Biology subjects

Miah, M. O.

Publications and source records attributed to Miah, M. O..

4 recordsLinked to original sources

bioRxiv2024

Augmented Reality and Cross-Device Interaction for Seamless Integration of Physical and Digital Scientific Papers

Researchers face the challenge of efficiently navigating vast scientific literature while valuing printed papers in the digital age. Printed materials facilitate deeper engagement and comprehension, leading to superior exam performance and enhanced retention. However, existing digital tools often need to pay more attention to the needs of researchers who value the tactile benefits of printed documents. In response to this gap, we introduce AR-PaperSync, a transformative solution that leverages Augmented Reality (AR) and cross-device interaction technology. AR-PaperSync seamlessly integrates the physical experience of printed papers with the interactive capabilities of digital tools. Researchers can effortlessly navigate inline citations, manage saved references, and synchronize reading notes across mobile, desktop, and printed paper formats. Our user-centric approach, informed by in-depth interviews with six researchers, ensures that AR-PaperSync is tailored to its target users needs. A comprehensive user study involving 28 participants evaluated AR-PaperSyncs significantly improved efficiency, accuracy, and cognitive load in academic reading tasks compared to conventional methods. These findings suggest that AR-PaperSync enhances the reading experience of printed scientific papers and provides a seamless integration of physical and digital reading environments for researchers.

scientific communication and education↗

bioRxiv2023

ODL-BCI: Optimal deep learning model for brain-computer interface to classify students concentration via hyper-parameter tuning

Brain-computer interface (BCI) research has gained increasing attention in educational contexts, offering the potential to monitor and enhance students cognitive states. Real-time classification of students confusion levels using electroencephalogram (EEG) data presents a significant challenge in this domain. Since real-time EEG data is dynamic and highly dimensional, current approaches have some limitations for predicting mental states based on this data. This paper introduces an optimal deep learning (DL) model for the BCI, ODL-BCI, optimized through hyperparameter tuning techniques to address the limitations of classifying students confusion in real time. Leveraging the "confused student EEG brainwave" dataset, we employ Bayesian optimization to fine-tune hyperparameters of the proposed DL model. The model architecture comprises input and output layers, with several hidden layers whose nodes, activation functions, and learning rates are determined utilizing selected hyperparameters. We evaluate and compare the proposed model with some state-of-the-art methods and standard machine learning (ML) classifiers, including Decision Tree, AdaBoost, Bagging, MLP, Nave Bayes, Random Forest, SVM, and XG Boost, on the EEG confusion dataset. Our experimental results demonstrate the superiority of the optimized DL model, ODL-BCI. It boosts the accuracy between 4% and 9% over the current approaches, outperforming all other classifiers in the process. The ODL-BCI implementation source codes can be accessed by anyone at https://github.com/MdOchiuddinMiah/ODL-BCI.

neuroscience↗

bioRxiv2021

CluSem: Accurate Clustering-based Ensemble Methodto Predict Motor Imagery Tasks from Multi-channelEEG Data

BackgroundThe classification of motor imagery electroencephalogram (MI-EEG) is a pivotal task in the biosignal classification process in the brain-computer interface (BCI) applications. Currently, this bio-engineering-based technology is being employed by researchers in various fields to develop cuttingedge applications. The classification of real-time MI-EEG signals is the most challenging task in these applications. The prediction performance of the existing classification methods is still limited due to the high dimensionality and dynamic behaviors of the real-time EEG data. Proposed MethodTo enhance the classification performance of real-time BCI applications, this paper presents a new clustering-based ensemble technique called CluSem to mitigate this problem. We also develop a new brain game called CluGame using this method to evaluate the classification performance of real-time motor imagery movements. In this game, real-time EEG signal classification and prediction tabulation through animated balls are controlled via threads. By playing this game, users can control the movements of the balls via the brain signals of motor imagery movements without using any traditional input devices. ResultsOur results demonstrate that CluSem is able to improve the classification accuracy between 5% and 15% compared to the existing methods on our collected as well as the publicly available EEG datasets. The source codes used to implement CluSem and CluGame are publicly available at https://github.com/MdOchiuddinMiah/MI-BCI_ML.

neuroscience↗

bioRxiv2020

Prediction of Motor Imagery Tasks from Multi-Channel EEG Data for Brain-Computer Interface Applications

The classification of motor imagery electroencephalogram (MI-EEG) is a pivotal part of the biosignal classification in the brain-computer interface (BCI) applications. Currently, this bio-engineering based technology is being employed by researchers in various fields to develop cutting edge applications. The classification of real-time MI-EEG signal is the core computing and challenging task in these applications. It is well-known that the existing classification methods are not so accurate due to the high dimensionality and dynamic behaviors of the real-time EEG data. To improve the classification performance of real-time BCI applications, this paper presents a clustering-based ensemble technique and a developed brain game that distinguishes different human thoughts. At first, we have gathered the brain signals, extracted and selected informative features from these signals to generate training and testing sets. After that, we have constructed several classifiers using Artificial Neural Network (ANN), Support Vector Machine (SVM), naive Bayes, Decision Tree (DT), Random Forest, Bagging, AdaBoost and compared the performance of these existing approaches with suggested clustering-based ensemble technique. On average, the proposed ensemble technique improved the classification accuracy of roughly 5 to 15% compared to the existing methods. Finally, we have developed the targeted brain game employing our suggested ensemble technique. In this game, real-time EEG signal classification and prediction tabulation through animated ball are controlled via threads. By playing this game, users can control the movements of the balls via the brain signals of motor imagery movements without using any traditional input devices. All relevant codes are available via open repository at: https://github.com/mrzResearchArena/MI-EEG.

bioengineering↗