Area of research
Cognitive Neuroscience · Biomedical Engineering
Research interest
Research interests include EEG and Brain-Computer Interfaces, Non-Invasive Vital Sign Monitoring, Optical Imaging and Spectroscopy Techniques, and Gaze Tracking and Assistive Technology.
Evaluating the Effectiveness of Immersive Virtual Reality Rehabilitation Games With Enhanced Visual Training for Hand Motor Function Improvement Using Electromyography: Randomized Controlled Trial
Vacuum-Sealed MEMS Resonators Based on Silicon Migration Sealing and Hydrogen Diffusion
Noninvasive brain–computer interfaces using fNIRS, EEG, and hybrid EEG-fNIRS
Analyzing Classification Performance of fNIRS-BCI for Gait Rehabilitation Using Deep Neural Networks
EEG-fNIRS-based hybrid image construction and classification using CNN-LSTM
Deep Learning-Based Unmanned Aerial Vehicle Control with Hand Gesture and Computer Vision
LASSO Homotopy-Based Sparse Representation Classification for fNIRS-BCI
EMG-based Control of Wheel Chair
EMG Signals Based Gesture Recognition Accuracy Improvement by Machine Learning Classifiers/Algorithms
Unmanned Aerial Vehicle Control by Eye-Tracking using Computer Vision and Machine Learning
Performance Analysis of Machine Learning Algorithms for EMG-based Gestures
Convolutional neural networks ensemble model for neonatal seizure detection
Motor Training Using Mental Workload (MWL) With an Assistive Soft Exoskeleton System: A Functional Near-Infrared Spectroscopy (fNIRS) Study for Brain–Machine Interface (BMI)
Vector Phase Analysis Approach for Sleep Stage Classification: A Functional Near-Infrared Spectroscopy-Based Passive Brain–Computer Interface
EEG Spectral Comparison Between Occipital and Prefrontal Cortices for Early Detection of Driver Drowsiness
Enhanced Accuracy for Multiclass Mental Workload Detection Using Long Short-Term Memory for Brain–Computer Interface
Enhancing classification accuracy of fNIRS-BCI using features acquired from vector-based phase analysis
Enhancing Classification Performance of fNIRS-BCI by Identifying Cortically Active Channels Using the z-Score Method
Cortical Tasks-Based Optimal Filter Selection: An fNIRS Study
A Novel Roll and Pitch Estimation Approach for a Ground Vehicle Stability Improvement Using a Low Cost IMU
Characterizing the Effect of Motion Class Taxonomy on the Performance of Hand Motion Classifiers
Enhanced Drowsiness Detection Using Deep Learning: An fNIRS Study
An Adaptive Multi-Robot Therapy for Improving Joint Attention and Imitation of ASD Children
Enhanced Performance for Multi-Forearm Movement Decoding Using Hybrid IMU–sEMG Interface
Assessment and Classification of Mental Workload in the Prefrontal Cortex (PFC) Using Fixed-Value Modified Beer-Lambert Law
Drowsiness Detection During a Driving Task Using fNIRS
Comparison of artificial neural network and support vector machine classifications for fNIRS-based BCI
Drowsiness detection in dorsolateral-prefrontal cortex using fNIRS for a passive-BCI
Control system design for a prosthetic leg using series damping actuator