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.

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Stroke is one of the leading cause of disability and death throughout the world. Among the hospitalized neurological patients, 60% acquire gait disturbance. Functional electrical stimulation (FES) is a form of electrical current which helps to contract the weak muscles in the patient with stroke. Several studies were conducted to identify the effectiveness of FES on gait performance. Therefore the purpose of the article was to identify the good quality RCTs and find their results regarding the effectiveness of FES on gait performance of stroke patient. 5 randomized control trials were identified from the Physiotherapy Evidence Database (PEDro) and their score was 5 and above. A careful analysis was performed to make synopsis of those articles and presented in table with description. The first 4 studies found that a combination approach such as Mirror Therapy, Action Observational Training, Brain-computer interface and standard rehabilitation along with FES is effective in gait performance than the FES alone. One study compared the effectiveness of dual and four channels FES where they found 4 channels FES is more effective than dual channel. Therefore the review concluded that FES combined with other treatment modalities is beneficial to restore gait performance of patient with stroke.