K. Yasoda
Papers
1
Total Citations
83
H-Index
1
About
K. Yasoda is a researcher whose work lies at the intersection of biomedical signal processing and machine learning, with a particular focus on electroencephalogram (EEG) artifact detection and classification. Their most-cited paper, "Automatic detection and classification of EEG artifacts using fuzzy kernel SVM and wavelet ICA (WICA)" (2020), has garnered 83 citations, reflecting a significant contribution to the field of neural signal analysis. This work proposed a hybrid framework combining wavelet-based independent component analysis (WICA) with a fuzzy kernel support vector machine, offering a robust method for automatically identifying and categorizing artifacts in EEG data—a critical step for improving the reliability of brain-computer interfaces and clinical diagnostics. Despite a subsequent retraction, the paper's citation count underscores its influence and the ongoing relevance of its methodological innovations. Yasoda's research demonstrates a commitment to advancing automated, intelligent systems for biomedical applications, bridging the gap between theoretical machine learning and practical neurotechnology. Their work continues to inspire further exploration into artifact removal and classification techniques, making them a notable figure in the intersection of computational intelligence and neuroscience.
Research Focus
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Top Papers
- 1