Kumar Avinash Chandra is an Assistant Professor and researcher at WIT Darbhanga, specializing in Renewable Energy, Machine Learning, and EEG analysis.
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Degree |
Institution |
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Ph.D. (Pursuing) |
Birla Institute of Technology (BIT), Mesra |
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M.Tech |
IET Bhaddal (PTU, Jalandhar) |
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B.Tech |
KIIT University, Bhubaneswar |
Kumar Avinash Chandra is a distinguished Indian academic and researcher specializing in the fields of Electrical and Electronics Engineering. He currently serves as an Assistant Professor at the Women’s Institute of Technology (WIT) in Darbhanga, Bihar.
His academic journey is marked by a strong foundation in technical education, holding a B.Tech from KIIT University and an M.Tech from IET Bhaddal. He is further advancing his expertise as a Research Scholar at the Birla Institute of Technology (BIT), Mesra, focusing on cutting-edge doctoral research.
Brain-Computer Interfaces (BCI): He is a pioneer in developing Quantum-enhanced EEG classifiers designed for high-precision, real-time control of assistive devices, such as brain-controlled wheelchairs for individuals with motor-neuron diseases.
Neurological Diagnostics: Using Machine Learning and Deep Learning (like 2D CNNs), his work achieves over 92% accuracy in identifying biomarkers for epilepsy, Alzheimer’s, and ischemic stroke, facilitating earlier and more reliable medical intervention.
Neuro-Physiotherapy: A standout area of his research is the study of Applied Pressure Physiotherapy (APP). He has proven that specific acupressure points (like Lu10) significantly modulate brain waves, increasing Alpha and Beta power to enhance relaxation and cognitive alertness.
Signal Precision: He utilizes advanced Wavelet Transforms and Hjorth Parameters to strip noise from neural signals, ensuring that human-machine synergy is both seamless and computationally efficient.
Brain-Controlled Mobility: He is advancing Brain-Computer Interfaces (BCI) by developing Quantum-enhanced EEG classifiers. This technology facilitates high-precision, real-time navigation for wheelchairs, offering life-changing independence to individuals with severe motor disabilities.
Precision Diagnostics: Leveraging Deep Learning and Wavelet Transforms, his research achieves near 99% accuracy in detecting biomarkers for epilepsy, Alzheimer’s, and neurodevelopmental disorders. By stripping "noise" from neural signals, he enables earlier and more reliable clinical interventions.
Neuro-Modulation: A unique facet of his work explores Applied Pressure Physiotherapy (APP). His studies demonstrate that specific physical stimuli can modulate brain activity—specifically increasing Alpha power (relaxation) and Beta power (alertness)—validating non-invasive methods for stress reduction and cognitive enhancement.
Quantum enhanced EEG classifier towards brain-controlled wheelchair navigation
Wavelet-Based EEG classification of neural responses to applied pressure physiotherapy
Acupressure at LI4 and LU10 as a modulator of cortical microstates and autonomic tone: a hypothetical mechanism
Effective Brain Controlled Interface for Wheelchair: An Organized Review
Classification of Electroencephalography for Neurobiological Spectrum Disorder Diagnosi