Study and analysis of motion artifacts for ambulatory electroencephalography

Motion artifacts contribute complexity in acquiring clean electroencephalography (EEG) data. It is one of the major challenges for ambulatory EEG. The performance mobile health monitoring, neurological disorders diagnosis and surgeries can be significantly improved by reducing motion artifacts. Although different papers have proposed various novel approaches removing artifacts, datasets used to validate those algorithms are questionable. In this paper, a unique EEG dataset was presented where ten activities were performed. No such previous recordings using EMOTIV headset available research history that explicitly mentioned considered number daily induced recordings. Quantitative study shows comparison correlation coefficient, coherence analysis depicted better similarity measure between sensor characterized with very low frequency which overlapped Delta rhythm Also, general wavelet transform based approach remove Further experiment more metrics longer recording duration each activity required finalize characteristics henceforth reliably identify subsequently contaminated