Brain and Behavior Computing
Editat de Mridu Sahu, G R Sinhaen Limba Engleză Paperback – 25 sep 2023
- Emphasizes real challenges in brain signal processing for a variety of applications for analysis, classification, and clustering.
- Discusses data sciences and its applications in brain computing visualization. Covers all the most recent tools for analysing the brain and it’s working.
- Describes brain modeling and all possible machine learning methods and their uses.
- Augments the use of data mining and machine learning to brain computer interface (BCI) devices.
- Includes case studies and actual simulation examples.
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Specificații
ISBN-13: 9780367552992
ISBN-10: 036755299X
Pagini: 428
Ilustrații: 91 Tables, black and white; 131 Line drawings, black and white; 78 Halftones, black and white; 209 Illustrations, black and white
Dimensiuni: 156 x 234 x 22 mm
Greutate: 0.68 kg
Ediția:1
Editura: CRC Press
Colecția CRC Press
ISBN-10: 036755299X
Pagini: 428
Ilustrații: 91 Tables, black and white; 131 Line drawings, black and white; 78 Halftones, black and white; 209 Illustrations, black and white
Dimensiuni: 156 x 234 x 22 mm
Greutate: 0.68 kg
Ediția:1
Editura: CRC Press
Colecția CRC Press
Cuprins
1. Simulation Tools for Brain Signal Analysis
2. Processing Techniques and Analysis of Brain Sensor Data Using Electroencephalography
3. Application of Machine-Learning Techniques in Electroencephalography Signals
4. Revolution of Brain Computer Interface: An Introduction
5. Signal Modeling Using Spatial Filtering and Matching Wavelet Feature Extraction for Classification of Brain Activity Pattern
6. Study and Analysis of the Visual P300 Speller on Neurotypical Subjects
7. Effective Brain Computer Interface Based on the Adaptive-Rate Processing and Classification of Motor Imagery Tasks
8. EEG-Based BCI Systems for Neurorehabilitation Applications
9. Scalp EEG Classification Using TQWT-Entropy Features for Epileptic Seizure Detection
10. An Efficient Single-Trial Classification Approach for Devanagari Script-Based Visual P300 Speller Using Knowledge Distillation and Transfer Learning
11. Deep Learning Algorithms for Brain Image Analysis
12. Evolutionary Optimization Based Two Dimensional Elliptical FIR Filters for Skull Stripping in Brain Imaging and Disorder Detection
13. EEG-Based Neurofeedback Game for Focus Level Enhancement
14. Detecting K-Complexes in Brain Signals Using WSST2-DETOKS
15. Directed Functional Brain Networks: Characterization of Information Flow Direction during Cognitive Function Using Non-Linear Granger Causality
16. Student Behavior Modeling and Context Acquisition: A Ubiquitous Learning Framework
2. Processing Techniques and Analysis of Brain Sensor Data Using Electroencephalography
3. Application of Machine-Learning Techniques in Electroencephalography Signals
4. Revolution of Brain Computer Interface: An Introduction
5. Signal Modeling Using Spatial Filtering and Matching Wavelet Feature Extraction for Classification of Brain Activity Pattern
6. Study and Analysis of the Visual P300 Speller on Neurotypical Subjects
7. Effective Brain Computer Interface Based on the Adaptive-Rate Processing and Classification of Motor Imagery Tasks
8. EEG-Based BCI Systems for Neurorehabilitation Applications
9. Scalp EEG Classification Using TQWT-Entropy Features for Epileptic Seizure Detection
10. An Efficient Single-Trial Classification Approach for Devanagari Script-Based Visual P300 Speller Using Knowledge Distillation and Transfer Learning
11. Deep Learning Algorithms for Brain Image Analysis
12. Evolutionary Optimization Based Two Dimensional Elliptical FIR Filters for Skull Stripping in Brain Imaging and Disorder Detection
13. EEG-Based Neurofeedback Game for Focus Level Enhancement
14. Detecting K-Complexes in Brain Signals Using WSST2-DETOKS
15. Directed Functional Brain Networks: Characterization of Information Flow Direction during Cognitive Function Using Non-Linear Granger Causality
16. Student Behavior Modeling and Context Acquisition: A Ubiquitous Learning Framework
Descriere
This book provides emphasis on brain and behaviour computing with different modalities available such as signal and image processing, data science, statistics, distributed computing including basics, model, algorithms, case studies, and research scope. It further explains brain signals sources, signal processing, manipulation and transformations.