Prefrontal Activation in the EEG Biofeedback training using a General-Purpose Brainwave Meter Lee Jae-Yong1, Lim Cheong-Hwan2 1Department of Computer Engineering, Hanseo University, Seosan-si, CO-31962, Korea 2Department of Radiological Science, Hanseo University, Seosan-si, CO-31962, Korea Online published on 14 May, 2018. Abstract Commercialized EEG monitoring technologies present no standard for evaluating conditions on practicing brain self-regulation; in order to verify the standardization of brain self-regulation through a Human Computer Interface(HCI) reference posture. Both Autogenic Training(AT) and ANSI/HFES 100 HCI reference postures were studied for similarities and differences. From these, six postures were similar and two other postures were somewhat different. To verify availability in applying the reference posture of ANSI/HFES 100–2007 in measuring brain waves as a standard posture, this thesis measured the brain wave of users after practicing AT in an upright sitting posture, for those already having acquired AT. Brain waves recorded for 120 seconds from 6 participants at an upright sitting posture, who had acquired the ability to enter an autogenic shift within 10 seconds. The α-wave made up 16∼21% while the θ-wave was 27∼29% of the brain waves. Training was applicable to all the users despite the users having differences positioning their head downward or not and their arms and hands resting on their knees or not, with differences in the leaning posture of AT and the comfortable workspace posture of ANSI/HFES 100. The results indicated that measuring brain waves after training for self-regulation just like the reference posture allows for maintaining a state of very high concentration. As such, there is a need for defining concentration standards by conducting a comparison of brain waves in general and after training self-regulating brain, both under the HCI conditions. In addition, a study on brain wave changes for untrained and subsequently trained users, both at the HCI posture, seems necessary. The methodology allows for quantifying how much concentration is present for a user interacting with a computer in situations such as controlling equipment remotely or in an education setting. Top Keywords Brain wave, HFES 100, HCI, Alpha wave, Theta wave. Top |