Artificial Intelligence and Multimodal Signal Processing in Human-Machine Interaction presents an overview of an emerging field that is concerned with exploiting multiple modalities of communication in both Artificial Intelligence and Human-Machine Interaction. The book not only provides cross disciplinary research in the fields of multimodal signal acquisition and sensing, analysis, IoTs (Internet of Things), Artificial Intelligence, and system architectures, it also evaluates the role of Artificial Intelligence I in relation to the realization of contemporary Human Machine Interaction (HMI) systems.
Readers are introduced to the multimodal signals and their role in the identification of the intended subjects, mental state and the realization of HMI systems are explored, and the applications of signal processing and machine/ensemble/deep learning for HMIs are assessed. A description of proposed methodologies is provided, and related works are also presented. This is a valuable resource for researchers, health professionals, postgraduate students, post doc researchers and faculty members in the fields of HMIs, Brain-Computer Interface (BCI), Prosthesis, Computer vision, and Mental state estimation, and all those who wish to broaden their knowledge in the allied field.
1. Chapter 1 Introduction to human-machine interaction - p. 1 2. Chapter 2 Artificial intelligence techniques for human-machine interaction - p. 19 3. Chapter 3 Feature extraction techniques for human-computer interaction - p. 43 4. Chapter 4 An overview of techniques and best practices to create intuitive and user-friendly human-machine interfaces - p. 63 5. Chapter 5 An overview of electroencephalogram based human-computer interface - p. 79 6. Chapter 6 Speech-driven human-machine interaction using Mel-frequency Cepstral coefficients with machine learning and Cymatics Display - p. 103 7. Chapter 7 EEG-based brain-computer interface using wavelet packet decomposition and ensemble classifiers - p. 125 8. Chapter 8 Understanding dyslexia and the potential of artificial intelligence in detecting neurocognitive impairment in dyslexia - p. 151 9. Chapter 9 Early dementia detection and severity classification with deep SqueezeNet convolutional neural network using EEG images - p. 171 10. Chapter 10 EEG-based stress identification using oscillatory mode decomposition and artificial neural network - p. 185 11. Chapter 11 EEG signal processing with deep learning for alcoholism detection - p. 211 12. Chapter 12 ECG-based emotion recognition using CWT and deep learning - p. 227 13. Chapter 13 EOG-based human-machine interaction using artificial intelligence - p. 247 14. Chapter 14 Surface EMG-based gesture recognition using wavelet transform and ensemble learning - p. 263 15. Chapter 15 EEG-based secure authentication mechanism using discrete wavelet transform and ensemble machine learning methods - p. 283 16. Chapter 16 EEG-based emotion recognition using AR burg and ensemble machine learning models - p. 303 17. Chapter 17 Immersive virtual reality and augmented reality in human-machine interaction - p. 331 18. Chapter 18 Mental workload levels of multiple sclerosis patients in the virtual reality environment - p. 343 19. Chapter 19 Vision-based action recognition for the human-machine interaction - p. 363 20. Chapter 20 Security and privacy in human-machine interaction for healthcare - p. 377 21. Index - p. 393
Artificial Intelligence and Multimodal Signal Processing in Human-Machine Interaction presents an overview of an emerging field that is concerned with exploiting multiple modalities of communication in both Artificial Intelligence and Human-Machine Interaction. The book not only provides cross disciplinary research in the fields of multimodal signal acquisition and sensing, analysis, IoTs (Internet of Things), Artificial Intelligence, and system architectures, it also evaluates the role of Artificial Intelligence I in relation to the realization of contemporary Human Machine Interaction (HMI) systems.
Readers are introduced to the multimodal signals and their role in the identification of the intended subjects, mental state and the realization of HMI systems are explored, and the applications of signal processing and machine/ensemble/deep learning for HMIs are assessed. A description of proposed methodologies is provided, and related works are also presented. This is a valuable resource for researchers, health professionals, postgraduate students, post doc researchers and faculty members in the fields of HMIs, Brain-Computer Interface (BCI), Prosthesis, Computer vision, and Mental state estimation, and all those who wish to broaden their knowledge in the allied field.
1. Chapter 1 Introduction to human-machine interaction - p. 1 2. Chapter 2 Artificial intelligence techniques for human-machine interaction - p. 19 3. Chapter 3 Feature extraction techniques for human-computer interaction - p. 43 4. Chapter 4 An overview of techniques and best practices to create intuitive and user-friendly human-machine interfaces - p. 63 5. Chapter 5 An overview of electroencephalogram based human-computer interface - p. 79 6. Chapter 6 Speech-driven human-machine interaction using Mel-frequency Cepstral coefficients with machine learning and Cymatics Display - p. 103 7. Chapter 7 EEG-based brain-computer interface using wavelet packet decomposition and ensemble classifiers - p. 125 8. Chapter 8 Understanding dyslexia and the potential of artificial intelligence in detecting neurocognitive impairment in dyslexia - p. 151 9. Chapter 9 Early dementia detection and severity classification with deep SqueezeNet convolutional neural network using EEG images - p. 171 10. Chapter 10 EEG-based stress identification using oscillatory mode decomposition and artificial neural network - p. 185 11. Chapter 11 EEG signal processing with deep learning for alcoholism detection - p. 211 12. Chapter 12 ECG-based emotion recognition using CWT and deep learning - p. 227 13. Chapter 13 EOG-based human-machine interaction using artificial intelligence - p. 247 14. Chapter 14 Surface EMG-based gesture recognition using wavelet transform and ensemble learning - p. 263 15. Chapter 15 EEG-based secure authentication mechanism using discrete wavelet transform and ensemble machine learning methods - p. 283 16. Chapter 16 EEG-based emotion recognition using AR burg and ensemble machine learning models - p. 303 17. Chapter 17 Immersive virtual reality and augmented reality in human-machine interaction - p. 331 18. Chapter 18 Mental workload levels of multiple sclerosis patients in the virtual reality environment - p. 343 19. Chapter 19 Vision-based action recognition for the human-machine interaction - p. 363 20. Chapter 20 Security and privacy in human-machine interaction for healthcare - p. 377 21. Index - p. 393
