Artificial Intelligence-Based Brain Computer Interface provides concepts of AI for the modeling of non-invasive modalities of medical signals such as EEG, MRI and FMRI. These modalities and their AI-based analysis are employed in BCI and related applications. The book emphasizes the real challenges in non-invasive input due to the complex nature of the human brain and for a variety of applications for analysis, classification and identification of different mental states. Each chapter starts with a description of a non-invasive input example and the need and motivation of the associated AI methods, along with discussions to connect the technology through BCI. Major topics include different AI methods/techniques such as Deep Neural Networks and Machine Learning algorithms for different non-invasive modalities such as EEG, MRI, FMRI for improving the diagnosis and prognosis of numerous disorders of the nervous system, cardiovascular system, musculoskeletal system, respiratory system and various organs of the body. The book also covers applications of AI in the management of chronic conditions, databases, and in the delivery of health services. Provides readers with an understanding of key applications of Artificial Intelligence to Brain-Computer Interface for acquisition and modelling of non-invasive biomedical signal and image modalities for various conditions and disorders Integrates recent advancements of Artificial Intelligence to the evaluation of large amounts of clinical data for the early detection of disorders such as Epilepsy, Alcoholism, Sleep Apnea, motor-imagery tasks classification, and others Includes illustrative examples on how Artificial Intelligence can be applied to the Brain-Computer Interface, including a wide range of case studies in predicting and classification of neurological disorders INDICE: 1. Introduction to Artificial Intelligence and Brain-Computer Interface2. Development BCI Using AI Diagnosis of Epileptic Seizure Disorders3. AI-Based BCI for Identification of Sleep Disorders Using EEG Signals4. Emotion Recognition Based BCI5. AI-Based BCI for Apnea Detection6. Motor-Imagery Task Classification in BCI7. Identifying Alcoholic Brain State and Effect in BCI8. Approaches for Classification of Apnea Disorders Using EEG Signals9. Stress Management Using Artificial Intelligence for BCI10. Machine Learning Techniques for Development of Smart Healthcare11. Prediction of Disease Based on Probabilistic Modeling of Medical Data12. AI-Based Classification of Focal Disorders Using EEG Signals13. Identification and Analysis of EEG Signals for BCI14. Intelligent Medical Data Processing for BCI15. Management of Disease Spread in Large Populations: Case Studies in BCI
- ISBN: 978-0-323-91197-9
- Editorial: Academic Press
- Encuadernacion: Rústica
- Páginas: 392
- Fecha Publicación: 08/03/2022
- Nº Volúmenes: 1
- Idioma: Inglés