This book discusses the basic requirements and constraints in building a braincomputer interaction system. These include the technical requirements for building the signal processing module and the acquisition module. The major aspects to be considered when designing a signal acquisition module for a braincomputer interaction system are the human brain, types and applications of braincomputer systems, and the basics of EEG (electroencephalogram) recording. The book also compares the algorithms that have been and that can be used to design the signal processing module of braincomputer interfaces, and describes the various EEG-acquisition devices available and compares their features and inadequacies. Further, it examines in detail the use of Emotiv EPOC (an EEG acquisition module developed by Emotiv) to build a complete braincomputer interaction system for driving robots using a neural network classification module.

Includes a detailed discussion of all relevant topics to help readers learn quickly Describes two successfully integrated robotic systems for braincomputer interface problem solving Compares algorithms for designing the signal processing module of braincomputer interfaces

Autorentext

Swagata Das is pursuing her PhD in Biological Systems Engineering at Hiroshima University, Japan, having completed her M.Tech. in Electronics Design and Technology, for which she received a gold medal, at Tezpur Central University, Assam, India. She was also awarded gold medals by His Excellency Late Dr. A. P. J. Abdul Kalam, Ex-President of India and Shri. Kapil Sibal, ex-Union Minister of India for her exceptional performance in academics at NERIST, Itanagar, India. Her research interests include electronics applications of mathematical modeling and brain-computer interaction. She has published her research work in Springer and IEEE proceedings. Devashree Tripathi completed her M.Tech. in Advanced Electronics Systems at CSIR-Central Electronics Engineering Research Institute. Currently she is a PhD candidate at the Department of Computer Science & Engineering at the University of California, Riverside and also a member of the SoCal Lab atUCR. Her research interests include computer architecture, GPGPU architecture design, high-performance computing, and fault-tolerance systems. She has worked on multiple projects on data-dependent applications on GPGPU, low power design of GPGPU execution units and has achieved notable improvements in terms of performance gain and power and area saving. She has published papers in respected journals and conferences. Jagdish Lal Raheja is a Senior Principal Scientist and Professor at the CSIR-Central Electronics Engineering Research Institute (CSIR-CEERI). Previously, he was also a Visiting Scientist at the University of Maribor, Slovenia and a Guest Scientist at the Technical University Dortmund, Germany, University College London, and the Fraunhofer-Institutszentrum Schloss Birlinghoven. His areas of specialization are image processing, pattern recognition, perception engineering, digital cartographic generalization and artificial intelligence, and he hascompleted numerous research projects in these areas. He has published in various high impact journals and conference proceedings. He is also an editorial board member and reviewer for several journals and conferences.

Inhalt
Preface.- Introduction.- An insight to the human brain and EEG.- A review on algorithms for EEG-based BCIs30.- Implications.- Results and Conclusions.