Full Description
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.
Contents
1. Introduction to Artificial Intelligence and Brain-Computer Interface
2. Development BCI Using AI Diagnosis of Epileptic Seizure Disorders
3. AI-Based BCI for Identification of Sleep Disorders Using EEG Signals
4. Emotion Recognition Based BCI
5. AI-Based BCI for Apnea Detection
6. Motor-Imagery Task Classification in BCI
7. Identifying Alcoholic Brain State and Effect in BCI
8. Approaches for Classification of Apnea Disorders Using EEG Signals
9. Stress Management Using Artificial Intelligence for BCI
10. Machine Learning Techniques for Development of Smart Healthcare
11. Prediction of Disease Based on Probabilistic Modeling of Medical Data
12. AI-Based Classification of Focal Disorders Using EEG Signals
13. Identification and Analysis of EEG Signals for BCI
14. Intelligent Medical Data Processing for BCI
15. Management of Disease Spread in Large Populations: Case Studies in BCI
