low frequency
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2022 ◽  
Vol 204 ◽  
pp. 107703
Author(s):  
Chuanjian Wu ◽  
Dahai Zhang ◽  
Jinghan He

2022 ◽  
Vol 51 ◽  
pp. 101908
Author(s):  
Chen Geng ◽  
Ying Li ◽  
Yoshinobu Tsujimoto ◽  
Michihiro Nishi ◽  
Xianwu Luo

2022 ◽  
Vol 3 (1) ◽  
pp. 101082
Author(s):  
Daniele Novarina ◽  
Fernando R. Rosas Bringas ◽  
Omar G. Rosas Bringas ◽  
Michael Chang

Author(s):  
Asma Islam ◽  
Eshrat Jahan Esha ◽  
Sheikh Farhana Binte Ahmed ◽  
Md. Kafiul Islam

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.


Author(s):  
Noor Thamer Almalah ◽  
Faris Hasan Aldabbagh

<p>In this paper, a designed circuit used for low-frequency filters is implemented and realized the filter is based on frequency-dependent negative resistance (FDNR) as an inductor simulator to substitute the traditional inductance, which is heavy and high cost due to the coil material manufacturing and size area. The simulator is based on an active operation amplifier or operation transconductance amplifier (OTA) that is easy to build in an integrated circuit with a minimum number of components. The third and higher-order Butterworth filter is simulated at low frequency for low pass filter to use in medical instruments and low-frequency applications. The designed circuit is compared with the traditional proportional integral controller enhanced (PIE) and T section ordinary filter. The results with magnitude and phase response were compared and an acceptable result is obtained. The filter can be used for general applications such as medical and other low-frequency filters needed.</p>


2022 ◽  
Vol 12 (3) ◽  
pp. 564-568
Author(s):  
Ming Lei ◽  
Junjian Zhang ◽  
Dongmei Wu

<sec> <title>Objective:</title> By using amplitude of low-frequency fluctuations (ALFF) we have analyzed activationsin brain regions at different phases in migraineurs. </sec> <sec> <title>Methods:</title> Participants included 41 patients with migraine, 19 in episode and 22 in interictal phase, and 22 controls in the healthy condition. To analyze the brain function of patients and controls, ALFF was used for performing the post-processing in the resting state by scores of Montreal Cognitive Assessment (MoCA) scale, Mini-Mental State Examination (MMSE), Hamilton Anxiety Rating Scale (HAM-A) and Hamilton Depression Rating Scale (HAM-D). </sec> <sec> <title>Results:</title> The comparison between groups of patients with migraine in the episode or interictal phases, and healthy controls showed that both episode and interictal migraine groups had the similar HAM-A and HAM-D scores (P > 0.05), but higher than that in controls (P < 0.01). For ALFF values of Episode and Interictal groups, the Montreal Neurological Institute (MNI) coordinates of the decreased ALFF were (−9, 42, 9), the voxel size = 215, including the bilateral anterior cingulate cortex (ACC), T =−4.15, without significant differences. Patients in Interictal group were with a stronger activation at MNI coordinates (12, 51, 12), in the bilateral ACC, voxel size = 90, T =3.87. </sec> <sec> <title>Conclusion:</title> ACC plays an adaptive, regulatory role in migraine and is related to multiple brain regions, which may mediate activation through descending anti-nociceptive pathways. ACC is related to opioid receptor and glutamate excitatory regulation. </sec>


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