[ Research ]
Brain–computer interfacing, neural signal processing and machine learning applied to EEG analysis and beyond.
Designing EEG-based brain–computer interfaces that translate neural activity into commands — from motor imagery classification and SSVEP frequency recognition to neurorobotics for assisting people with disabilities.
Processing and cleaning electroencephalography (EEG) signals — artifact and noise suppression, time-frequency analysis and coherence estimation using wavelet and synchrosqueezing transforms.
Applying machine learning and deep-learning models to brain-signal classification, health prediction and intelligent systems, including explainable and data-driven approaches.
Digital filter design and performance analysis, image edge detection, segmentation and binarization techniques for real-world signal and image processing problems.
An ongoing university-funded project developing a biometric-based system for class attendance management and secure examinee identification.
Funded by: University Funded — Research & Extension Cell, JKKNIU
View ProjectAnalysis of electroencephalography (EEG) signals recorded in response to audio stimuli, supporting the development of brain-computer interface applications.
Funded by: ICT Division, Ministry of Posts, Telecommunications and Information Technology, Government of Bangladesh
View Project