Also, a general wavelet transform based approach was presented which can be used in future to remove motion artifacts. Motion artifacts were characterized with very low frequency which overlapped with the Delta rhythm of the EEG. Quantitative study shows that in comparison to correlation coefficient, the coherence analysis depicted a better similarity measure between motion artifacts & motion sensor data. No such previous EEG recordings are available in research history that explicitly mentioned and considered a number of daily activities that induced motion artifacts in EEG recordings. For this purpose, an EMOTIV EEG headset alongside built-in motion sensors was used. In this paper, a unique EEG dataset was presented where ten different activities were performed. Although different papers have proposed various novel approaches for removing motion artifacts, the datasets used to validate those algorithms are questionable. The performance of mobile health monitoring, neurological disorders diagnosis and surgeries can be significantly improved by reducing the motion artifacts. It is one of the major challenges for ambulatory EEG. Motion artifacts contribute complexity in acquiring clean EEG data. Experiments in real-world environments show the potential practicality of reallife applications of low-cost wearable and wireless BCI systems for users actively working in and interacting with their environments. This study demonstrates the robustness of our system to remove gait-related movement artifacts during human locomotion. Then, we apply an adaptive Kalman filter to estimate the mapping between the stride-based artifact template and EEG space, subtracting the motion-related noise from the raw EEG signal. We first construct stride-based artifact templates employing a gyroscope to measure the angular velocity of the human body. Here, we present a real-time ambulatory brain computer interface which allows us to detect gait phases and remove motion-related artifacts from EEG signals during walking in real-world environments. EEG signals have been considered to be too noisy to record brain dynamics during human locomotion. Although human cognition often occurs while moving, most studies of the dynamics of the human brain examine subjects while static and seated in a highly controlled laboratory.
0 Comments
Leave a Reply. |
AuthorWrite something about yourself. No need to be fancy, just an overview. ArchivesCategories |