Bipul Thapa

Biplov Paneru, Bipul Thapa, Bishwash Paneru et al.

Brain-machine interfaces (BMIs), particularly those based on electroencephalography (EEG), offer promising solutions for assisting individuals with motor disabilities. However, challenges in reliably interpreting EEG signals for specific tasks, such as simulating keystrokes, persist due to the complexity and variability of brain activity. Current EEG-based BMIs face limitations in adaptability, usability, and robustness, especially in applications like virtual keyboards, as traditional machine-learning models struggle to handle high-dimensional EEG data effectively. To address these gaps, we developed an EEG-based BMI system capable of accurately identifying voluntary keystrokes, specifically leveraging right and left voluntary hand movements. Using a publicly available EEG dataset, the signals were pre-processed with band-pass filtering, segmented into 22-electrode arrays, and refined into event-related potential (ERP) windows, resulting in a 19x200 feature array categorized into three classes: resting state (0), 'd' key press (1), and 'l' key press (2). Our approach employs a hybrid neural network architecture with BiGRU-Attention as the proposed model for interpreting EEG signals, achieving superior test accuracy of 90% and a mean accuracy of 91% in 10-fold stratified cross-validation. This performance outperforms traditional ML methods like Support Vector Machines (SVMs) and Naive Bayes, as well as advanced architectures such as Transformers, CNN-Transformer hybrids, and EEGNet. Finally, the BiGRU-Attention model is integrated into a real-time graphical user interface (GUI) to simulate and predict keystrokes from brain activity. Our work demonstrates how deep learning can advance EEG-based BMI systems by addressing the challenges of signal interpretation and classification.

Biplov Paneru, Bishwash Paneru, Bipul Thapa et al.

This study offers a revolutionary strategy to developing wheelchairs based on the Brain-Computer Interface (BCI) that incorporates Artificial Intelligence (AI) using a The device uses electroencephalogram (EEG) data to mimic wheelchair navigation. Five different models were trained on a pre-filtered dataset that was divided into fixed-length windows using a sliding window technique. Each window contained statistical measurements, FFT coefficients for different frequency bands, and a label identifying the activity carried out during that window that was taken from an open-source Kaggle repository. The XGBoost model outperformed the other models, CatBoost, GRU, SVC, and XGBoost, with an accuracy of 60%. The CatBoost model with a major difference between training and testing accuracy shows overfitting, and similarly, the best-performing model, with SVC, was implemented in a tkinter GUI. The wheelchair movement could be simulated in various directions, and a Raspberry Pi-powered wheelchair system for brain-computer interface is proposed here.

Biplov Paneru, Bipul Thapa, Durga Prasad Mainali et al.

Accurately estimating the Remaining Useful Life (RUL) of a battery is essential for determining its lifespan and recharge requirements. In this work, we develop machine learning-based models to predict and classify battery RUL. We introduce a two-level ensemble learning (TLE) framework and a CNN+MLP hybrid model for RUL prediction, comparing their performance against traditional, deep, and hybrid machine learning models. Our analysis evaluates various models for both prediction and classification while incorporating interpretability through SHAP. The proposed TLE model consistently outperforms baseline models in RMSE, MAE, and R squared error, demonstrating its superior predictive capabilities. Additionally, the XGBoost classifier achieves an impressive 99% classification accuracy, validated through cross-validation techniques. The models effectively predict relay-based charging triggers, enabling automated and energy-efficient charging processes. This automation reduces energy consumption and enhances battery performance by optimizing charging cycles. SHAP interpretability analysis highlights the cycle index and charging parameters as the most critical factors influencing RUL. To improve accessibility, we developed a Tkinter-based GUI that allows users to input new data and predict RUL in real time. This practical solution supports sustainable battery management by enabling data-driven decisions about battery usage and maintenance, contributing to energy-efficient and innovative battery life prediction.

Sansrit Paudel, Subek Acharya, Piriyankan Kirupaharan et al.

Dyslexia is a neurological learning disability that primarily disrupts one's ability to read, write, and spell, affecting an estimated 15-20% of the global population. This high prevalence underscores the importance of developing effective interventions. This study presents a systematic literature review conducted between 2015 and 2024 to evaluate current trends in assistive technologies for children with dyslexia. This research shows that digital assistive technologies are leading interventions, especially with the use of mobile apps and augmented reality. More innovative technologies like virtual reality, NLP, haptic technologies, and tangible user interfaces are emerging to provide unique solutions addressing the user's needs. While non-computing devices are generally less effective in comparison to modern digital solutions, they provide a promising alternative in settings with limited access to technology.

Bipul Thapa, Biplov Paneru, Bishwash Paneru et al.

This paper presents an Artificial Intelligence (AI) integrated novel approach to Brain-Computer Interface (BCI)-based wheelchair development, utilizing a motor imagery right-left-hand movement mechanism for control. The system is designed to simulate wheelchair navigation based on motor imagery right and left-hand movements using electroencephalogram (EEG) data. A pre-filtered dataset, obtained from an open-source EEG repository, was segmented into arrays of 19x200 to capture the onset of hand movements. The data was acquired at a sampling frequency of 200Hz. The system integrates a Tkinter-based interface for simulating wheelchair movements, offering users a functional and intuitive control system. We propose a BiLSTM-BiGRU model that shows a superior test accuracy of 92.26% as compared with various machine learning baseline models, including XGBoost, EEGNet, and a transformer-based model. The Bi-LSTM-BiGRU attention-based model achieved a mean accuracy of 90.13% through cross-validation, showcasing the potential of attention mechanisms in BCI applications.