most current studies involve the feedback of frequency information from one or two EEG channels, we propose a system where feedback delivers time/frequency information from independent components of the EEG. The signal at a single electrode represents the summation of many different sources of brain activity. Therefore, to control the signal recorded at a single electrode, one may have to control the activities of multiple cortical sources. In contrast, independent components account for the contributions of an isolated cortical source to the entire set of scalp electrodes.
We are particularly interested in testing whether such feedback may improve overall task performance, in particular during expert monitoring and learning. Such improvement might be expected if the independent component process variables controlled are involved in or, more broadly, are controlled by brain systems that regulate cognitive processes involved in optimum performance, for example attention and working memory. We have built a simple neurofeedback paradigm, currently based on the Wolpaw-lab BCI2000 software, to test this hypothesis, and are currently running pilot experiments to refine our experimental procedures. We believe that these experiments can also address fundamental questions about control of cortical EEG. Is it possible to learn to control a single independent EEG component without affecting the activities of other components?
A new form of ICA decomposition, log spectral ICA, reveals that discrete frequency bands in limited sets of independent EEG components are under common modulatory control during cognitive task performance. Applying this form of analysis to EEG data recorded during successful BCAI or neurofeedback should tell us whether subjects use the same modulatory processes to control their brain rhythm during feedback. This will bear on the fundamental relationship between macroscopic EEG activities and distributed cortical arousal and control systems.
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