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El. knyga: Deep Learning For Eeg-based Brain-computer Interfaces: Representations, Algorithms And Applications

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  • Formatas: 296 pages
  • Išleidimo metai: 14-Sep-2021
  • Leidėjas: World Scientific Europe Ltd
  • Kalba: eng
  • ISBN-13: 9781786349606
Kitos knygos pagal šią temą:
Deep Learning For Eeg-based Brain-computer Interfaces: Representations, Algorithms And ApplicationsDidesnis paveikslėlis
  • Formatas: 296 pages
  • Išleidimo metai: 14-Sep-2021
  • Leidėjas: World Scientific Europe Ltd
  • Kalba: eng
  • ISBN-13: 9781786349606
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Deep Learning for EEG-based Brain-Computer Interfaces is an exciting book that describes how emerging deep learning improves the future development of Brain-Computer Interfaces (BCI) in terms of representations, algorithms, and applications. BCI bridges humanity's neural world and the physical world by decoding an individuals' brain signals into commands recognizable by computer devices.

This book presents a highly comprehensive summary of commonly-used brain signals; a systematic introduction of around 12 subcategories of deep learning models; a mind-expanding summary of 200+ state-of-the-art studies adopting deep learning in BCI areas; an overview of a number of BCI applications and how deep learning contributes, along with 31 public BCI datasets. The authors also introduce a set of novel deep learning algorithms aimed at current BCI challenges such as robust representation learning, cross-scenario classification, and semi-supervised learning. Various real-world deep learning-based BCI applications are proposed and some prototypes are presented. The work contained within proposes effective and efficient models which will provide inspiration for people in academia and industry who work on BCI.
Preface v
Part 1: Background 1(44)
1 Introduction
3(6)
1.1 Background on the Brain-Computer Interface
3(2)
1.2 Why Deep Learning?
5(1)
1.3 Why This Book?
6(3)
2 Brain Signal Acquisition
9(18)
2.1 Invasive Approaches
9(5)
2.1.1 Intracortical Approaches
12(1)
2.1.2 Electrocorticography
13(1)
2.2 Noninvasive Approaches
14(7)
2.2.1 Electroencephalography
14(3)
2.2.2 Functional Near-infrared Spectroscopy
17(1)
2.2.3 Functional Magnetic Resonance Imaging
18(1)
2.2.4 Electrooculography
19(1)
2.2.5 Magnetoencephalography
20(1)
2.3 EEG Paradigms
21(6)
2.3.1 Spontaneous EEG
22(1)
2.3.2 Evoked Potential
22(5)
3 Deep Learning Foundations
27(18)
3.1 Discriminative Deep Learning Models
29(6)
3.1.1 Multilayer Perceptron
29(2)
3.1.2 Recurrent Neural Networks
31(3)
3.1.3 Convolutional Neural Networks
34(1)
3.2 Representative Deep Learning Models
35(5)
3.2.1 Autoencoder
36(2)
3.2.2 Restricted Boltzmann Machine
38(1)
3.2.3 Deep Belief Networks
39(1)
3.3 Generative Deep Learning Models
40(3)
3.3.1 Variational Autoencoder
40(2)
3.3.2 Generative Adversarial Networks
42(1)
3.4 Hybrid Models
43(2)
Part 2: Deep Learning-Based BCI and Its Applications 45(50)
4 Deep Learning-Based BCI
47(30)
4.1 Intracortical and ECoG
47(1)
4.2 EEG Potentials
48(13)
4.2.1 Spontaneous EEG Potentials
48(10)
4.2.2 Evoked Potentials
58(3)
4.3 fNIRS
61(1)
4.4 fMRI
62(1)
4.5 EOG
63(1)
4.6 MEG
64(1)
4.7 Discussion
64(13)
4.7.1 Discussions on Brain Signals
71(2)
4.7.2 Discussions on Deep Learning Models
73(4)
5 Deep Learning-Based BCI Applications
77(18)
5.1 Health Care
77(7)
5.2 Smart Environments
84(1)
5.3 Communication
85(1)
5.4 Security
85(1)
5.5 Affective Computing
86(1)
5.6 Driver Fatigue Detection
86(1)
5.7 Mental Load Measurement
87(1)
5.8 Other Applications
88(1)
5.9 Benchmark Data Sets
88(3)
5.10 Discussions
91(4)
Part 3: Recent Advances on Deep Learning for EEG-Based BCI 95(72)
6 Robust Brain Signal Representation Learning
97(26)
6.1 Overview
97(3)
6.2 Subject-Dependent
100(13)
6.2.1 Temporal Representation Learning
100(3)
6.2.2 Spatial Representation Learning
103(2)
6.2.3 Graphical Representation Learning
105(4)
6.2.4 Spatiotemporal Representation Learning
109(3)
6.2.5 Discussion
112(1)
6.3 Cross-Subject
113(7)
6.3.1 Overview
113(1)
6.3.2 EEG Characteristic Analysis
113(2)
6.3.3 Representation Learning Framework
115(5)
6.4 Subject-Independent
120(3)
6.4.1 Transfer Learning
121(1)
6.4.2 Intersubject Transfer Learning
121(2)
7 Cross-Scenario Classification
123(26)
7.1 Overview
123(1)
7.2 Attention-Based Classification Across Signal Sources
124(11)
7.2.1 Overview
124(1)
7.2.2 Reinforced Selective Attention Model
125(9)
7.2.3 Discussion
134(1)
7.3 Attention-Based Classification Across Applications
135(12)
7.3.1 Overview
135(1)
7.3.2 Reinforced Attentive CNN
136(2)
7.3.3 Evaluation Across Applications
138(9)
7.3.4 Discussion
147(1)
7.4 Transfer Learning Methods
147(2)
8 Semi-Supervised Classification
149(18)
8.1 Generative Methods
149(12)
8.1.1 Overview
149(3)
8.1.2 Adversarial Variational Embedding Algorithm
152(5)
8.1.3 Evaluation
157(4)
8.1.4 Discussion
161(1)
8.2 Wrapper Methods
161(3)
8.2.1 Self-Training
162(1)
8.2.2 Co-Training
163(1)
8.2.3 Boosting
164(1)
8.3 Unsupervised Representations Learning
164(3)
Part 4: Typical Deep Learning for EEG-Based BCI Applications 167(74)
9 Authentication
169(22)
9.1 EEG-Based Person Identification
169(13)
9.1.1 Challenges
169(4)
9.1.2 EEG Pattern Analysis
173(2)
9.1.3 Methodology
175(6)
9.1.4 Discussions
181(1)
9.2 Person Authentication
182(9)
9.2.1 Motivations
183(1)
9.2.2 Methodology
184(5)
9.2.3 Data Acquisition
189(2)
10 Visual Reconstruction
191(20)
10.1 Brain2Object: Printing Your Mind
191(11)
10.1.1 Brain2Object System
192(6)
10.1.2 Data Acquisition
198(1)
10.1.3 Online System
199(2)
10.1.4 Discussions
201(1)
10.2 Geometrical Shape Reconstruction
202(9)
10.2.1 EEG Signal Acquisition
203(1)
10.2.2 Methodology
204(4)
10.2.3 Evaluations
208(2)
10.2.4 Discussions
210(1)
11 Language Interpretation
211(10)
11.1 Methodology
212(4)
11.1.1 Overview
212(1)
11.1.2 Deep Feature Learning
212(3)
11.1.3 Feature Adaptation
215(1)
11.2 Brain-Controlled Typing System
216(3)
11.3 Discussion
219(2)
12 Intent Recognition in Assisted Living
221(6)
12.1 System Overview
221(1)
12.2 Orthogonal Array Tuning Method
222(3)
12.2.1 Overview
222(1)
12.2.2 OATM Workflow
223(2)
12.3 Deployment
225(2)
12.3.1 Mind-Controlled Mobile Robot
225(1)
12.3.2 Mind-Controlled Appliances
226(1)
13 Patient-Independent Neurological Disorder Detection
227(10)
13.1 Introduction
227(2)
13.2 Methodology
229(6)
13.2.1 Overview
229(2)
13.2.2 EEG Decomposition
231(2)
13.2.3 Attention-Based Seizure Diagnosis
233(1)
13.2.4 Patient Detection
234(1)
13.2.5 Training Details
235(1)
13.3 Discussions
235(2)
14 Future Directions and Conclusion
237(4)
14.1 Future Directions
237(3)
14.1.1 General Framework
237(1)
14.1.2 Subject-Independent Classification
238(1)
14.1.3 Semi-Supervised and Unsupervised Classification
238(1)
14.1.4 Hardware Portability
239(1)
14.2 Conclusion
240(1)
Bibliography 241(32)
Index 273
Xiang Zhang is a postdoc fellow at Harvard University, working with Prof. Marinka Zitnik in Machine Intelligence for Medicine and Science (MIMS) lab. He received his PhD degree (in 2020) at School of Computer Science from the University of New South Wales (UNSW) while supervised by Dr Lina Yao. Xiang has a number of publications on the top venues including SIGKDD, ICDM, UbiComp, IJCAI, PerCom, AAAI, ACM TIST, and IEEE Internet of Things Journal. Moreover, Xiang has been awarded Google PhD Fellowship 2018 in Human Computer Interface on a super competitive basis (4 recipients in Australia among 57 recipients global). He was also selected for EPFL Engineering PhD Summit (11 winners out of 200+ applicants).

Xiang's research interests lay in graph representation learning, data mining, and deep learning with focusing applications on neurological diagnosis, user authentication, biomedical sciences, health care, and Brain-Computer Interface (BCI).

Lina Yao is currently a Scientia Senior Lecturer at School of Computer Science and Engineering, the University of New South Wales (UNSW). She was awarded Australia Research Council (ARC) Discovery Early Career Researcher Award (DECRA) and Inaugural Vice Chancellor's Women's Research Excellence Award (University of Adelaide) in 2015, and Scientia Fellowship (UNSW) in 2020.

She currently serves as Associate Editor for ACM Transactions on Sensor Networks (TOSN) and PC members of several most prestigious data mining and machine learning international conferences including NeurIPS, KDD, SIGIR, AAAI, IJCAI, ICDM, and ACM MM. Lina has published around 200 papers on top journal and conferences, along with four books/chapters.

Lina is directing the Data Dynamics Lab (D² Lab) that strives for developing novel data mining, machine learning and deep learning algorithms as well as designing systems and interfaces to enable novel ways of human-machine interactions, including an improved understanding of challenges such as robustness, trust, explainability and resilience that improve human-autonomy partnership. Her research is motivated by, and contributes to, various applications in Information Filtering, Healthcare Informatics, Cyber Security, Transportation, Industry 4.0 and E-commerce.
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