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Proceedings Paper

Control channels in the brain and their influence on brain executive functions
Author(s): Qinglei Meng; Fow-Sen Choa; Elliot Hong; Zhiguang Wang; Mohammad Islam
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Paper Abstract

In a computer network there are distinct data channels and control channels where massive amount of visual information are transported through data channels but the information streams are routed and controlled by intelligent algorithm through “control channels”. Recent studies on cognition and consciousness have shown that the brain control channels are closely related to the brainwave beta (14-40 Hz) and alpha (7-13 Hz) oscillations. The high-beta wave is used by brain to synchronize local neural activities and the alpha oscillation is for desynchronization. When two sensory inputs are simultaneously presented to a person, the high-beta is used to select one of the inputs and the alpha is used to deselect the other so that only one input will get the attention. In this work we demonstrated that we can scan a person’s brain using binaural beats technique and identify the individual’s preferred control channels. The identified control channels can then be used to influence the subject’s brain executive functions. In the experiment, an EEG measurement system was used to record and identify a subject’s control channels. After these channels were identified, the subject was asked to do Stroop tests. Binaural beats was again used to produce these control-channel frequencies on the subject’s brain when we recorded the completion time of each test. We found that the high-beta signal indeed speeded up the subject’s executive function performance and reduced the time to complete incongruent tests, while the alpha signal didn’t seem to be able to slow down the executive function performance.

Paper Details

Date Published: 22 May 2014
PDF: 11 pages
Proc. SPIE 9107, Smart Biomedical and Physiological Sensor Technology XI, 910716 (22 May 2014); doi: 10.1117/12.2050147
Show Author Affiliations
Qinglei Meng, Univ. of Maryland, Baltimore County (United States)
Fow-Sen Choa, Univ. of Maryland, Baltimore County (United States)
Elliot Hong, Univ. of Maryland School of Medicine (United States)
Zhiguang Wang, Univ. of Maryland, Baltimore County (United States)
Mohammad Islam, Univ. of Maryland, Baltimore County (United States)


Published in SPIE Proceedings Vol. 9107:
Smart Biomedical and Physiological Sensor Technology XI
Brian M. Cullum; Eric S. McLamore, Editor(s)

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