Yazar "Miah, Abu Saleh Musa" seçeneğine göre listele

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    Öğe
    Motor-imagery BCI task classification using riemannian geometry and averaging with mean absolute deviation
    (Ieee, 2019) Miah, Abu Saleh Musa; Ahmed, Saadaldeen Rashid Ahmed; Ahmed, Mohammed Rashid; Bayat, Oguz; Duru, Adil Deniz; Molla, Md. Khademul Islam
    Brain Computer interface (BCI) is thought as a better way to link within brain and computer alternative machine. Many types of physiological signal will work BCI framework. Motor imagery (MI) has incontestable to be a excellent way to work a BCI system. Recent research concerning MI based mostly BCI framework, lower performance accuracy and intense of time have common issues. Main focuses of this paper is select the appropriate central point of tangent space in Tangent Space Linear Discriminant analysis-based Motor-Imagery Brain-Computer interfacing. Method name tangent space mapping LDA (TSMLDA) analysis takes its moves from the observations that normally, the EEG signal embodies outliers, so the centrality as a geometric mean of tangent space might not be the simplest alternative. We tend to propose the employment of strong estimators of variance matrices average. Specifically, Median Absolute Deviation(MAD) going to be planned and mentioned. Associate in Nursing experimental analysis can show the advance of Tangent house Linear Discriminant Analysis corresponding to the planned strong estimators. Experimental results show that our proposed method performs 3% better than the recently developed algorithms.
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    Öğe
    Real time sleep onset detection from single channel EEG signal using block sample entropy
    (IOP Publishing Ltd, 2020) Zobaed, Talha; Ahmed, Saadaldeen Rashid Ahmed; Miah, Abu Saleh Musa; Binta, Salma Masuda; Ahmed, Mohammed Rashid Ahmed; Rashid, Mamunur
    In recent years, driver's temporary state has been one in each of the foremost causes of road accidents and would possibly lead to severe physical damaging, mortality and necessary and noticeable economic losses. Maximum road accidents possible to avoided, if possible, to properly monitored driver's drowsiness and a system are given warnings. In this work, a simple and inexpensive method has been offered to detect driver's drowsiness or sleep onset detection with single channel EEG signal analysis. The key novelty of this work is to identify the sleep onset detection from a publicly available graph signal dataset by exploitation only one feature, simply implementable filter in any microcontroller device or smartphone and a threshold based mostly classification. Since, threshold-based classification techniques don't need to train the classifier, hence, new subject adaptation is comparatively easier and real time implementation is more feasible. This novel approach can be easily implemented in smartphone to design and expand a drowsiness detection and alarming system for vehicle's driver. On a variety of subjects, the experimental results show 95.68% accuracy. © 2020 Published under licence by IOP Publishing Ltd.