Brain Informatics Technology
Description
Brain informatics sits at the intersection of information technology and neuroscience, using innovations from both fields to deepen our understanding of the human brain. Through tools like EEG and fMRI, researchers have gained new insights into cognition, behavior, and neurological disorders, paving the way for treatments, personalized medicine, and diagnostic advances. The integration of brain-computer interfaces and machine learning further expands possibilities in areas such as AI, robotics, healthcare, and human-machine interaction.
This book offers a perceptive study of the relationship between neuroscience and IT, exploring the significant implications of computational approaches in solving the secrets of the human brain. Navigating through topics such as brain anatomy, cognitive processes, and computer models of brain activity, it provides a thorough overview of the fundamental concepts that underpin brain informatics research. It also looks at real-world applications in a variety of fields, including customized medicine, healthcare diagnostics, instructional technology, and artificial intelligence systems inspired by the human brain. This essential guide offers a comprehensive view of the revolutionary potential of brain informatics influencing the future of information technology.
Readers will find this volume:
Explores the intersection of neuroscience and informatics with practical applications;
Provides insights into cutting-edge research and technologies shaping brain-computer interaction;
Features contributions from leading experts in brain informatics and cognitive technologies.
Audience
Researchers and professionals in the fields of neuroscience, cognitive science, artificial intelligence, and data analytics.
More details
Other editions
Additional editions
Persons
Ruby Bhatt, PhD is an assistant professor in the Department of Computer Science at Medi-Caps University, Indore, India. She has authored several research papers in refereed journals and more than 15 conference papers on security issues in wireless sensor networks and optimization techniques. Her areas of interest include wireless sensor networks, artificial intelligence, data mining, and data analytics.
D. Dhanya, PhD is an Associate Professor in the Department of Artificial Intelligence and Data Science at the Mar Ephraem College of Engineering and Technology, Marthandam, India, with more than ten years of teaching experience. She has published various research papers in esteemed refereed international journals and presented her research at various international conferences. Her research focuses on cloud computing, evolutionary algorithms, artificial intelligence, and wireless sensor networks.
Roshani Choudhary, PhD is highly experienced in the development of classification algorithms for solving various classification problems. With more than six years of teaching and research experience, her research interests include writing machine learning algorithms and codes for solving real-life problems and the development of complex neural networks.
Content
Preface xxiii
Part I: Foundations of Brain Informatics 1
1 Foundations of Brain Informatics: An Overview 3
Hirald Dwaraka Praveena, C. Subhas, A. Jaya Lakshmi, M. Venkatanaresh and P. Geetha
1.1 Introduction to Brain Informatics 4
1.2 Theoretical Foundations of Brain Informatics 7
1.3 Investigations of Human Information Processing Systems 9
1.4 Technologies and Tools in Brain Informatics 12
1.5 Integration of Technologies in Brain Informatics 15
1.6 Conclusion 20
1.7 Future Scope 21
2 Foundation of Cognitive and Computational Brain Science 25
Hema Umapathi, Ananya Pattjoshi, Khushi Sanjeev Udasi, Tushar Tejonidhi M., Yuvaraj Sivamani, Saranraj Pazhani and Sumitha Elayaperumal
2.1 Introduction 26
2.2 Beginning of Computational Neuroscience 29
2.3 Key Concepts for Cognitive Brain Science 31
2.4 Key Concepts in Computational Brain Science 34
2.5 Application 38
2.6 Future and Challenges in Computational Neurobiology 43
2.7 Challenges and Constraints Today 44
2.8 Future Directions and Opportunities 45
2.9 Conclusion 49
3 Future Directions and Challenges in Brain Informatics 57
Kriti Sankhla
3.1 Introduction to Brain Informatics 58
3.2 Global Landscape and Future Directions 61
3.3 Multi-Modal Brain Data Integration 63
3.4 Data Fusion Techniques and Machine Learning 66
3.5 Computational Neuroscience and Brain Modeling 70
3.6 Brain-Computer Interfaces (BCIs) 73
3.7 Data Privacy and Security in Brain Informatics 76
3.8 Interdisciplinary Collaboration in Brain Informatics 78
3.9 Ethical and Societal Implications 80
3.10 Conclusion 83
Part II: Data Acquisition and Ethical Considerations 89
4 Data Acquisition Technologies in Brain Informatics: Tools and Techniques 91
Nilesh Kharche and Anamika Ahirwar
4.1 Introduction 91
4.2 Brain Informatics 92
4.3 Electroencephalography (EEG) 93
4.4 Magnetoencephalography (MEG) 95
4.5 Functional Magnetic Resonance Imaging (fMRI) 96
4.6 Positron Emission Tomography (PET) 98
4.7 Near-Infrared Spectroscopy (NIRS) 99
4.8 Invasive Techniques in Brain Data Acquisition 100
4.9 Multimodal Data Acquisition Approaches 102
4.10 Emerging Technologies and Trends in Brain Data Acquisition 104
4.11 Data Quality, Storage, and Management in Brain Informatics 106
4.12 Ethical Issues Involved with the Acquisition of Brain Data 108
4.13 Case Studies and Applications in Brain Data Acquisition 110
4.14 Conclusion 112
5 Ethical Consideration in Brain Informatics Color Blindness Research and Applications 115
Sakshi Khullar and Yogita Thareja
5.1 Introduction 116
5.2 Literature Review 118
5.3 Research Methodology 121
5.4 Implications/Conclusion of the Study 124
5.5 Discussion 127
5.6 Conclusion 130
5.7 Challenges and Future Scope 130
Part III: Neural Networks and Machine Learning 135
6 Neural Networks: The Core Foundations, Challenges, and Applications in Brain Informatics 137
Madiha Munawar, Monika Singh T., Kishor Kumar Reddy C. and Marlia Mohd Hanafiah
6.1 Introduction 138
6.2 Fundamentals of Neural Networks in Brain Informatics 143
6.3 Architectures and Models of Neural Networks 148
6.4 Applications of Neural Networks in Brain Informatics 154
6.5 Ethical Considerations and Challenges in Brain Informatics 160
6.6 Optimization Techniques in Neural Networks for Brain Informatics 163
6.7 Comparative Analysis of Neural Network Techniques 166
6.8 Future Directions and Emerging Trends in Neural Networks for Brain Informatics 169\
6.9 Conclusion 172
7 Neural Networks: Building Blocks of Brain Informatics 177
Kiran Raj V. and Anoop Jacob Thomas
7.1 Introduction to Neural Networks and Brain Informatics 177
7.2 Structure and Function of Human Brain 183
7.3 Basic Components of Artificial Neural Networks 185
7.4 Types of Neural Networks 187
7.5 Application of Neural Networks in Brain Imaging and Neurosciences 190
7.6 Challenges and Future Directions 192
7.7 Conclusion 194
8 Machine Learning Techniques for Brain and Health Data 201
Shubhra Dixit, Surbhi Gupta and Ajay Sharma
8.1 Introduction 202
8.2 Supervised Learning 202
8.3 Unsupervised Learning Techniques in Brain and Health Data 208
8.4 Semi-Supervised Learning Techniques in Brain and Health Data 211
8.5 Reinforcement Learning Techniques in Brain and Health Data 213
8.6 Deep Learning in Brain and Health Data 219
8.7 Natural Language Processing in Brain and Health Data 223
8.8 Applications of Machine Learning in Brain and Health Data 224
8.9 Future Directions and Challenges in Machine Learning Techniques for Brain and Health Data 226
9 Machine Learning Revolutionizing Brain Health: Innovations and Future Directions 231
Santosh Soni, Pramod Singh and Akhilesh A. Waoo
9.1 Overview of Machine Learning (ML) in Healthcare 232
9.2 Machine Learning Foundations 232
9.3 Machine Learning Applications in Brain Health 234
9.4 Case Studies 240
9.5 Tools and Technologies 244
9.6 Current Innovations and Future Directions 247
9.7 Conclusion 250
10 Machine Learning in Brain and Health Data: Current Advances and Future Pathways 253
Selvani Deepthi Kavila, Rajesh Bandaru, Moni Sushma Deep Kavila and K. Veera Raghavendra Rao
10.1 Introduction 254
10.2 Literature Survey 255
10.3 Proposed System 258
10.4 Performance Analysis and Results 271
10.5 Conclusion 276
10.6 Future Scope 277
Part IV: Imaging and Cognitive Applications 281
11 Novel Study of MRI Brain Tumor Detection and Segmentation by Digital Image Processing Techniques 283
V. Thamilarasi, N. Kanya, R. Roselin, Dahlia Sam and S. Babu
11.1 Background 284
11.2 Brain Anatomy 284
11.3 Literature Review 285
11.4 Segmentation 285
11.5 Evaluation Metrics 297
11.6 Result and Discussion 298
11.7 Conclusion 301
12 Cognitive Brain Imaging Techniques and Their Applications in Intelligent Decision-Making 305
Raginee Tiwari, Ashwini A. Waoo and Akhilesh A. Waoo
12.1 Cognitive Brain Introduction and Functional Classification 306
12.2 Introduction of Brain Imaging Techniques 309
12.3 Functional Brain Imaging Study 313
12.4 Recent Study of Human Decision-Making 315
12.5 Neuro-Scientific Insights in Decision-Making 319
12.6 Aspects and Related Facts for Intelligent Decisions 320
12.7 Psychological Disorders and Decision Making 321
12.8 Summary 322
13 Brain Cognitive Development Informatics System to Deal with Various Types of Autism Spectrum Disorder 325
H. Parveen Begum
13.1 Introduction 326
13.2 Literature Review 326
13.3 Methodology - Cognitive Informatics 340
13.4 BCDIS Architecture 343
13.5 Result & Discussion of BCDIS 345
14 Cognitive Intelligence with Brain Imaging: Methodological Challenges 351
Kiran Raj V. and Anoop Jacob Thomas
14.1 Introduction to Cognitive Intelligence 351
14.2 Brain Imaging Techniques in Understanding Cognitive Intelligence 355
14.3 Conclusion 365
Part V: Data Analytics and Neuroscience Applications 373
15 Unraveling the Neural Tapestry: Insights Into Brain Big Data Analytics, Curation, and Management 375
Tuhin James Paul, Rojin G. Raj, Amandeep Singh, Md. Misbah and Khadga Raj Aran
15.1 Introduction 376
15.2 Brain Big Data Analytics 383
15.3 Knowledge Graphs in Brain Informatics 387
15.4 Data Curation and Management 390
15.5 Ethical Considerations and Challenges 392
15.6 Future Directions and Emerging Trends 394
15.7 Conclusion 398
16 Mental Health Detection and Prediction through Machine Learning Technology: Issues and Future Opportunities 403
Kedar Nath Singh, Harsh Pratap Singh, Mahesh Panjwani and Snehil Dahima
16.1 Introduction 404
16.2 Literature Review 408
16.3 Difficulties and Factors 416
16.4 Machine Learning Applications in Future Directions 418
16.5 Conclusions 420
17 Computationally Intelligent Techniques for Neuroscience Applications 425
Vidhya R., Dhanya D., Renu D. S. and Jani Anbarasi L.
17.1 Introduction 426
17.2 Related Work 427
17.3 Methodology 435
17.4 Results and Discussion 438
17.5 Conclusion and Future Work 443
Part VI: Case Studies and Future Challenges 449
18 An Evaluation of Application¿Based Parent Guidance for Kids with Psychological Disorders 451
Sivaprakash. C., P. Ramkumar, R. Uma, P. Hosanna Princye and Sa. Viswavardinii
18.1 Introduction 452
18.2 Classification System 453
18.3 Definition and Classification 454
18.4 Causes 459
18.5 Diagnosis and Assessment 460
18.6 Impact and Consequences 461
18.7 Treatment and Intervention 462
18.8 Prevention and Early Intervention 463
18.9 Methodology 463
18.10 Future Directions and Implications 466
18.11 Conclusion 468
19 Challenges and Future Direction of Brain Imaging Studies with Focus on Understanding 471
Shital R. Shegokar and Anamika Ahirwar
19.1 Introduction 472
19.2 Non-Invasive Efficient Neuroimaging Methods 473
19.3 fMRI 473
19.4 Current Developments in Neuroimaging Methods and How They Affect Neuroscience Research 475
19.5 New Advancements in fMRI Technology: Improved Spatial and Temporal Resolution 476
19.6 EEG Technology 477
19.7 EEG and fMRI Limitations and Challenges 480
19.8 Innovative Techniques in Neuroimaging 481
19.9 Transcranial Electrical Stimulation (TES) 483
19.10 Using TES and DTI to Treat and Recognize Brain Connectivity Conditions 484
19.11 Limitations, Challenges, and Future Directions in DTI and TES 485
19.12 An Overview of Latest Advances in Neuroimaging and How Those Affect Clinical Practice and Neuroscience Research 486
19.13 Future Prospects for Neuroimaging 486
19.14 Conclusion 487
20 Detection of Brain Tumor Using Machine Learning Model 493
R. Uma, P. Ramkumar, Sivaprakash. C., J. Anitha Ruth and Sa.Viswavardinii
20.1 Introduction 494
20.2 Literature Review 497
20.3 Methodology 499
20.4 Conclusion 506
References 506
Index 509