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Brain to Computer Interfaces Using Machine Learning and Deep Learning

Edited by Tawseef Ahmed Teli, Syed Immamul Ansarullah, Arun Kumar Rana, Suman Lata Tripathi, and Kashif Nisar
Copyright: 2026 | Expected Pub Date: 2026
ISBN: 9781394389506 | Hardcover |
428 pages
Price: $225 USD

One Line Description
Brain-Computer Interface; Deep Learning; EEG; Signal Decoding; Machine Learning; Motor Imagery Classification; FPGA; BCI; LSTM; Brain Signal Processing; EEG Feature Extraction; Sustainable Assistive Technology; Neuroprosthetics; Neurorehabilitation; Imagined Speech Decoding

Description
What if the most powerful interface you will ever use requires no keyboard, no screen, no voice, just thought? Brain-computer interfaces are making this a reality, and brainwave-to-machine commands are the comprehensive technical roadmaps to understanding, building, and deploying them. These innovative technologies provide previously unheard-of opportunities for control, rehabilitation, and communication by bridging the gap between the human brain and external equipment.
This book presents the fundamentals of neuroscience that make brain-computer interfaces possible, covering the electrical language of neurons, the recording modalities that capture it, and the preprocessing pipelines that transform raw brainwaves into analysis-ready data. From that foundation, it builds systematically through classical machine learning algorithms, convolutional neural networks for spatial EEG pattern recognition, and long-short-term memory-based recurrent architectures for decoding the temporal dynamics of brain activity, always anchored to real implementation, not just theory. Dedicated chapters and case studies address neurorehabilitation for stroke and spinal cord injury recovery, early detection of dementia, and the convergence of brain-computer interfaces with augmented and virtual reality. Competing titles either restrict themselves to a single modality or a single technique; this book refuses that narrowness, delivering a unified framework that moves from algorithm design to sustainable hardware deployment. This essential guide is both a rigorous graduate-level text and an enduring reference for the researchers, engineers, and clinicians who will shape the future of human-computer interaction.

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Author / Editor Details
Tawseef Ahmed Teli, PhD is an Assistant Professor in the Higher Education Department at Government Degree College Anantnag Jammu and Kashmir, India. He holds a PhD in computer science from the University of Kashmir, with research spanning machine and deep learning, IoT, autonomous systems, robotics, drug discovery, and network security.

Syed Immamul Ansarullah, PhD is an Assistant Professor in the Department of IMBA at the University of Kashmir. He holds a PhD in machine learning and AI and publishes actively at the intersection of soft computing, data mining, and cybersecurity.

Arun Kumar Rana, PhD is an Assistant Professor at the Galgotias College of Engineering. He brings more than 16 years of teaching and research experience, with more than 30 SCI-indexed papers, ten granted patents, and domain strength in image processing, IoT, and wireless sensor networks.

Suman Lata Tripathi, PhD is a professor at the Symbiosis Institute of Technology with more than 22 years in academia. She has published more than 141 peer-reviewed publications, 14 Indian patents, and has active roles as a book series editor.

Kashif Nisar, PhD is a Lecturer in Information Technology at the Swinburne University of Technology. He is a Senior IEEE Member, Dean's Award–winning lecturer, and cybersecurity specialist with a PhD from Universiti Teknologi Petronas and postdoctoral training at Auckland University of Technology, New Zealand.

Table of Contents
Preface
1. Topography of Brain-Machine Interfaces: Mapping Neural Signals for Advanced Interaction
Sheikh Umar Mushtaq, Bazila Farooq, Umar Bashir and Sophiya Sheikh
1.1 Introduction
1.2 Neural Signal Acquisition for Brain-Machine Interfaces
1.3 Topographical Mapping of Neural Signals
1.3.1 Cortical Dynamics and Signal Localization
1.3.2 Signal Processing and Feature Extraction
1.4 Machine Learning in BMI Signal Translation
1.4.1 Supervised Learning Approaches
1.4.2 Adaptive Learning and Neural Plasticity
1.5 Neuroimaging and Deep Learning Integration
1.5.1 Functional Neuroimaging for Enhanced Mapping
1.5.2 Deep Learning Architectures for Advanced Signal Processing
1.6 Case Studies Related to the Topography of BMIs: Mapping Neural Signals for Advanced Interaction
1.7 Challenges and Future Directions
1.7.1 Biocompatibility and Longevity of Implants
1.7.2 Real-Time Processing and Latency Reduction
1.7.3 Ethical and Privacy Concerns
1.8 Conclusion
References
2. Understanding Nerve Signal and the Nervous System
Irtiqa Amin, Quraazah Akeemu Amin, Sheikh Ikhlaq and Obaid Ahmad Bhat
2.1 Introduction
2.1.1 Structure and Components of the HNS
2.2 Mechanism of Neurotransmission and Synaptic Transmission
2.3 Nervous System Disorders and Diseases
2.4 Advances in Neuroscience and Future Advancements
2.5 Conclusion
Abbreviations
Definition
Bibliography
3. Acquisition of Brain Signals and Preprocessing for Machine Learning
Viveka S.
3.1 Introduction
3.2 Brain Signal Acquisition
3.2.1 Brain–Computer Interface (BCI)
3.2.2 Working of BCI
3.3 Types of Brain Signals and Acquisition Methods
3.3.1 Electroencephalography (EEG)
3.3.2 Electrocorticography (ECoG)
3.3.3 Functional Magnetic Resonance Imaging (fMRI)
3.3.4 Magnetoencephalography (MEG)
3.3.5 Functional Near-Infrared Spectroscopy (fNIRS)
3.4 Preprocessing Methods
3.4.1 Independent Component Analysis (ICA)
3.4.2 Filters in Signal Processing
3.4.3 Surface Laplacian (SL)
3.4.4 Common Spatial Pattern (CSP)
3.4.5 Normalization and Standardization
3.4.6 Baseline Correction
3.4.7 Temporal Preprocessing
3.4.8 Spatial Preprocessing
3.4.9 Denoising
3.5 Feature Extraction and Selection
3.5.1 Temporal Feature Extraction
3.5.2 Spatial Feature Extraction
3.5.3 Time-Frequency Feature Extraction
3.5.4 Connectivity Feature Extraction
3.5.5 Entropy-Based Feature Extraction
3.5.6 Feature Selection Techniques for Brain Signals
3.6 Machine Learning in Brain Signal Analysis
3.6.1 Categories of Machine Learning Models
3.6.2 Importance of Machine Learning in Brain Signal Interpretation
3.7 Application of Machine Learning on Brain Signals: DEAP Dataset Case Study
3.7.1 EEG Channels and Their Significance
3.7.2 Significance of Preprocessing
3.7.3 Feature Extraction
3.7.4 Feature Selection
3.7.5 Support Vector Machines (SVM) in Brain Signal Analysis
3.8 Conclusion
References
4. Basics of Machine Learning for Brain Signal Decoding
Richa and Sakshi Mittal
4.1 Introduction to Brain Signal Decoding
4.1.1 Types of Brain Signals
4.2 What Does Brain Signal Decoding Involve
4.3 Machine Learning’s Role in Brain Signal Decoding
4.3.1 Pattern Recognition
4.3.2 Dimensionality Reduction
4.3.3 Time Series Analysis
4.3.4 Real-Time Decoding
4.3.5 Unsupervised Learning and Clustering
4.4 Signal Preprocessing and Feature Extraction
4.5 Challenges in Brain Signal Decoding
4.6 Applications of Machine Learning in Brain Signal Decoding
4.7 Future Trends in Brain Signal Decoding
4.8 Conclusion
Bibliography
5. Deep Learning Architectures for Brain Signal Processing
Goldwyn Sudhakar Jebaraj, Vidhya S. and Konguvel Elango
5.1 Introduction to Deep Learning in Neuroscience
5.2 Foundational Deep Learning Architectures
5.2.1 Convolutional Neural Networks (CNNs)
5.2.2 Recurrent Neural Networks (RNNs)
5.2.3 Graph Neural Networks (GNNs)
5.2.4 Autoencoders and Variational Autoencoders (VAEs)
5.3 Deep Learning Applications and Advanced Topics in Brain Signal Processing
5.3.1 Classification by Brain Signal Modality & Specific Adaptation
5.3.2 Classification by Learning Paradigm/Task
5.3.3 Deep Learning for Multimodal Brain Signal Fusion
5.4 Future Directions & Emerging Trends
References
6. Decoding Motor Imagery with Machine Learning and Deep Learning
Irtiqa Amin, Quraazah Akeemu Amin, Khair Ul Nisa, Sheikh Ikhlaq and Obaid Ahmad Bhat
6.1 Introduction
6.2 Basic Architecture of the Human Brain
6.2.1 Structural Components of the Human Brain
6.2.2 Connectivity and Communication
6.2.3 Developing Brain Architecture
6.3 Architecture of MI via DL and ML
6.3.1 Overview of MI Brain Computer Interface Systems
6.3.2 MI Understanding Using ML/DL Tactics
6.4 Neuroscientific Basis of Motor Imagery
6.4.1 Involvement of Brain Regions in Motor Imagery
6.4.2 Neural Pathways Implication in Motor Imagery
6.4.3 Neurophysiologic Evidence for Motor Imagery
6.4.4 Functional Role of Motor Imagery in Motor Learning and Rehabilitation
6.4.5 Comparing Motor Imagery (MI) and Motor Execution (ME): Similarities and Differences
6.5 Types of MI
6.5.1 Types of MI Based on Sensory Modality
6.5.2 Types of MI Based on Perspective
6.5.3 Applications of Different Types of Motor Imagery
6.6 Application of Motor Imagery
6.6.1 Medical and Rehabilitation Applications
6.6.2 Sports and Performance Enhancement
6.6.3 Brain-Computer Interfaces (BCIs) and Assistive Technologies
6.6.4 Cognitive Science and Psychological Applications
6.6.5 Robotics, Artificial Intelligence, Virtual Reality (VR), and Gaming
6.6.6 Music and Artistic Performance
6.7 Challenges in Motor Imagery in DL and ML Research
6.8 Conclusion 1
References
7. Beyond Motor Control: Decoding Speech and Thoughts with ML and DL
Irtiqa Amin, Quraazah Akeemu Amin, Towseef Ahmad Wani, Aaquib Hussain Ganai and Fida Hussain Bhat
7.1 Introduction
7.1.1 Expanding beyond Motor Movement Control
7.2 Neural Basics of Speech and Thoughts
7.2.1 Neural Correlation of Internal Thoughts and the Silent Speech Approach and Processing
7.2.1.1 Decoding Speech and Thoughts with BCIs and AI
7.2.2 Challenges in Decoding Non-Motor Cognitive Processes
7.3 ML and DL for Neural Decoding
7.3.1 Future Directions and Emerging Trends
7.4 Data Acquisition for Speech and Thought Decoding
7.4.1 Methods of Neural Data Acquisition for Speech and Thought Decoding
7.5 Challenges, Future Directions, and Emerging Tools
7.5.1 Challenges in Brain Signal and Speech Decoding
7.5.2 Future Directions in Brain Signal and Speech Decoding
7.5.3 Emerging Tools in ML/DL for Brain Signal Decoding
7.6 Conclusion
References
8. Machine Learning and Deep Learning for Brain-Computer Interface Rehabilitation
M. Menagadevi, M. Nirmala, D. Thiyagarajan and Suman Lata Tripathi
8.1 Introduction
8.2 Brain-Computer Interfaces (BCIs) in Neurorehabilitation
8.3 Machine Learning Approaches in Brain‑Computer Interfaces
8.3.1 Comparative Analysis of ML Models in BCI Performance
8.4 Deep Learning Techniques for BCIs
8.4.1 Comparative Analysis of DL Models in BCI Performance
8.5 Challenges and Limitations in BCI-Based Neurorehabilitation
8.5.1 Signal Variability and Noise Issues
8.5.2 Computational Complexity and Real-Time Processing
8.5.3 Generalization and Adaptability Issues
8.6 Future Directions and Innovations in BCI Research
Conclusion
References
9. Ethics of AI-Driven Brain‑Computer Interfaces: A Focus on Security and Data Privacy
Nitin Soni, Prince Soni and Kushal Jain
9.1 Introduction
9.2 Understanding Brain Signals and BCI Architecture
9.3 Security Challenges in BCIs
9.4 ML/DL-Based Security Mechanisms for Brain‑Computer Interfaces (BCIs)
9.5 Ethical and Privacy Considerations in ML/DL-Based BCIs
9.6 Future Directions and Recommendations
9.7 Conclusion
Bibliography
10. The Evolving Landscape of Brain-Computer Interfaces
Kritika Arora, Thayanithi C.A., Elipe Arjun and Priyanka Singh
10.1 Introduction
10.1.1 Historical Evolution of BCIs
10.1.2 Current State of the Field
10.2 Recent Technological Advances
10.2.1 Hardware Innovations
10.2.1.1 Neural Sensors and Electrodes
10.2.1.2 Signal Acquisition Systems
10.2.1.3 Miniaturization Technologies
10.2.2 Software Developments
10.2.2.1 Advanced Signal Processing
10.2.2.2 Real-Time Decoding Algorithms
10.2.2.3 Adaptive Learning Systems
10.2.3 Integration Technologies
10.2.3.1 Cloud Computing Integration
10.2.3.2 Edge Computing Solutions
10.2.3.3 IoT Connectivity
10.3 Emerging Applications
10.3.1 Medical Applications
10.3.1.1 Neurological Rehabilitation
10.3.1.2 Assistive Technologies
10.3.2 Consumer Applications
10.3.2.1 Gaming and Entertainment
10.3.2.2 Personal Productivity
10.3.2.3 Smart Home Control
10.3.3 Industrial Applications
10.3.3.1 Manufacturing Control
10.3.3.2 Professional Training
10.3.3.3 Workplace Safety
10.4 Next-Generation BCI Systems
10.4.1 Advanced Neural Interfaces
10.4.1.1 High-Resolution Recording
10.4.1.2 Wireless Technologies
10.4.1.3 Long-Term Stability
10.4.2 Intelligent Processing
10.4.2.1 Autonomous Adaptation
10.4.2.2 Context Awareness
10.4.2.3 Multi-Modal Integration
10.4.3 User Experience
10.4.3.1 Natural Control Interfaces
10.4.3.2 Feedback Mechanisms
10.4.3.3 Learning and Adaptation
10.5 Challenges and Solutions
10.5.1 Technical Challenges
10.5.1.1 Signal Quality
10.5.1.2 Processing Speed
10.5.1.3 Power Management
10.5.2 Implementation Issues
10.5.2.1 Cost Considerations
10.5.2.2 Scalability
10.5.2.3 Maintenance Requirements
10.5.3 User Adoption
10.5.3.1 Training Requirements
10.5.3.2 Acceptance Factors
10.5.3.3 Usability Concerns
10.6 Future Directions
10.6.1 Technological Trends
10.6.1.1 Advanced Neural Decoding
10.6.1.2 Artificial General Intelligence Integration
10.6.1.3 Brain-to-Brain Communication
10.6.2 Application Horizons
10.6.2.1 Emerging Use Cases
10.6.2.2 Novel Interfaces
10.6.2.3 Integration Possibilities
10.6.3 Research Opportunities
10.6.3.1 Potential Breakthroughs
10.7 Ethical and Societal Implications
10.7.1 Ethical Considerations
10.7.2 Societal Impact
10.8 Conclusion
10.8.1 Feedback Mechanisms
10.8.2 Learning and Adaptation
References
11. Advancing Brain-Computer Interfaces with Innovative Deep Learning Approaches
Ashaq Hussain Bhat, Hashmat Fida, Arshid Ahmad Wani and Danish Rashid Pala
11.1 Introduction
11.2 The Evolution of Brain-Computer Interfaces
11.2.1 Early Developments in BCIs
11.3 Deep Learning in BCIs: An Overview
11.4 Representation Learning in BCIs
11.5 Transfer Learning for Model Generalization
11.5.1 Types of Transfer Learning in Brain-Computer Interfaces (BCIs)
11.5.2 Methods for Effective Transfer Learning
11.5.3 Applications of Transfer Learning in Brain-Computer Interfaces
11.5.4 Data Augmentation and Synthetic EEG Data
11.6 Self-Supervised Learning (SSL) in Brain-Computer Interfaces (BCIs)
11.6.1 Key Techniques in SSL for BCIs
11.6.2 Applications of SSL in Brain-Computer Interfaces
11.7 Applications of DL-Based BCIs
11.7.1 Motor Imagery for Prosthetic Control
11.7.2 Neurorehabilitation for Stroke Patient Recovery
11.7.3 Identification of Emotions for Mental Health Assessment
11.7.4 Cognitive Load Monitoring and Adaptive Learning Systems
11.7.5 Brain-to-Text Communication and Augmentative Speech Technologies
11.7.6 Control of Prosthetics through Motor Imagery
11.7.7 Neurorehabilitation for Stroke Recovery
11.7.8 Identifying Emotions for Mental Health Assessment
11.7.9 Systems for Cognitive Load Assessment and Adaptive Learning
11.7.10 Brain-Based Technologies for Augmentative Speech and Communication
11.7.11 Neurocontrolled Smart Home Interfaces
11.7.12 Controlled Prosthetics through Motor Imagery
11.7.13 Neurorehabilitation Recovery for Stroke Patients
11.7.14 Emotion Detection for Mental Health Assessment
11.8 Challenges and Future Directions
11.8.1 Technical Challenges
11.8.2 Interpretability and Trust in AI-Driven BCIs
11.8.3 Ethical, Legal, and Societal Challenges
11.8.3.1 Privacy and Security
11.8.3.2 Ethical Considerations in Neural Modulation
11.8.3.3 Bias and Fairness in BCI Systems
11.8.3.4 Legal and Regulatory Compliance
11.9 Future Research Directions
Conclusion
References
12. Applications of Brain-Computer Interfacing
Sanjay Kumar, Vikram Bali, Kuldeep Singh Kashwan, InderpreetKaur and Mohit Mittal
12.1 Introduction
12.1.1 Definition and Importance of Brain Signal Decoding
12.1.2 Role of Machine Learning in Neuroscience
12.1.3 Current Advancements and Applications
12.2 Objectives and Scope of the Chapter
12.2.1 Key Objectives of the Chapter
12.2.2 Scope of the Chapter
12.3 Fundamentals of Brain Signals
12.3.1 Types of Brain Signals
12.3.1.1 Electroencephalography (EEG)
12.3.1.2 Functional Magnetic Resonance Imaging (fMRI)
12.3.1.3 Magnetoencephalography (MEG)
12.3.2 Physiological Basis of Brain Signal Generation
12.3.3 Challenges in Capturing and Interpreting Brain Signals
12.3.4 Significance of Mouth Extraction in Brain Signals
12.4 Basics of Machine Learning
12.4.1 Core Concepts in Machine Learning
12.4.1.1 Supervised Learning
12.4.1.2 Unsupervised Learning
12.4.1.3 Reinforcement Learning
12.4.2 Common Algorithms for Signal Decoding
12.4.2.1 Support Vector Machines (SVM)
12.4.2.2 Decision Trees
12.4.2.3 Neural Networks
12.4.3 Importance of Data Preprocessing and Article Engineering
12.4.4 Machine Learning Techniques for Brain Signal Decoding
12.4.5 Preprocessing Techniques for Brain Signals
12.4.5.1 Noise Removal
12.4.5.2 Signal Segmentation
12.4.6 Feature Extraction Techniques
12.4.6.1 Frequency-Based Features
12.4.6.2 Time-Domain Features
12.4.7 Overview of Deep Learning Methods for Decoding
12.4.7.1 Convolutional Neural Networks (CNN)
12.4.7.2 Recurrent Neural Networks (RNN)
12.4.8 Comparison of Traditional ML and Deep Learning in Brain Signal Decoding
12.4.8.1 Traditional Machine Learning (ML)
12.4.8.2 Deep Learning
12.5 Case Studies and Applications
12.5.1 Brain-Computer Interface Applications
12.5.1.1 Assistive Technologies
12.5.1.2 Communication Devices for Disabled Individuals
12.5.1.3 Clinical Applications
12.6 Challenges and Future Directions
12.6.1 Key Challenges
12.6.1.1 Data Scarcity and Quality
12.6.1.2 Interpretability of ML Models
12.6.1.3 Ethical and Privacy Anxieties
12.6.2 Future Research Directions
12.6.2.1 Explainable AI for Neuroscience
12.6.2.2 Real-Time Brain Signal Decoding Systems
12.6.2.3 Integration of Hybrid Models
12.7 Conclusion
References
13. Boosting Workforce Potential with Human Augmentation Using Brain-Computer Interface
Rajesh Singh, Fraiz Parveen and Praveen Kumar Malik
13.1 Introduction to Human Resource Management
13.1.1 Optimizing Human-Centric AI with BCI in Industry 5.0
13.1.2 BCI-Based Trust and Transparency Challenges in Industry 5.0
13.1.3 Ethical and Organizational Framework for BCI in Industry 5.0
13.2 Human Augmentation
13.2.1 Benefits of Human Augmentation Technology
13.3 Case Studies
13.3.1 Replicating Human Capabilities
13.3.2 Supplementing Human Ability
13.3.3 Exceeding Human Ability
13.4 Discussion and Future Prospects
13.5 Conclusions
References
14. Brain-Computer Interface for Trust and Transparency in Human-Centric Artificial Intelligence
Rajesh Singh, Aashna Sinha, Anita Gehlot and Praveen Kumar Malik
14.1 Introduction to Human Centric AI
14.1.1 Enhancing Human-Centric AI with BCI
14.1.2 The Human-Centered Ethical AI (HCEAI) Framework
14.2 AI in Transparency and Trust
14.3 Human-Centric AI in Different Sectors
14.4 Human-Centric Explainable AI (HCEAI) in Education and Healthcare
14.5 Case Study
14.6 Discussion and Future Perspective
Conclusion
References
15. Brain-Computer Interface Rehabilitation: Challenges & Future Directions
Rabiya Nazeer and Dhanpratap Singh
15.1 Introduction
15.2 Machine Learning in BCI Rehabilitation
15.2.1 Role of Machine Learning in Brain Signal Interpretation
15.2.2 Commonly Used ML Algorithms in BCI
15.2.3 Applications in Rehabilitation
15.2.4 Limitations of Traditional Machine Learning
15.2.5 The Shift toward Deep Learning
15.3 Deep Learning for BCI Rehabilitation
15.3.1 Introduction to Deep Learning in BCIs
15.3.2 Advantages of DL in EEG Signal Processing
15.3.3 Deep Learning Architectures for BCI
15.3.4 Applications of DL-Based BCIs in Rehabilitation
15.3.5 Case Example: CNN-BiLSTM Hybrid Model
15.3.6 Challenges in Applying DL to BCIs
15.4 Case Studies and Applications of ML/DL-Based BCIs in Rehabilitation
15.4.1 Motor Rehabilitation with BCI Systems
15.4.2 Cognitive Rehabilitation
15.4.3 Assistive Communication
15.4.4 Emotion and Stress Detection in Therapy
15.4.5 Comparative Table of Applications
15.4.6 Datasets Used in Case Studies
15.4.7 Real-World BCI Systems in Use
15.5 Challenges and Limitations in ML/DL-Based BCI Rehabilitation
15.5.1 Inter-Subject and Intra-Subject Variability
15.5.2 Data Limitations
15.5.3 Real-Time Processing Constraints
15.5.4 Interpretability and Explainability
15.5.5 Usability and Long-Term Engagement
15.5.6 Ethical and Legal Considerations
15.5.7 Generalization and Robustness
15.5.8 Summary Table: Challenges and Research Opportunities
15.6 Workflow of Motor Imagery-Based BCI Rehabilitation Using Deep Learning
15.7 Future Research Directions in ML/DL-Based BCI Rehabilitation
15.7.1 Hybrid BCI Systems
15.7.2 Transfer Learning and Personalization
15.7.3 Semi-Supervised and Few-Shot Learning
15.7.4 Explainable AI (XAI) in BCI
15.7.5 Edge Computing and Real-Time Deployment
15.7.6 Federated Learning for Privacy-Preserving Training
15.7.7 Virtual Reality (VR) and Immersive Interfaces
15.7.8 Standardization and Clinical Validation
15.7.9 Summary of Future Opportunities
15.8 Conclusion
Bibliography
16. Brain Signal Processing with a Focus on Electroencephalography in Attention-Deficit Hyperactivity Disorder
Gaurav Gangwar, Nimisha Singh, Ramandeep Sandhu, Deepika Ghai and Suman Lata Tripathi
16.1 Introduction
16.2 Related Work
16.3 Methodology
16.3.1 Data Collection
16.3.2 Data Pre-Processing
16.3.3 Feature Extraction Using Autoencoder
16.3.3.1 Autoencoder
16.3.4 Reptile Search Algorithm
16.3.4.1 Initialization
16.3.4.2 Encircling Phase (Exploration)
16.3.5 Reptile Search Algorithm-Based Feature Selection
16.3.6 Model Building
16.3.6.1 Random Forest
16.3.6.2 AdaBoost
16.3.6.3 Support Vector Machine
16.3.6.4 K-Nearest Neighbor
16.3.6.5 XGBoost
16.3.7 Model Evaluation
16.3.8 Performance Metrics
16.4 Results and Discussion
16.4.1 Results
16.4.2 Discussion
16.5 Conclusion
References
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Brain to Computer Interfaces Using Machine Learning and Deep Learning