Brain Computer Interface (BCI) is one of the prominent and challenging research area in the field of Science , Engineering and Technology. My primary research is on developing Electroencephalography (EEE) patterns for epileptic patients using machine learning techniques. Identifying new feature and combination of feature is an innovative approach to produce high accuracy rate in classification problems.
UGC-BSR Fellowship under “Research Fellowship in Science for Meritorious Students” scheme
1.Total Variation Based Multi Feature Model forEpilepsy Detection using Support Vector Machine. IETE Journal of Research, 62(6), 822-832. ISSN: 0377-2063. (ISI THOMSON REUTERS)
2.Electromyography Based Detection of Neuropathy Disorder Using Reduced Cepstral Feature. Indian Journal of Science and Technology, 9(8), 878991-87902. ISSN: 0974 -5645. (ISI THOMSON REUTERS)
3.Influence of Linear Features in Nonlinear Electroencephalography (EEG) Signals. Procedia Computer Science, Elseveir 47 (C), 229-236. ISSN: 1877-0509. (SCOPUS)
4.A Note on Methods Used for Deception Analysis and Influence of Thinking Stimulus in Deception Detection. International Journal of Engineering and Technology (IJET), 7 (1), 109-116. ISSN: 0975-4024 (SCOPUS)
5.Classification of Electromyography Signals using Wavelet Decomposition Method. IEEE Explore, 1326-1329. ISBN: 978-1-4799-3974-9 (SCOPUS)
6.Lie Detection based on Teeth Bite Neural Response using SVM and Neural Networks. International Journal of AppliedEngineering Research, 9(20), 4686-4690. ISSN: 0973-9769 (SCOPUS)
7.Sample Entropy based Epilepsy Detection using SVM. Elseveir book chapter. ISBN: 9789351071942.
8.Feature Extraction Methods used in EEG Signal Analysis. IJRCSIT , 2 (A), 12-19. ISSN: 2319-5010.
9.Characterization of PCA for EEG eyeartifact Detection. International Journal of Business Intelligence, 3 (1), 351-354. ISSN:2278 – 2400.
10.Support Vector Machine Technique for EEG Signal. International Journal of Computer Applications, 63 (13), 1-5.ISSN: 0975 -8887. Cited: 48
11.Analysis of Electroencephalography (EEG) Signals and its Categorization – A Study. Procedia Engineering, Elsevier , 38,2525-2536. ISSN: 1877-7058 (SCOPUS)
12.Methods Used for Identifying EEG Signal Artifacts. Elseveir , 375-379.
13.Role of Principal Component Analysis in Electroencephalography Signal Analysis. ICCT, (pp. 989-919). Manila.
14.A Comprehensive Survey on EEG Signal Classification Algorithms. ICDMSCT. Sastra University.
15.A Note on Electroencephalography (EEG) and its Applications on Human Brain. MMASC, (pp. 942-953).
16.A note on Brain Image Analysis.NCRTACT, (pp. 200-202).
17.Decisive Cluster Evaluation of Institutional Quality in Education Systems. National Conference on Systemic, Cybernetics & Informatics.
18.Facial Recognition using image Processing Techniques. ISTE Staff Chapter.
19. 4G Wireless Networks. National Conference on Recent Trends in Computing Technologies.
20.Distributed Image Server using High Speed Networks. ISTE Staff Chapter.