Artificial Intelligence and Multimodal Signal Processing in Human-Machine Interaction
- 1st Edition - September 18, 2024
- Imprint: Academic Press
- Editors: Abdulhamit Subasi, Saeed Mian Qaisar, Humaira Nisar
- Language: English
- Paperback ISBN:9 7 8 - 0 - 4 4 3 - 2 9 1 5 0 - 0
- eBook ISBN:9 7 8 - 0 - 4 4 3 - 2 9 1 5 1 - 7
Artificial Intelligence and Multimodal Signal Processing in Human-Machine Interaction presents an overview of an emerging field that is concerned with exploiting multiple modali… Read more
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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.
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.
- Covers advances in the multimodal signal processing and artificial intelligence assistive HMIs
- Presents theories, algorithms, realizations, applications, approaches, and challenges that will have their impact and contribution in the design and development of modern and effective HMI (Human Machine Interaction) system
- Presents different aspects of the multimodal signals, from the sensing to analysis using hardware/software, and making use of machine/ensemble/deep learning in the intended problem-solving
Researchers working across the biosciences in the fields of Bioinformatics, Computer Science, Biomedical and Computer Engineering
1. Introduction to human-machine interaction
SYED SAAD AHMED, HUMAIRA NISAR, AND LO PO KIM
2. Artificial intelligence techniques for human-machine interaction
HAMID MUKHTAR
3. Feature extraction techniques for human-computer interaction
ABDULHAMIT SUBASI AND SAEED MIAN QAISAR
4. An overview of techniques and best practices to create intuitive and user-friendly human-machine interfaces
VEERENDRA DAKULAGI, KIM HO YEAP, HUMAIRA NISAR, ROHINI DAKULAGI, G N BASAVARAJ, AND MIGUEL VILLAGOMEZ GALINDO
5. An overview of EEG-based human-computer interface (HCI)
MD MAHMUDUL HASAN, SITI ARMIZA MOHD ARIS, AND NORIZAM SULAIMAN
6. Speech-driven human-machine interaction using Mel-frequency Cepstral coefficients with machine learning and Cymatics
SAEED MIAN QAISAR
7. EEG-based brain-computer interface using wavelet packet decomposition and ensemble classifiers
ABDULHAMIT SUBASI AND SAEED MIAN QAISAR
8. Understanding dyslexia and the potential of AI in detecting neurocognitive impairment in dyslexia
SITI ATIYAH ALI, HUMAIRA NISAR, NURFAIZATUL AISYAH AB AZIZ, NOR ASYIKIN FADZIL, NUR SAIDA MOHAMAD ZABER, AND LUTHFFI IDZHAR ISMAIL
9. Early dementia detection and severity classification with deep SqueezeNet convolutional neural network using EEG images
NOOR KAMAL AL-QAZZAZ, SAWAL HAMID BIN MOHD ALI, AND SITI ANOM AHMAD
10. EEG-based stress identification using oscillatory mode decomposition and artificial neural network
SARIKA KHANDELWAL, NILIMA SALANKAR, AND SAEED MIAN QAISAR
11. EEG signal processing with deep learning for alcoholism detection
HAMID MUKHTAR
12. Machine learning and signal processing for ECG-based emotion recognition
FADIME TOKMAK, AYSE KOSAL BULBUL, SAEED MIAN QAISAR, AND ABDULHAMIT SUBASI
13. EOG-based human-machine interaction using artificial intelligence
ALBERTO LOPEZ AND FRANCISCO FERRERO
14. Surface EMG-based gesture recognition using wavelet transform and ensemble learning
ABDULHAMIT SUBASI AND SAEED MIAN QAISAR
15. EEG-based secure authentication mechanism using discrete wavelet transform and ensemble machine learning methods
ABDULHAMIT SUBASI, SAEED MIAN QAISAR, AND AKILA SARIRETE
16. EEG-based emotion recognition using AR burg and ensemble machine learning models
ABDULHAMIT SUBASI AND SAEED MIAN QAISAR
17. Immersive virtual reality and augmented reality in human-machine interaction
MUSTAFA CAN GURSESLI, ANTONIO LANATA, ANDREA GUAZZINI, AND RUCK THAWONMAS
18. Mental workload levels of multiple sclerosis patients in the virtual reality environment
SEDA SASMAZ KARACAN AND HAMDI MELIH SARAOGLU
19. Vision-based action recognition for the human-machine interaction
ANKUSH VERMA, VANDANA SINGH, AMIT PRATAP SINGH CHOUHAN, ABHISHEK, AND ANJALI RAWAT
20. Security and privacy in human-machine interaction for healthcare sector
ANKUSH VERMA, AMIT PRATAP SINGH CHOUHAN, VANDANA SINGH, LEKHA SINGH, GAUTAM SUKLABAIDYA, ABHISHEK SHARMA, AND PANKAJ VERMA
SYED SAAD AHMED, HUMAIRA NISAR, AND LO PO KIM
2. Artificial intelligence techniques for human-machine interaction
HAMID MUKHTAR
3. Feature extraction techniques for human-computer interaction
ABDULHAMIT SUBASI AND SAEED MIAN QAISAR
4. An overview of techniques and best practices to create intuitive and user-friendly human-machine interfaces
VEERENDRA DAKULAGI, KIM HO YEAP, HUMAIRA NISAR, ROHINI DAKULAGI, G N BASAVARAJ, AND MIGUEL VILLAGOMEZ GALINDO
5. An overview of EEG-based human-computer interface (HCI)
MD MAHMUDUL HASAN, SITI ARMIZA MOHD ARIS, AND NORIZAM SULAIMAN
6. Speech-driven human-machine interaction using Mel-frequency Cepstral coefficients with machine learning and Cymatics
SAEED MIAN QAISAR
7. EEG-based brain-computer interface using wavelet packet decomposition and ensemble classifiers
ABDULHAMIT SUBASI AND SAEED MIAN QAISAR
8. Understanding dyslexia and the potential of AI in detecting neurocognitive impairment in dyslexia
SITI ATIYAH ALI, HUMAIRA NISAR, NURFAIZATUL AISYAH AB AZIZ, NOR ASYIKIN FADZIL, NUR SAIDA MOHAMAD ZABER, AND LUTHFFI IDZHAR ISMAIL
9. Early dementia detection and severity classification with deep SqueezeNet convolutional neural network using EEG images
NOOR KAMAL AL-QAZZAZ, SAWAL HAMID BIN MOHD ALI, AND SITI ANOM AHMAD
10. EEG-based stress identification using oscillatory mode decomposition and artificial neural network
SARIKA KHANDELWAL, NILIMA SALANKAR, AND SAEED MIAN QAISAR
11. EEG signal processing with deep learning for alcoholism detection
HAMID MUKHTAR
12. Machine learning and signal processing for ECG-based emotion recognition
FADIME TOKMAK, AYSE KOSAL BULBUL, SAEED MIAN QAISAR, AND ABDULHAMIT SUBASI
13. EOG-based human-machine interaction using artificial intelligence
ALBERTO LOPEZ AND FRANCISCO FERRERO
14. Surface EMG-based gesture recognition using wavelet transform and ensemble learning
ABDULHAMIT SUBASI AND SAEED MIAN QAISAR
15. EEG-based secure authentication mechanism using discrete wavelet transform and ensemble machine learning methods
ABDULHAMIT SUBASI, SAEED MIAN QAISAR, AND AKILA SARIRETE
16. EEG-based emotion recognition using AR burg and ensemble machine learning models
ABDULHAMIT SUBASI AND SAEED MIAN QAISAR
17. Immersive virtual reality and augmented reality in human-machine interaction
MUSTAFA CAN GURSESLI, ANTONIO LANATA, ANDREA GUAZZINI, AND RUCK THAWONMAS
18. Mental workload levels of multiple sclerosis patients in the virtual reality environment
SEDA SASMAZ KARACAN AND HAMDI MELIH SARAOGLU
19. Vision-based action recognition for the human-machine interaction
ANKUSH VERMA, VANDANA SINGH, AMIT PRATAP SINGH CHOUHAN, ABHISHEK, AND ANJALI RAWAT
20. Security and privacy in human-machine interaction for healthcare sector
ANKUSH VERMA, AMIT PRATAP SINGH CHOUHAN, VANDANA SINGH, LEKHA SINGH, GAUTAM SUKLABAIDYA, ABHISHEK SHARMA, AND PANKAJ VERMA
- Edition: 1
- Published: September 18, 2024
- Imprint: Academic Press
- Language: English
AS
Abdulhamit Subasi
Abdulhamit Subasi is a highly specialized expert in the fields of Artificial Intelligence, Machine Learning, and Biomedical Signal and Image Processing. His extensive expertise in applying machine learning across diverse domains is evident in his numerous contributions, including the authorship of multiple book chapters, as well as the publication of a substantial body of research in esteemed journals and conferences. His career has spanned various prestigious institutions, including the Georgia Institute of Technology in Georgia, USA, where he served as a dedicated researcher. In recognition of his outstanding research contributions, Subasi received the prestigious Queen Effat Award for Excellence in Research in May 2018. His academic journey includes a tenure as a Professor of computer science at Effat University in Jeddah, Saudi Arabia, from 2015 to 2020. Since 2020, he has assumed the role of Professor of medical physics at the Faculty of Medicine, University of Turku in Turku, Finland
Affiliations and expertise
Institute of Biomedicine, Faculty of Medicine, University of Turku, Turku, Finland Department of Computer Science, College of Engineering, Effat University, Jeddah, Saudi ArabiaSQ
Saeed Mian Qaisar
Dr. Qaisar currently holds the position of Research & Innovation Department Head for the South-East Region at CESI LINEACT, located in France. In recognition of his teaching and learning excellence, he was honored with the Queen Effat Award in May 2016. Dr. Qaisar's accomplishments encompass two granted patents, as well as an extensive portfolio of published works spanning journal articles, book chapters, and conference papers. Furthermore, Dr. Qaisar contributes to the academic community as an editor for various international journals and is actively involved in the technical and review committees of several international journals and conferences. His current areas of research focus include signal processing, circuits and systems, artificial intelligence, event-driven systems, biomedical and bioinformatics applications, smart grid technology, energy storage, and sampling theory.
Affiliations and expertise
CESI LINEACT, Lyon, France College of Engineering, Effat University, Jeddah, Saudi ArabiaHN
Humaira Nisar
Humaira Nisar has a B.S (Honours) in Electrical Engineering from the University of Engineering and Technology, Lahore, Pakistan, M.S in Nuclear Engineering from Quaid-i-Azam University, Islamabad, Pakistan, another M.S in Mechatronics, and Ph.D. in Information and Mechatronics from Gwangju Institute of Science and Technology, Gwangju, South Korea. She has more than twenty years of research experience. Currently, she is working as a Full Professor in the Department of Electronic Engineering, Universiti Tunku Abdul Rahman, Kampar, Malaysia. Her research interests include signal and image processing, biomedical imaging, neuro-signal processing and analysis, Brain-Computer Interface, and Neurofeedback. She has published hundreds international journal and conference papers. She is a senior member of IEEE
Affiliations and expertise
Universiti Tunku, MalaysiaRead Artificial Intelligence and Multimodal Signal Processing in Human-Machine Interaction on ScienceDirect