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, ana...

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Bibliographic Details
Corporate Author:	ScienceDirect (Online service)
Other Authors:	Subasi, Abdulhamit (Editor), Qaisar, Saeed Mian (Editor), Nisar, Humaira, 1970- (Editor)
Format:	eBook
Language:	English
Published:	London, United Kingdom ; San Diego, CA : Academic Press, an imprint of Elsevier, [2025]
Series:	Artificial intelligence applications in healthcare and medicine (Series)
Subjects:	Biosensors. Signal processing > Digital techniques. Artificial intelligence > Medical applications. Human-computer interaction. Brain-computer interfaces. Artificial intelligence. Intelligence artificielle. Biocapteurs. Traitement du signal > Techniques numériques. Interfaces cerveau-ordinateur. artificial intelligence. Electronic books.
Online Access:	Connect to the full text of this electronic book

Table of Contents:

Chapter 1. Introduction to human-machine interaction
Chapter 2. Artificial intelligence techniques for human-machine interaction
Chapter 3. Feature extraction techniques for human-computer interaction
Chapter 4. An overview of techniques and best practices to create intuitive and user-friendly human-machine interfaces
Chapter 5. An overview of electroencephalogram based human-computer interface
Chapter 6. Speech-driven human-machine interaction using Mel-frequency Cepstral coefficients with machine learning and Cymatics Display
Chapter 7. EEG-based brain-computer interface using wavelet packet decomposition and ensemble classifiers
Chapter 8. Understanding dyslexia and the potential of artificial intelligence in detecting neurocognitive impairment in dyslexia
Chapter 9. Early dementia detection and severity classification with deep SqueezeNet convolutional neural network using EEG images
Chapter 10. EEG-based stress identification using oscillatory mode decomposition and artificial neural network
Chapter 11. EEG signal processing with deep learning for alcoholism detection
Chapter 12. ECG-based emotion recognition using CWT and deep learning
Chapter 13. EOG-based human-machine interaction using artificial intelligence
Chapter 14. Surface EMG-based gesture recognition using wavelet transform and ensemble learning
Chapter 15. EEG-based secure authentication mechanism using discrete wavelet transform and ensemble machine learning methods
Chapter 16. EEG-based emotion recognition using AR burg and ensemble machine learning models
Chapter 17. Immersive virtual reality and augmented reality in human-machine interaction
Chapter 18. Mental workload levels of multiple sclerosis patients in the virtual reality environment
Chapter 19. Vision-based action recognition for the human-machine interaction
Chapter 20. Security and privacy in human-machine interaction for healthcare.