Brain Informatics Technology

Anamika Ahirwar, PhD is a Professor in the Department of Computer Science and Engineering at the Compucom Institute of Technology and Management, Jaipur, Rajasthan, India, with 21 years of experience in teaching and research. She has published more than 70 research papers in reputed national and international journals and conferences, five patents, and authored several books. Her research areas include medical imaging, data mining, celestial sound, Internet of Things, and machine learning. 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.

• Preface xxiiiPart I: Foundations of Brain Informatics 11 Foundations of Brain Informatics: An Overview 3Hirald Dwaraka Praveena, C. Subhas, A. Jaya Lakshmi, M. Venkatanaresh and P. Geetha1.1 Introduction to Brain Informatics 41.2 Theoretical Foundations of Brain Informatics 71.3 Investigations of Human Information Processing Systems 91.4 Technologies and Tools in Brain Informatics 121.5 Integration of Technologies in Brain Informatics 151.6 Conclusion 201.7 Future Scope 212 Foundation of Cognitive and Computational Brain Science 25Hema Umapathi, Ananya Pattjoshi, Khushi Sanjeev Udasi, Tushar Tejonidhi M., Yuvaraj Sivamani, Saranraj Pazhani and Sumitha Elayaperumal2.1 Introduction 262.2 Beginning of Computational Neuroscience 292.3 Key Concepts for Cognitive Brain Science 312.4 Key Concepts in Computational Brain Science 342.5 Application 382.6 Future and Challenges in Computational Neurobiology 432.7 Challenges and Constraints Today 442.8 Future Directions and Opportunities 452.9 Conclusion 493 Future Directions and Challenges in Brain Informatics 57Kriti Sankhla3.1 Introduction to Brain Informatics 583.2 Global Landscape and Future Directions 613.3 Multi-Modal Brain Data Integration 633.4 Data Fusion Techniques and Machine Learning 663.5 Computational Neuroscience and Brain Modeling 703.6 Brain–Computer Interfaces (BCIs) 733.7 Data Privacy and Security in Brain Informatics 763.8 Interdisciplinary Collaboration in Brain Informatics 783.9 Ethical and Societal Implications 803.10 Conclusion 83Part II: Data Acquisition and Ethical Considerations 894 Data Acquisition Technologies in Brain Informatics: Tools and Techniques 91Nilesh Kharche and Anamika Ahirwar4.1 Introduction 914.2 Brain Informatics 924.3 Electroencephalography (EEG) 934.4 Magnetoencephalography (MEG) 954.5 Functional Magnetic Resonance Imaging (fMRI) 964.6 Positron Emission Tomography (PET) 984.7 Near-Infrared Spectroscopy (NIRS) 994.8 Invasive Techniques in Brain Data Acquisition 1004.9 Multimodal Data Acquisition Approaches 1024.10 Emerging Technologies and Trends in Brain Data Acquisition 1044.11 Data Quality, Storage, and Management in Brain Informatics 1064.12 Ethical Issues Involved with the Acquisition of Brain Data 1084.13 Case Studies and Applications in Brain Data Acquisition 1104.14 Conclusion 1125 Ethical Consideration in Brain Informatics Color Blindness Research and Applications 115Sakshi Khullar and Yogita Thareja5.1 Introduction 1165.2 Literature Review 1185.3 Research Methodology 1215.4 Implications/Conclusion of the Study 1245.5 Discussion 1275.6 Conclusion 1305.7 Challenges and Future Scope 130Part III: Neural Networks and Machine Learning 1356 Neural Networks: The Core Foundations, Challenges, and Applications in Brain Informatics 137Madiha Munawar, Monika Singh T., Kishor Kumar Reddy C. and Marlia Mohd Hanafiah6.1 Introduction 1386.2 Fundamentals of Neural Networks in Brain Informatics 1436.3 Architectures and Models of Neural Networks 1486.4 Applications of Neural Networks in Brain Informatics 1546.5 Ethical Considerations and Challenges in Brain Informatics 1606.6 Optimization Techniques in Neural Networks for Brain Informatics 1636.7 Comparative Analysis of Neural Network Techniques 1666.8 Future Directions and Emerging Trends in Neural Networks for Brain Informatics 169\6.9 Conclusion 1727 Neural Networks: Building Blocks of Brain Informatics 177Kiran Raj V. and Anoop Jacob Thomas7.1 Introduction to Neural Networks and Brain Informatics 1777.2 Structure and Function of Human Brain 1837.3 Basic Components of Artificial Neural Networks 1857.4 Types of Neural Networks 1877.5 Application of Neural Networks in Brain Imaging and Neurosciences 1907.6 Challenges and Future Directions 1927.7 Conclusion 1948 Machine Learning Techniques for Brain and Health Data 201Shubhra Dixit, Surbhi Gupta and Ajay Sharma8.1 Introduction 2028.2 Supervised Learning 2028.3 Unsupervised Learning Techniques in Brain and Health Data 2088.4 Semi-Supervised Learning Techniques in Brain and Health Data 2118.5 Reinforcement Learning Techniques in Brain and Health Data 2138.6 Deep Learning in Brain and Health Data 2198.7 Natural Language Processing in Brain and Health Data 2238.8 Applications of Machine Learning in Brain and Health Data 2248.9 Future Directions and Challenges in Machine Learning Techniques for Brain and Health Data 2269 Machine Learning Revolutionizing Brain Health: Innovations and Future Directions 231Santosh Soni, Pramod Singh and Akhilesh A. Waoo9.1 Overview of Machine Learning (ML) in Healthcare 2329.2 Machine Learning Foundations 2329.3 Machine Learning Applications in Brain Health 2349.4 Case Studies 2409.5 Tools and Technologies 2449.6 Current Innovations and Future Directions 2479.7 Conclusion 25010 Machine Learning in Brain and Health Data: Current Advances and Future Pathways 253Selvani Deepthi Kavila, Rajesh Bandaru, Moni Sushma Deep Kavila and K. Veera Raghavendra Rao10.1 Introduction 25410.2 Literature Survey 25510.3 Proposed System 25810.4 Performance Analysis and Results 27110.5 Conclusion 27610.6 Future Scope 277Part IV: Imaging and Cognitive Applications 28111 Novel Study of MRI Brain Tumor Detection and Segmentation by Digital Image Processing Techniques 283V. Thamilarasi, N. Kanya, R. Roselin, Dahlia Sam and S. Babu11.1 Background 28411.2 Brain Anatomy 28411.3 Literature Review 28511.4 Segmentation 28511.5 Evaluation Metrics 29711.6 Result and Discussion 29811.7 Conclusion 30112 Cognitive Brain Imaging Techniques and Their Applications in Intelligent Decision-Making 305Raginee Tiwari, Ashwini A. Waoo and Akhilesh A. Waoo12.1 Cognitive Brain Introduction and Functional Classification 30612.2 Introduction of Brain Imaging Techniques 30912.3 Functional Brain Imaging Study 31312.4 Recent Study of Human Decision-Making 31512.5 Neuro-Scientific Insights in Decision-Making 31912.6 Aspects and Related Facts for Intelligent Decisions 32012.7 Psychological Disorders and Decision Making 32112.8 Summary 32213 Brain Cognitive Development Informatics System to Deal with Various Types of Autism Spectrum Disorder 325H. Parveen Begum13.1 Introduction 32613.2 Literature Review 32613.3 Methodology – Cognitive Informatics 34013.4 BCDIS Architecture 34313.5 Result & Discussion of BCDIS 34514 Cognitive Intelligence with Brain Imaging: Methodological Challenges 351Kiran Raj V. and Anoop Jacob Thomas14.1 Introduction to Cognitive Intelligence 35114.2 Brain Imaging Techniques in Understanding Cognitive Intelligence 35514.3 Conclusion 365Part V: Data Analytics and Neuroscience Applications 37315 Unraveling the Neural Tapestry: Insights Into Brain Big Data Analytics, Curation, and Management 375Tuhin James Paul, Rojin G. Raj, Amandeep Singh, Md. Misbah and Khadga Raj Aran15.1 Introduction 37615.2 Brain Big Data Analytics 38315.3 Knowledge Graphs in Brain Informatics 38715.4 Data Curation and Management 39015.5 Ethical Considerations and Challenges 39215.6 Future Directions and Emerging Trends 39415.7 Conclusion 39816 Mental Health Detection and Prediction through Machine Learning Technology: Issues and Future Opportunities 403Kedar Nath Singh, Harsh Pratap Singh, Mahesh Panjwani and Snehil Dahima16.1 Introduction 40416.2 Literature Review 40816.3 Difficulties and Factors 41616.4 Machine Learning Applications in Future Directions 41816.5 Conclusions 42017 Computationally Intelligent Techniques for Neuroscience Applications 425Vidhya R., Dhanya D., Renu D. S. and Jani Anbarasi L.17.1 Introduction 42617.2 Related Work 42717.3 Methodology 43517.4 Results and Discussion 43817.5 Conclusion and Future Work 443Part VI: Case Studies and Future Challenges 44918 An Evaluation of Application‑Based Parent Guidance for Kids with Psychological Disorders 451Sivaprakash. C., P. Ramkumar, R. Uma, P. Hosanna Princye and Sa. Viswavardinii18.1 Introduction 45218.2 Classification System 45318.3 Definition and Classification 45418.4 Causes 45918.5 Diagnosis and Assessment 46018.6 Impact and Consequences 46118.7 Treatment and Intervention 46218.8 Prevention and Early Intervention 46318.9 Methodology 46318.10 Future Directions and Implications 46618.11 Conclusion 46819 Challenges and Future Direction of Brain Imaging Studies with Focus on Understanding 471Shital R. Shegokar and Anamika Ahirwar19.1 Introduction 47219.2 Non-Invasive Efficient Neuroimaging Methods 47319.3 fMRI 47319.4 Current Developments in Neuroimaging Methods and How They Affect Neuroscience Research 47519.5 New Advancements in fMRI Technology: Improved Spatial and Temporal Resolution 47619.6 EEG Technology 47719.7 EEG and fMRI Limitations and Challenges 48019.8 Innovative Techniques in Neuroimaging 48119.9 Transcranial Electrical Stimulation (TES) 48319.10 Using TES and DTI to Treat and Recognize Brain Connectivity Conditions 48419.11 Limitations, Challenges, and Future Directions in DTI and TES 48519.12 An Overview of Latest Advances in Neuroimaging and How Those Affect Clinical Practice and Neuroscience Research 48619.13 Future Prospects for Neuroimaging 48619.14 Conclusion 48720 Detection of Brain Tumor Using Machine Learning Model 493R. Uma, P. Ramkumar, Sivaprakash. C., J. Anitha Ruth and Sa.Viswavardinii20.1 Introduction 49420.2 Literature Review 49720.3 Methodology 49920.4 Conclusion 506References 506Index 509