Study and analysis of motion artifacts for ambulatory electroencephalography

Abstract

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

A survey on bio-signal analysis for human-robot interaction

“…In study [14], researchers created a new EEG dataset that was infected with real-time motion artifacts and discovered that coherence was a better similarity metric between the motion artifact-infected EEG and motion sensor data. This real dataset was created to aid researchers working on EEG motion artifact reduction techniques by allowing them to compare and contrast existing methods as well as develop new models.…”

Efficient EEG motion artifact elimination framework for ambulatory epileptic seizure detection application

“…Despite the advances in EEG technology and seizure detection algorithms [9-12], motion artifacts remain a persistent issue in ambulatory EEG recordings. These artifacts can introduce false-positive or false-negative seizure detections, leading to potentially life-threatening consequences for patients [13,14]. Therefore, the development of robust and efficient techniques for motion artifact removal is essential to improve the accuracy and efficacy of ambulatory seizure detection systems.…”