Brain–Machine Interface for Developing Virtual-Ball Movement Controlling Game

Miah, Md. Ochiuddin; Hassan, Al Maruf; Mamun, Khondaker A.; Farid, Dewan Md.

doi:10.1007/978-981-13-7564-4_51

Cited by 7 publications

(14 citation statements)

References 13 publications

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“…In future iterations of AR-PaperSync, we plan to harness the capabilities of Machine Learning (ML) and deep learning to enhance the system's functionality and user experience significantly [31,32,33,34]. We will integrate a one-click access feature for open-source cited papers, utilizing ML algorithms for efficient retrieval from extensive academic databases, addressing Participant P9's feedback.…”

Section: Future Workmentioning

confidence: 99%

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

Miah,

Kong

2024

Preprint

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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-PaperSync’s 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.

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Section: Future Workmentioning

confidence: 99%

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

Miah,

Kong

2024

Preprint

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“…3) Bagging: Bagging, or Bootstrap Aggregating, is another ensemble method that crafts numerous base models using random subsets of the dataset [27]. The amalgamation of these models results in better predictions, reducing overfitting [8].…”

Section: A Baseline Modelsmentioning

confidence: 99%

“…The 10-20 system's meticulous electrode placement ensures consistency and standardization by distributing electrodes based on precise percentages of the scalp's left-right and front-back dimensions [26]. This adherence to the 10-20 system simplifies integration with existing EEG research and data analysis methodologies [5].…”

Section: Neurosky Mindsetmentioning

confidence: 99%

“…Classification techniques play a pivotal role in the realm of brain-computer interfaces (BCIs) to analyze electroencephalogram (EEG) data [8]. The application of these techniques aids in classifying different mental states based on the acquired data [16], [27]. In this study, we utilized several baseline models, including Decision Tree, AdaBoost, Bagging, Multilayer Perceptron (MLP), Naïve Bayes, Random Forest, Support Vector Machines (SVM), XG Boost, and a specially designed deep learning model.…”

Section: Classification Techniquesmentioning

confidence: 99%

“…The evolution of BCI technology has unlocked groundbreaking pathways in cognitive neuroscience, especially in understanding and interpreting EEG data [1,2,3]. EEG, a representation of the brain's electrical activity, offers invaluable insights into cognitive states and has diverse applications, ranging from medical diagnostics to enhancing learning experiences in education [4,5,6].…”

Section: Introductionmentioning

confidence: 99%

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ODL-BCI: Optimal deep learning model for brain-computer interface to classify students confusion via hyperparameter tuning

Miah,

Habiba,

Kabir

2023

Preprint

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Brain-computer interface (BCI) research has gained attention in education contexts, offering the potential to monitor and enhance student’s cognitive states and this study focuses on developing an optimal deep learning model, ODL-BCI, for real-time classification of students’ concentration levels. The model incorporates hyper-parameter tuning techniques and utilizes the publicly available “Confused student EEG brainwave data” dataset. We propose a deep learning model with hyperparameters optimized through Bayesian optimization. The model’s architecture is constructed with an input layer, several hidden layers, and an output layer. The number of nodes and activation functions in the hidden layers is determined using Bayesian optimization. The learning rate is also optimized for each layer. The proposed model is evaluated and compared with several standard machine learning classifiers, including Decision Tree, AdaBoost, Bagging, MLP, Naïve Bayes, Random Forest, SVM, and XG Boost, on an EEG confusion dataset. Experimental results demonstrate that the optimized deep learning model outperforms all other classifiers, achieving an accuracy of 74 percent. The model’s effectiveness in accurately classifying students’ concentration levels highlights its potential as a valuable tool in educational settings. This research contributes to the advancement of BCI technology, providing insights into the optimization of deep learning models for EEG-based cognitive assessment. Future work involves exploring the model’s generalizability on larger datasets and extending its applicability to other BCI applications.

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CluSem: Accurate clustering-based ensemble method to predict motor imagery tasks from multi-channel EEG data

Miah

Muhammod

Mamun

et al. 2021

Journal of Neuroscience Methods

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Brain–Machine Interface for Developing Virtual-Ball Movement Controlling Game

Cited by 7 publications

References 13 publications

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

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

ODL-BCI: Optimal deep learning model for brain-computer interface to classify students confusion via hyperparameter tuning

CluSem: Accurate clustering-based ensemble method to predict motor imagery tasks from multi-channel EEG data

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