Ritankar Das

ORCID: 0000-0003-4397-2275
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About
Contact & Profiles
Research Areas
  • EEG and Brain-Computer Interfaces
  • Neural Networks and Applications
  • Functional Brain Connectivity Studies
  • Child Nutrition and Feeding Issues
  • Blind Source Separation Techniques
  • Autism Spectrum Disorder Research
  • Computability, Logic, AI Algorithms
  • Currency Recognition and Detection
  • Machine Learning in Bioinformatics
  • Behavioral and Psychological Studies
  • Fractal and DNA sequence analysis
  • Neural dynamics and brain function
  • Family and Disability Support Research

Indian Statistical Institute
2018-2019

Maulana Abul Kalam Azad University of Technology, West Bengal
2015-2017

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10.2139/ssrn.4683898 preprint EN 2024-01-01

Epileptogenic brain connectivity networks are altered compared to normal ones. Here, we have investigated the properties of epileptogenic by applying graph theoretical, statistical and machine learning approaches resting state electroencephalography (EEG) recordings obtained from 30 volunteers 51 patients suffering generalized epilepsy. In case epileptic patients, found that behave like random networks. There is some loss in node connectivity. Hub nodes more affected during Hence, show less...

10.1109/bibm.2015.7359791 article EN 2021 IEEE International Conference on Bioinformatics and Biomedicine (BIBM) 2015-11-01

Autism spectrum disorder (ASD) is a neurodevelopmental characterized by social communication difficulties and restricted repetitive behaviors or interests. Applied behavior analysis (ABA) has been shown to significantly improve outcomes for individuals on the autism spectrum. However, challenges regarding access, cost, provider shortages remain obstacles treatment delivery. To this end, parents were trained as parent technicians (pBTs), improving access ABA, empowering provide ABA in their...

10.7759/cureus.62377 article EN Cureus 2024-06-14

Epilepsy is a neurological condition of human being, mostly treated based on the patients’ seizure symptoms, often recorded over multiple visits to health-care facility. The lengthy time-consuming process obtaining recordings creates an obstacle in detecting epileptic patients real time. An signature validated EEG data similar kinds epilepsy cases will haste decision-making clinicians. In this paper, we have identified derived signatures for differentiating from normal individuals. Here...

10.3233/fi-2020-1968 article EN Fundamenta Informaticae 2020-12-18
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