Unlock the future of technology and medicine with this essential book that provides a comprehensive, perceptive study of Brain Informatics, detailing how computational approaches are revolutionizing our understanding of the brain and driving innovations in AI, robotics, and personalized healthcare. 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.

Table of Contents:
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