This book highlights a new biomedical signal processing method of extracting a specific underlying signal from possibly noisy multi-channel recordings, and shows that the method is suitable for extracting independent components from the measured electroencephalogram (EEG) signal. The system efficiently extracts memory spindles and is also effective in Alzheimer seizures. Current developments in computer hardware and signal processing have made it possible for EEG signals or “brain waves” to communicate between humans and computers – an area that can be extended for use in this domain.
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1 | (12) |
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1.1 Brain Signals Processing (EEG) |
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1.3 The Necessity for Automated Classification |
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1.4 EEG Artifacts and Their Prevention |
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1.5 EEG Classification Methods (Literature Survey) |
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1.6 Key Problems in EEG Classification Methods |
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1.7 A New Framework for Handling Uncertainty and --- Artefacts in EEG Classification |
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9 | (4) |
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11 | (2) |
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2 Analysis of Electroencephalogram (EEG) Using ANN |
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2.2 Proposed System and Specification |
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2.2.1 Digital Signal Transformation and Denoising |
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2.2.2 Data Hiding and Retrieval |
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2.2.4 Recognition of Brain Signals Using Neural Network |
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2.3.2 Artificial Neural Network |
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2.3.3 Neural Network Software |
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25 | (1) |
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2.7 System Implementation |
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28 | (2) |
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2.7.1 Using Back Propagation Network |
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29 | (1) |
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30 | (3) |
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3 Classification and Analysis of EEG Using SVM and MRE |
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33 | (14) |
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33 | (1) |
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3.2 Resources and Techniques |
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3.2.1 Attainment of EEG Data |
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3.2.2 Fuzzy System as a Pre Classifier |
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3.2.3 Fuzzy Membership Functions |
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3.2.5 Estimation of Risk Level in Fuzzy Outputs |
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3.2.6 Binary Representation of Risk Level Patterns |
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3.2.7 Support Vector Machine as Post Classifier |
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3.3 Support Vector Mechanism for Optimization of Fuzzy Outputs |
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3.3.1 Minimum Relative Entropy (MRE) for Optimization of Fuzzy Outputs |
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3.3.2 Algorithm for MRE Optimization |
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3.4 Result and Discussion |
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4 Intelligent Technique to Identify Epilepsy Captures Using Fuzzy System |
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47 | (14) |
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48 | (4) |
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4.3 Fuzzy C-Means Clustering |
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4.5 Fuzzy Firefly Algorithm |
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4.6 Results and Discussion |
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56 | (4) |
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4.6.2 Motivation and Advantage of Using Fuzzy Logic |
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5 Analysis of EEG to Find Alzheimer's Disease Using Intelligent Techniques |
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5.2 Techniques and Resources |
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63 | (6) |
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5.2.1 Signal Attainment and EEG Database |
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63 | (1) |
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64 | (1) |
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65 | (1) |
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5.2.5 Neural Network Classifier |
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Dr. Sasikumar Gurumurthy is a professor at the Department of Computer Science and Systems engineering at Sree Vidyanikethan Engineering College in Tirupati. His current interests include soft computing and artificial intelligence in biomedical engineering, human and machine interaction and applications of intelligent system techniques, new user interfaces, brain-based interactions, human-centric computing, fuzzy sets and systems, image processing, cloud computing, content-based learning and social network analysis.
Dr Naresh Babu Muppalaneni is an associate professor at the Department of Computer Science and Systems Engineering at Sree Vidhyanikethan Engineering College in Tirupati. He has 10 years of teaching and research experience. He received a research grant from DST under the Young Scientist scheme to work on Identifying single drug multiple targets for diabetes. His research interests are cryptology, computer networks and computational systems biology.
Xiao-Zhi Gao received his B.Sc. and M.Sc. degrees from the Harbin Institute of Technology, China in 1993 and 1996, respectively. He earned a D.Sc. (Tech.) degree from the Helsinki University of Technology, Finland in 1999. He is currently a visiting researcher at the Machine Vision and Pattern Recognition Laboratory, Lappeenranta University of Technology, Finland. He is also a guest professor at Beijing Normal University, Harbin Institute of Technology, and Beijing City University, China. Dr. Gao has published more than 150 technical papers in refereed journals and for international conferences. He is an Associate Editor of the Journal of Intelligent Automation and Soft Computing and an editorial board member of the Journal of Applied Soft Computing, International Journal of Bio-Inspired Computation, and Journal of Hybrid Computing Research. Dr. Gao was the General Chair of the 2005 IEEE Mid-Summer Workshop on Soft Computing in Industrial Applications. His current research interests includeneural networks, fuzzy logic, evolutionary computing, swarm intelligence, and artificial immune systems, together with their applications in industrial electronics.
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