Ganesh Naik received his BE in Electronics and Communication Engineering from the University of Mysore, India, in 1997, his ME in Communication and Information Engineering from Griffith University, Brisbane, Australia, in 2002, and his PhD in Electronics Engineering, specializing in Biomedical Engineering and Signal Processing, from RMIT University, Melbourne, Australia, in 2009.

He is currently an academic and postdoctoral research fellow at MARCS Institute, Western Sydney University. Prior to that he held a Chancellor's Post-Doctoral Research Fellowship position at the Faculty of Engineering and Information Technology (FEIT), UTS. To date he has edited 11 books, and authored more than 100 book chapters and articles in peer-reviewed journals and conference proceedings. His research interests include EMG signal processing, pattern recognition, Blind Source Separation (BSS) techniques, biomedical signal processing, Human-Computer Interface (HCI) and audio signal processing.

At present, he is serving as an associate editor of IEEE ACCESS and two Springer journals. He is also a reviewer and member of the editorial board for several reputed journals. He was a recipient of the Baden-Württemberg Scholarship from the University of Berufsakademie, Stuttgart, Germany (2006-2007). In 2010, Dr Naik was awarded an ISSI overseas fellowship by the Skilled Institute in Victoria, Australia.

Covers cutting-edge topics in biomedical signal analysis, highlighting open problems concerning clinical applications

Presents both theory and applications with examples

Offers an in-depth analysis of biomedical signal processing topics

Discusses cutting-edge aspects of biomedical signals and provides comprehensive information on the core principles of various biomedical signal applications

Surface Electromyography (EMG) Signal Processing, Classification, and Practical Considerations.- Estimation of ankle joint torque and angle based on S-EMG signal for assistive rehabilitation robots.- Force Myography and its Application to Human Locomotion.- Comparison of Independence of Triceps Brachii and Biceps Brachii between Paretic and Non-Paretic side during different MVCs-A case study.- An EEG brain-computer interface to classify motor imagery signals.