Noninvasive detection of brain order-disorder transitions using functional EEG

Abstract

Systems capable of real-time monitoring have several potential health-related applications, particularly for individuals who desire to age in place as well as those living with acute health conditions. An electrical response exists near the scalp from ionic transmission within neurons (brain cells) and may be measured noninvasively by electroencephalography (EEG). Use of EEG for detecting convulsive1, 2 and nonconvulsive3 epilepsy is well developed and documented in the literature with sensitivities between 80 and 90%. Real-time EEG monitoring during sleep may provide valuable information on alterations in sleep patterns, which has been shown to be effective in detecting and predicting anxiety, depression, and early dementia in the elderly.4, 5 Anxiety and depression in turn have been directly correlated to numerous deleterious chronic health conditions such as cardiovascular disease, stroke, sleep disorders, and diabetes. Having a mechanism to detect and predict onset of anxiety and depression would be a powerful out-of-clinic tool. A potential application for at-home EEGmonitoring is assessment of head injuries and post-concussion syndrome. As these conditions affect the alertness of individuals, there is evidence that head injuries affect the alpha wave patterns of the EEG in the range of 8–12Hz.6 While many previous studies have shown the potential for EEG to detect and assess cognitive impairments, most have been conducted under controlled research conditions in the clinic. Extension of these findings to the home requires further investigation as environmental conditions are likely to affect the EEG data collected. Wang and colleagues7 showed the efficacy of an untethered, wireless brain-machine interface system using 20 dry electrodes embedded into a head cap. This wireless ‘functional’ EEG Figure 1. (a) User-friendly smartphone interface to the functional electroencephalography (f-EEG) head mount. (b) 1D Peano-Hilberttype space-filling curve replaces the 3D layout of the electrodes. (c) High-density wired conventional EEG is replaced by a wireless hat. (d) Right-hemisphere brain f-EEG response stimulated by the visual input for a left eye dominator: before (left) and after compressive sensing (middle), as well as the difference errors (right). (e) Singlet electrode-averaged EEG (left) vs. pair-electrode fluctuation f-EEG (right) reflecting the subject’s intention to point a computer screen cursor to the left as opposed to the right: 121 electrodes (1 node to 82 points), 250Hz (averaged over 1/16 second).