Dr Neelam Sharma

About

In the dynamic domain of dementia diagnosis through EEG signal processing within cognitive paradigms, my research thrusts encompass the fusion of cutting-edge technologies. By integrating Brain-Computer Interface (BCI) technology and harnessing the power of Machine Learning, I aim to craft a cost-effective and sophisticated diagnostic system. This system decodes neural patterns associated with cognitive tasks and incorporates real-time cognitive load detection, Neurofeedback mechanisms, and real-time assessments of the Engagement Index and fatigue index.

Leveraging my extensive expertise in Biomedical Signal Processing, Biomedical Instrumentation, Time-series Analysis, Probability Theory, and more, coupled with a deep understanding of Machine Learning, I am poised to contribute significantly to the early detection of neurocognitive disorders. Proficient in MATLAB, EEGLAB, and Psychtoolbox tools, I aspire to advance the field by seamlessly integrating computational intelligence with neuroscience, thereby pushing the boundaries of dementia diagnosis and enhancing our ability to comprehend and address cognitive impairments.

Employment

Employment history is unavailable.

Education

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Projects & Funding

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Publications (6)

• EEG based dementia diagnosis using multi-class support vector machine with motor speed cognitive test
  Biomedical Signal Processing and Control 2021 DOI: 10.1016/j.bspc.2020.102102
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• Iterative Filtering Decomposition Based Early Dementia Diagnosis Using EEG With Cognitive Tests
  IEEE Transactions on Neural Systems and Rehabilitation Engineering 2020 DOI: 10.1109/tnsre.2020.3007860
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• EEG and Cognitive Biomarkers Based Mild Cognitive Impairment Diagnosis
  IRBM 2019 DOI: 10.1016/j.irbm.2018.11.007
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• Diagnosis of Vascular Cognitive Impairment Using EEG
  Indian Journal of Public Health Research & Development 2017 DOI: 10.5958/0976-5506.2017.00454.5
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• The role of EEG signal processing in detection of neurocognitive disorders
  International Journal of Behavioural and Healthcare Research 2016 DOI: 10.1504/ijbhr.2016.081073
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• An Internet of things approach for motion detection using Raspberry Pi
  Proceedings of 2015 International Conference on Intelligent Computing and Internet of Things 2015 DOI: 10.1109/icaiot.2015.7111554
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