K. S. Bhuvaneshwari
Papers
1
Total Citations
83
H-Index
1
About
K. S. Bhuvaneshwari is a researcher whose work lies at the intersection of biomedical signal processing and machine learning, with a particular focus on electroencephalogram (EEG) analysis. Her most-cited contribution, "Automatic detection and classification of EEG artifacts using fuzzy kernel SVM and wavelet ICA (WICA)" (2020, 83 citations), proposed a novel hybrid framework that combines wavelet-based independent component analysis with a fuzzy kernel support vector machine to automatically identify and classify artifacts in EEG signals. This work addresses a critical bottleneck in neural data interpretation, offering a more robust and efficient method for cleaning EEG recordings—a prerequisite for reliable brain-computer interfaces and clinical diagnostics. While the paper has since been retracted, its citation count underscores its initial influence and the ongoing relevance of the problem it tackled. Bhuvaneshwari’s research demonstrates a strong commitment to enhancing the accuracy and automation of biomedical signal analysis, contributing to the broader goal of making neural data more actionable for both research and real-world applications.
Research Focus
Key Achievements
Top Papers
- 1