Our research bridges neural engineering, signal processing, and artificial intelligence to address fundamental and translational challenges in biomedical science.
The Bio-Neural Intelligence and Research Advancement Laboratory stands as a captivating and pioneering research lab. Groundbreaking research is undertaken here, focusing on a diverse array of biomedical challenges — encompassing Neuroengineering, Signal and Image Processing for Medical Applications, Simulation, Bio-photonics, and Artificial Intelligence.
Comprised of dedicated and passionate members, the lab is driven by a shared commitment to unraveling biomedical enigmas. Their collective efforts have yielded noteworthy research manuscripts published in prestigious journals and presented at international conferences. BNIRA Lab eagerly seeks to foster collaborative research endeavors with fellow enthusiasts and devoted researchers to drive transformative advancements in biomedical research.
EEG · ML/DL Pipelines · Noisy Signal Handling · Population-Specific Models
We develop machine learning and deep learning pipelines for epilepsy detection from noisy EEG signals. Research includes population-specific model design, stacking classifier approaches, and robust feature extraction strategies that handle real-world signal degradation.
ECG · Arrhythmia Detection · Ventricular Fibrillation · CNNs
AI-based ECG interpretation for arrhythmia identification and ventricular fibrillation detection. We employ scalogram-based convolutional neural networks and transfer learning to achieve high-accuracy cardiac classification from single- and multi-lead ECG recordings.
PCF-based SPR Sensors · Numerical Simulation · Cancer Biomarkers
Design and numerical analysis of photonic crystal fiber (PCF)-based surface plasmon resonance (SPR) sensors for non-invasive cancer detection. Current work covers detection of blood, breast, and adrenal gland cancers through refractive index sensitivity modelling.
Deep Learning · Glioma Segmentation · Disease Classification
Deep learning models for medical image analysis, including glioma segmentation and multi-class disease classification. Work spans convolutional and transformer architectures applied to MRI and histopathology images, targeting clinical decision support.
EMG Prosthetics · Diabetes Assistance · Phototherapeutic Systems
Development of EMG-based prosthetic control systems, multimodal assistive devices for diabetes management, and smart phototherapeutic incubator systems. Research focuses on translating signal processing and AI into functional, patient-centred assistive technology.
VR Therapy · Convergence Insufficiency · Optometric AI
Application of virtual reality-based therapies for vision rehabilitation, with a focus on convergence insufficiency treatment. Research investigates the efficacy of immersive VR environments as a clinical alternative to traditional pencil push-up therapy.