인문학
사회과학
자연과학
공학
의약학
농수해양학
예술체육학
복합학
DBpia AI
AI 에이전트
AI 아이디어
AI 리더
AI 뷰어
Citeasy
크롬 서지관리 다운로드
지원사업
학술연구/단체지원/교육 등 연구자 활동을 지속하도록 DBpia가 지원하고 있어요.
커뮤니티
연구자들이 자신의 연구와 전문성을 널리 알리고, 새로운 협력의 기회를 만들 수 있는 네트워킹 공간이에요.
초록·키워드
Mobile Electroencephalography (EEG) measures human brain activity during locomotion, extending brain dynamics research into real-world scenarios. However, EEG is highly susceptible to artifacts, and more reliable approaches are needed to attenuate motion artifacts in mobile EEG recordings. Dual-layer EEG, which records scalp EEG simultaneously with isolated motion artifact signals, presents a novel and promising approach. This method assumes that noise-biased EEG data captures the common noise as the isolated motion artifact data. The purpose of this study was to investigate the relationship between signals from both sides of the electrode and their relationship to movement. We developed a benchtop test platform where the top and bottom sides of a dual-sided electrode interfaced with conductive fabric. Using a robotic arm, we moved the dual-sided electrode with different directions, magnitudes, frequencies, and randomness. We quantified correlations between signals from the top and bottom sides to understand the relationship of the dual-sided signals. We also quantified correlations between movement and signals from both the top and bottom sides to examine the extent to which the signals are related to movement. Movement direction affected the correlation signs, and all correlation metrics scaled with movement magnitude rather than frequency. Increased movement randomness reduced top and bottom signal correlation. These findings contribute new insights into signal correlations of the dual-sided EEG electrode and could inform the development of improved dual-layer EEG algorithms and hardware innovations for real-world applications.
인공지능 문자 인식 모델을 통해 추출된 텍스트로, 일부 오타나 오류가 포함될 수 있으나 지속적으로 개선 중입니다.
오류를 발견하셨다면 해당 부분을 드래그한 후 ' 를 통해 신고해주세요.
오류를 발견하셨다면 해당 부분을 드래그한 후 ' 를 통해 신고해주세요.
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