Computational Intelligence and Deep Learning Methods for Neuro-rehabilitation Applications explores the different possibilities of providing AI based neuro-rehabilitation methods to treat neurological disorders. This book provides in-depth knowledge on the challenges and solutions associated with the different varieties of neuro-rehabilitation through the inclusion of case studies and real-time scenarios in different geographical locations. Beginning with an overview of neuro-rehabilitation applications, the book discusses the role of machine learning methods in brain function grading for adults with Mild Cognitive Impairment, Brain Computer Interface for post-stroke patients, developing assistive devices for paralytic patients, and cognitive treatment for spinal cord injuries. Topics also include AI-based video games to improve the brain performances in children with autism and ADHD, deep learning approaches and magnetoencephalography data for limb movement, EEG signal analysis, smart sensors, and the application of robotic concepts for gait control.
1. Computational Intelligence and Deep Learning Methods for Neuro-rehabilitation Applications - p. Page i 2. Cover - p. Page i 3. Front Matter - p. Page ii 4. Table of Contents - p. Page iii 5. Copyright - p. Page iv 6. Contributors - p. Page iv 7. Preface - p. Page vii 8. List of Illustrations - p. Page ix 9. List of Tables - p. Page x 10. Chapter 1 : AI-based technologies, challenges, and solutions for neurorehabilitation: A systematic mapping - p. Page x 11. Chapter 2 : Complex approaches for gait assessment in neurorehabilitation - p. Page 24 12. Chapter 3 : Deep learning method for adult patients with neurological disorders under remote monitoring - p. Page 55 13. Chapter 4 : Rehabilitation for individuals with autism spectrum disorder using mixed reality virtual assistants - p. Page 79 14. Chapter 5 : Wearable sleeve for physiotherapy assessment using ESP32 and IMU sensor - p. Page 99 15. Chapter 6 : Machine learning for Developing neurorehabilitation-aided assistive devices - p. Page 118 16. Chapter 7 : Deep learning and machine learning methods for patients with language and speech disorders - p. Page 142 17. Chapter 8 : Machine learning for cognitive treatment planning in patients with neurodisorder and trauma injuries - p. Page 161 18. Chapter 9 : Artifacts removal techniques in EEG data for BCI applications: A survey - p. Page 191 19. Chapter 10 : Deep learning system of naturalistic communication in brain–computer interface for quadriplegic patient - p. Page 210 20. Chapter 11 : Motor imaginary tasks-based EEG signals classification using continuous wavelet transform and LSTM network - p. Page 236 21. Chapter 12 : Enhancing human brain activity through a systematic study conducted using graph theory and probability concepts on a hydar prehistoric organism - p. Page 253 22. Index - p. Page 276
Computational Intelligence and Deep Learning Methods for Neuro-rehabilitation Applications explores the different possibilities of providing AI based neuro-rehabilitation methods to treat neurological disorders. This book provides in-depth knowledge on the challenges and solutions associated with the different varieties of neuro-rehabilitation through the inclusion of case studies and real-time scenarios in different geographical locations. Beginning with an overview of neuro-rehabilitation applications, the book discusses the role of machine learning methods in brain function grading for adults with Mild Cognitive Impairment, Brain Computer Interface for post-stroke patients, developing assistive devices for paralytic patients, and cognitive treatment for spinal cord injuries. Topics also include AI-based video games to improve the brain performances in children with autism and ADHD, deep learning approaches and magnetoencephalography data for limb movement, EEG signal analysis, smart sensors, and the application of robotic concepts for gait control.
1. Computational Intelligence and Deep Learning Methods for Neuro-rehabilitation Applications - p. Page i 2. Cover - p. Page i 3. Front Matter - p. Page ii 4. Table of Contents - p. Page iii 5. Copyright - p. Page iv 6. Contributors - p. Page iv 7. Preface - p. Page vii 8. List of Illustrations - p. Page ix 9. List of Tables - p. Page x 10. Chapter 1 : AI-based technologies, challenges, and solutions for neurorehabilitation: A systematic mapping - p. Page x 11. Chapter 2 : Complex approaches for gait assessment in neurorehabilitation - p. Page 24 12. Chapter 3 : Deep learning method for adult patients with neurological disorders under remote monitoring - p. Page 55 13. Chapter 4 : Rehabilitation for individuals with autism spectrum disorder using mixed reality virtual assistants - p. Page 79 14. Chapter 5 : Wearable sleeve for physiotherapy assessment using ESP32 and IMU sensor - p. Page 99 15. Chapter 6 : Machine learning for Developing neurorehabilitation-aided assistive devices - p. Page 118 16. Chapter 7 : Deep learning and machine learning methods for patients with language and speech disorders - p. Page 142 17. Chapter 8 : Machine learning for cognitive treatment planning in patients with neurodisorder and trauma injuries - p. Page 161 18. Chapter 9 : Artifacts removal techniques in EEG data for BCI applications: A survey - p. Page 191 19. Chapter 10 : Deep learning system of naturalistic communication in brain–computer interface for quadriplegic patient - p. Page 210 20. Chapter 11 : Motor imaginary tasks-based EEG signals classification using continuous wavelet transform and LSTM network - p. Page 236 21. Chapter 12 : Enhancing human brain activity through a systematic study conducted using graph theory and probability concepts on a hydar prehistoric organism - p. Page 253 22. Index - p. Page 276
