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.
1. Artificial Intelligence-Based Brain-Computer Interface - p. i 2. Cover - p. i 3. Title Page - p. i 4. Copyright - p. iv 5. Table of Contents - p. iv 6. Contributors - p. xiii 7. 1 Multiclass sleep stage classification using artificial intelligence based time-frequency distribution and CNN - p. 1 8. 2 A comprehensive review of the movement imaginary brain-computer interface methods: Challenges and future directions - p. 23 9. 3 A new approach to feature extraction in MI-based BCI systems - p. 75 10. 4 Evaluation of power spectral and machine learning techniques for the development of subject-specific BCI - p. 99 11. 5 Concept of AI for acquisition and modeling of noninvasive modalities for BCI - p. 121 12. 6 Bi-LSTM-deep CNN for schizophrenia detection using MSST-spectral images of EEG signals - p. 145 13. 7 Detection of epileptic seizure disorder using EEG signals - p. 163 14. 8 Customized deep learning algorithm for drowsiness detection using single-channel EEG signal - p. 189 15. 9 EEG-based deep learning neural net for apnea detection - p. 203 16. 10 Classification of mental states from rational dilation wavelet transform and bagged tree classifier using EEG signals - p. 217 17. 11 A novel metaheuristic optimization method for robust spatial filter designation and classification of speech imagery tasks in EEG Brain-Computer Interface - p. 237 18. 12 Variational mode decomposition-based finger flexion detection using ECoG signals - p. 261 19. 13 An insight into the hardware and software aspects of a BCI system with focus on ultra-low power bulk driven OTA and Gm-C based filter design, and a detailed review of the recent AI/ML techniques - p. 283 20. 14 Deep autoencoder-based automated brain tumor detection from MRI data - p. 317 21. 15 Measure the superior functionality of machine intelligence in brain tumor disease prediction - p. 353 22. Index - p. 369
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.
1. Artificial Intelligence-Based Brain-Computer Interface - p. i 2. Cover - p. i 3. Title Page - p. i 4. Copyright - p. iv 5. Table of Contents - p. iv 6. Contributors - p. xiii 7. 1 Multiclass sleep stage classification using artificial intelligence based time-frequency distribution and CNN - p. 1 8. 2 A comprehensive review of the movement imaginary brain-computer interface methods: Challenges and future directions - p. 23 9. 3 A new approach to feature extraction in MI-based BCI systems - p. 75 10. 4 Evaluation of power spectral and machine learning techniques for the development of subject-specific BCI - p. 99 11. 5 Concept of AI for acquisition and modeling of noninvasive modalities for BCI - p. 121 12. 6 Bi-LSTM-deep CNN for schizophrenia detection using MSST-spectral images of EEG signals - p. 145 13. 7 Detection of epileptic seizure disorder using EEG signals - p. 163 14. 8 Customized deep learning algorithm for drowsiness detection using single-channel EEG signal - p. 189 15. 9 EEG-based deep learning neural net for apnea detection - p. 203 16. 10 Classification of mental states from rational dilation wavelet transform and bagged tree classifier using EEG signals - p. 217 17. 11 A novel metaheuristic optimization method for robust spatial filter designation and classification of speech imagery tasks in EEG Brain-Computer Interface - p. 237 18. 12 Variational mode decomposition-based finger flexion detection using ECoG signals - p. 261 19. 13 An insight into the hardware and software aspects of a BCI system with focus on ultra-low power bulk driven OTA and Gm-C based filter design, and a detailed review of the recent AI/ML techniques - p. 283 20. 14 Deep autoencoder-based automated brain tumor detection from MRI data - p. 317 21. 15 Measure the superior functionality of machine intelligence in brain tumor disease prediction - p. 353 22. Index - p. 369
