Basabdatta Sen Bhattacharya

Papers

Total Citations

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About

Basabdatta Sen Bhattacharya is a computational neuroscientist whose research bridges neural modeling, machine learning, and biomedical signal processing. Her key contributions lie in developing adaptive machine learning algorithms for electroencephalography (EEG) analysis, particularly for motor imagery classification in brain-computer interfaces. Her most cited work, "Adaptive Parameterized AdaBoost Algorithm with application in EEG Motor Imagery Classification" (2015, 5 citations), introduces an innovative modification to the Real AdaBoost algorithm that addresses the critical issue of misclassifying already correctly classified samples. By incorporating adaptive parameterization, her method enhances classification accuracy and robustness, offering a more reliable tool for decoding neural signals. This work exemplifies her broader focus on improving computational models for neurological data interpretation. Bhattacharya’s research has implications for assistive technologies and clinical diagnostics, where precise EEG classification is vital. Though her citation count is modest, her contributions are foundational in refining machine learning approaches for complex, noisy biomedical datasets. Her interdisciplinary approach—combining computational theory with real-world neural applications—positions her as a thoughtful contributor to the evolving field of neural engineering and adaptive learning systems.