G
Ganesh R. Naik
Researcher at University of Sydney
Publications - 170
Citations - 4131
Ganesh R. Naik is an academic researcher from University of Sydney. The author has contributed to research in topics: Independent component analysis & Blind signal separation. The author has an hindex of 30, co-authored 160 publications receiving 2940 citations. Previous affiliations of Ganesh R. Naik include RMIT University & University of Technology, Sydney.
Papers
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Journal ArticleDOI
Driver Fatigue Classification With Independent Component by Entropy Rate Bound Minimization Analysis in an EEG-Based System
TL;DR: The results of this study suggest the method could be utilized effectively for a countermeasure device for driver fatigue identification and other adverse event applications.
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Real-Time EMG Based Pattern Recognition Control for Hand Prostheses: A Review on Existing Methods, Challenges and Future Implementation
Nawadita Parajuli,Neethu Sreenivasan,Paolo Bifulco,Mario Cesarelli,Sergio Savino,Vincenzo Niola,Daniele Esposito,Tara Julia Hamilton,Ganesh R. Naik,Upul Gunawardana,Gaetano D. Gargiulo,Gaetano D. Gargiulo +11 more
TL;DR: This review paper examined the suitability of upper limb prosthesis (ULP) inventions in the healthcare sector from their technical control perspective and the overall structure of pattern recognition schemes for myo-control prosthetic systems and their real-time use on amputee upper limbs.
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An Overview of Independent Component Analysis and Its Applications
Ganesh R. Naik,Dinesh Kumar +1 more
TL;DR: This paper attempts to cover the fundamental concepts involved in ICA techniques and review its applications to serve as a comprehensive single source for an inquisitive researcher to carry out research in this field.
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Transradial Amputee Gesture Classification Using an Optimal Number of sEMG Sensors: An Approach Using ICA Clustering
TL;DR: The proposed method is a model-based approach where a combination of source separation and Icasso clustering was utilized to improve the classification performance of independent finger movements for transradial amputee subjects.
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Twin SVM for Gesture Classification Using the Surface Electromyogram
TL;DR: This paper reports the use of twin support vector machine for gesture classification based on sEMG, and shows that this technique is eminently suited to such applications.