K
Khademul Islam Molla
Researcher at University of Rajshahi
Publications - 7
Citations - 108
Khademul Islam Molla is an academic researcher from University of Rajshahi. The author has contributed to research in topics: Support vector machine & Tangent space. The author has an hindex of 5, co-authored 7 publications receiving 72 citations.
Papers
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Journal ArticleDOI
Multiband tangent space mapping and feature selection for classification of EEG during motor imagery.
TL;DR: The increased classification accuracy of MI tasks with the proposed MTSMS approach can yield effective implementation of BCI.
Journal ArticleDOI
Unsupervised frequency-recognition method of SSVEPs using a filter bank implementation of binary subband CCA.
TL;DR: The results suggest that BsCCA significantly improves the performance of SSVEP-based BCI compared to the state-of-the-art methods and can be usable in real world applications.
Journal ArticleDOI
Climatic data analysis for groundwater level simulation in drought prone Barind Tract, Bangladesh: Modelling approach using artificial neural network
Ripon Hasda,Md. Ferozur Rahaman,Md. Ferozur Rahaman,Chowdhury Sarwar Jahan,Khademul Islam Molla,Quamrul Hasan Mazumder +5 more
TL;DR: In this paper, the authors used non-linear autoregressive model with exogenous inputs (NARX) of Artificial Neural Network (ANN), used for groundwater level (GWL) simulation to predict its weekly level up to 52 weeks ahead in selected 14 Permanent Hydrograph Stations (PHSs) in the drought prone Barind Tract in the northwestern part of Bangladesh and is considered to be the first attempt of this type in the country.
Proceedings ArticleDOI
Classification of motor imagery BCI using multiband tangent space mapping
TL;DR: The tangent space mapping (TSM) becomes an effective method to implement brain computer interface (BCI) with motor imagery and is employed with multiband approach to extract discriminative features from electroencephalogram (EEG) to enhance classification accuracy.
Proceedings ArticleDOI
Phase synchronization analysis of EEG channels using bivariate empirical mode decomposition
TL;DR: Numerical results suggest that the PLV induced from BEMD can effectively detect phase synchrony between electrodes and is a promising feature for BCI implementation.