A5087474428- EEG and Brain-Computer Interfaces
- Cardiac Imaging and Diagnostics
- Advanced MRI Techniques and Applications
- Neural Networks and Applications
- Neuroscience and Neural Engineering
- Advanced X-ray and CT Imaging
- Medical Imaging Techniques and Applications
- ECG Monitoring and Analysis
- Stroke Rehabilitation and Recovery
- Nanoparticles: synthesis and applications
- Gaze Tracking and Assistive Technology
- AI and HR Technologies
- Neural dynamics and brain function
- Antimicrobial agents and applications
- Blind Source Separation Techniques
- Nanocomposite Films for Food Packaging
- Image and Signal Denoising Methods
Dublin City University
2024
Aligarh Muslim University
2018-2022
Cornell University
2005
In this paper, different machine learning algorithms such as Linear Discriminant Analysis, Support vector (SVM), Multi-layer perceptron, Random forest, K-nearest neighbour, and Autoencoder with SVM have been compared. This comparison was conducted to seek a robust method that would produce good classification accuracy. To end, of classifying raw Electroencephalography (EEG) signals associated imagined movement the right hand relaxation state, namely has proposed. The EEG dataset used in...
Cine cardiac magnetic resonance (CMR) imaging is recognised as the benchmark modality for comprehensive assessment of function. Nevertheless, acquisition process cine CMR considered an impediment due to its prolonged scanning time. One commonly used strategy expedite through k-space undersampling, though it comes with a drawback introducing aliasing effects in reconstructed image. Lately, deep learning-based methods have shown remarkable results over traditional approaches rapidly achieving...
In this paper, we propose a system for the purpose of classifying Electroencephalography (EEG) signals associated with imagined movement right hand and relaxation state using machine learning algorithm namely Random Forest Algorithm. The EEG dataset used in research was created by University Tubingen, Germany. were processed wavelet transform analysis Daubechies orthogonal as mother wavelet. After analysis, eight features extracted. Subsequently, feature selection method based on Algorithm...
Brain-Computer Interface has gained significant attention from the research community as it provides a potential avenue to aid people with motor disabilities. However, major challenge in this is accurately decode EEG signals control an external device. This study used data participant spinal cord injury map them into different classes that can be commands. Further, deep transfer learning model implemented classify imagery for neuro-prosthetic Finally, proposed method achieves best accuracy of 100%.