A5103223490- EEG and Brain-Computer Interfaces
- Blind Source Separation Techniques
- COVID-19 diagnosis using AI
- Radiology practices and education
- Muscle activation and electromyography studies
- Gaze Tracking and Assistive Technology
- Digital Radiography and Breast Imaging
- Neural Networks and Applications
- Neural dynamics and brain function
Florida International University
2024-2025
Khulna University of Engineering and Technology
2019-2021
Chest radiographs, or chest X-rays (CXRs), are widely used as first-line diagnostic tools for detecting various diseases. However, accurately interpreting CXRs remains challenging, human performance is influenced by individual expertise and other factors, often resulting in delays, high costs, potential misinterpretations. To address these limitations, automated computer-based detection systems offer the to enhance accuracy, reduce enable timely disease identification. This study presents...
Abstract Individuals who are suffering from the most severe of motor disabilities can improve their quality life by controlling and directing mechanical electronic devices. As for Spinal Cord Injured (SCI) patients', attempted hand movements be classified using electroencephalography (EEG). The research aims to develop a hybrid CNN‐LSTM (Convolutional Neural Network—Long Short Term Memory) architecture multichannel EEG signal classification. It is challenging task classify real‐world data...
EEG signal in the time domain with high sampled rate faces difficulties for their noise sensitive properties that lead to erroneous feature extraction. Since extraction is one of most significant steps classification, common spatial pattern (CSP) a widely used approach Conventional CSP may often fail maintain discriminative features between classes. Therefore, frequency (FCSP) proposed by work overcome limitations conventional CSP. We have applied and FCSP method on motor imagery data The...