The Berlin Brain Computer Interface 1st editon by Benjamin Blankertz, Michael Tangermann, Florin Popescu, Matthias Krauledat, Siamac Fazli, Marton Donaczy, Gabriel Curio, Klaus Robert Muller ISBN 3540688587 9783540688587

The Berlin Brain-Computer Interface 1st editon by Benjamin Blankertz, Michael Tangermann, Florin Popescu, Matthias Krauledat, Siamac Fazli, Márton Dónaczy, Gabriel Curio, Klaus-Robert Müller – Ebook PDF Instant Download/Delivery. 3540688587, 978-3540688587
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Product details:

ISBN 10:       3540688587
ISBN 13: 978-3540688587
Author:         Benjamin Blankertz, Michael Tangermann, Florin Popescu, Matthias Krauledat, Siamac Fazli, Márton Dónaczy, Gabriel Curio, Klaus-Robert Müller

The Berlin Brain-Computer Interface (BBCI) uses a machine learning approach to extract subject-specific patterns from high-dimensional EEG-features optimized for revealing the user’s mental state. Classical BCI application are brain actuated tools for patients such as prostheses (see Section 4.1) or mental text entry systems ([2] and see [3,4,5,6] for an overview on BCI). In these applications the BBCI uses natural motor competences of the users and specifically tailored pattern recognition algorithms for detecting the user’s intent. But beyond rehabilitation, there is a wide range of possible applications in which BCI technology is used to monitor other mental states, often even covert ones (see also [7] in the fMRI realm). While this field is still largely unexplored, two examples from our studies are exemplified in Section 4.3 and 4.4.

The Berlin Brain-Computer Interface 1st Table of contents:

Chapter 1: Introduction
1.1 Overview of Fuzzy Data Mining
1.2 Cognitive View in Data Mining
1.3 The Role of Similarity in Data Mining
1.4 Real-World Applications of Fuzzy Data Mining
1.5 Objectives and Scope of the Book
1.6 Structure of the Book

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Chapter 2: Fundamentals of Fuzzy Logic and Data Mining
2.1 Introduction to Fuzzy Logic
2.2 Basic Concepts in Data Mining
2.3 Fuzzy Logic in Data Mining
2.4 Types of Fuzzy Data Mining Techniques
2.5 Fuzzy Set Theory and Its Relevance in Data Mining
2.6 Challenges in Fuzzy Data Mining

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Chapter 3: Cognitive Perspectives in Fuzzy Data Mining
3.1 Cognitive Science and Data Mining
3.2 Cognitive Models and Fuzzy Systems
3.3 Understanding Similarity from a Cognitive Perspective
3.4 Cognitive Decision-Making and Fuzzy Data Mining
3.5 Applications of Cognitive Approaches in Data Mining

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Chapter 4: Similarity Measures in Fuzzy Data Mining
4.1 Defining Similarity in Fuzzy Systems
4.2 Classical Similarity Measures
4.3 Fuzzy Similarity Measures
4.4 Cognitive-Inspired Similarity Measures
4.5 Applications and Challenges of Similarity Measures

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Chapter 5: Fuzzy Clustering and Classification
5.1 Fuzzy Clustering Algorithms
5.2 Fuzzy Classification Techniques
5.3 Evaluating Clustering and Classification with Fuzzy Logic
5.4 Applications of Fuzzy Clustering and Classification
5.5 Challenges in Fuzzy Clustering and Classification

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Chapter 6: Fuzzy Association Rules and Regression
6.1 Introduction to Fuzzy Association Rules
6.2 Mining Fuzzy Association Rules
6.3 Fuzzy Regression Models
6.4 Applications of Fuzzy Association Rules and Regression
6.5 Challenges in Fuzzy Association Rule Mining

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Chapter 7: Real-World Applications of Fuzzy Data Mining
7.1 Healthcare and Medical Data Analysis
7.2 Financial and Risk Analysis
7.3 Social Networks and Web Mining
7.4 Industrial and Manufacturing Systems
7.5 Environmental and Ecological Data Mining

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Chapter 8: Advanced Topics in Fuzzy Data Mining
8.1 Fuzzy Data Mining in Big Data
8.2 Deep Learning and Fuzzy Systems
8.3 Hybrid Fuzzy Approaches
8.4 Evolutionary Algorithms in Fuzzy Data Mining
8.5 Probabilistic and Stochastic Models in Fuzzy Systems

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Chapter 9: Challenges and Future Directions
9.1 Challenges in Fuzzy Data Mining
9.2 Scalability and Efficiency Issues
9.3 Integrating Cognitive Models with Data Mining
9.4 Future Trends and Emerging Applications
9.5 Research Directions in Fuzzy Data Mining

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Chapter 10: Conclusion
10.1 Summary of Key Findings
10.2 Contributions to Fuzzy Data Mining
10.3 Final Thoughts and Future Outlook

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