Niraj Bagh

About

My research field is Motor Imagery-based Brain-Computer Interface. Motor Imagery-based Brain-Computer Interface is a communication device that deciphers the motor intension or imagination of the paralyzed patients into a control signal to interact with the surrounding environment. My research aim is to design a low-cost MI-based BCI system for paralyzed patients. So, basically the research objective is to decode and investigate the brain patterns of the patients while imagining certain movements like grasping hands, legs, up or down of tongue movements, etc. I am highly expertise in Signal and Image Processing, Biomedical Instrumentation, Time-series Analysis, Probability Theory, Circuit Theory, Electromagnetic Wave Theory, Satellite and Mobile Communications, Cognitive Science, NeuroScience, Data Science, Statistic, Machine Learning, Deep Learning, MATLAB, C/C++, R-studio, Python, TensorFlow, Keras, Pytorch, Panda, Numpy, etc.

Employment

• Indian Institute of Technology Madras Research Scholar
  2015 - Present

Education

• Indian Institute of Technology Madras Research Scholar
  2015 - Present

Projects & Funding

Projects & funding information is unavailable.

Publications (6)

• Hilbert transform-based event-related patterns for motor imagery brain computer interface
  Biomedical Signal Processing and Control 2020 DOI: 10.1016/j.bspc.2020.102020
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• Classification of Motor Imagery Activities Using Band Power and Second Order Difference Plot
  2019 IEEE Bombay Section Signature Conference (IBSSC) 2019 DOI: 10.1109/ibssc47189.2019.8973077
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• Classification of Motor Imagery Tasks using Phase Space Reconstruction and Empirical Mode Decomposition
  2019 IEEE Canadian Conference of Electrical and Computer Engineering (CCECE) 2019 DOI: 10.1109/ccece.2019.8861732
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• Detection of Motor Imagery Movements Based on the Features of Phase Space Reconstruction
  International IEEE/EMBS Conference on Neural Engineering, NER 2019 DOI: 10.1109/NER.2019.8717113
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• Classification of Motor Imagery Tasks Using Inter Trial Variance in the Brain Computer Interface
  MeMeA 2018 - 2018 IEEE International Symposium on Medical Measurements and Applications, Proceedings 2018 DOI: 10.1109/MeMeA.2018.8438648
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• Automated Neural Network Based Classification of HRV and ECG Signals of Smokers: A Preliminary Study
  Advancements of Medical Electronics 2015 DOI: 10.1007/978-81-322-2256-9_25
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