An Eeg Based Emotion Recognition And Classification Using Machine Learning Techniques
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An Eeg Based Emotion Recognition And Classification Using Machine Learning Techniques
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Author : Akalya devi C
language : en
Publisher:
Release Date : 2019
An Eeg Based Emotion Recognition And Classification Using Machine Learning Techniques written by Akalya devi C and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 2019 with categories.
Emotions are complex phenomena that play significant roles in the quality of human life. Emotion plays a major role in motivation, perception, cognition, creativity, attention, learning and decision-making. A major problem in understanding emotion is the assessment of the definition of emotions. According to the WHO, every year, almost one million people die from suicide. Suicide is a leading cause of death among teenagers and adults. Existing techniques uses simple keyword search method to find emotional content in blog data and identify bloggers at risk of suicide. However, Deep sentiment analysis in suicide notes has not yet been explored much with computational approaches using advanced Machine Learning and Natural Language Processing techniques. The main contribution of the proposed work employs Electroencephalography (EEG) based psychological states for initializing the parameter weights of the neural network, which is crucial to train an accurate model while avoiding the need to inject any additional features. The Synchronized brainwave dataset contains electroencephalogram (EEG) signal values and details of the patient. The proposed methodology using Machine learning techniques to detect emotion will help individuals, industry, educational institution and Government organization to take decisions and helps people to be more comfortable in expressing their problems.
Foundations Of Augmented Cognition
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Author : Dylan D. Schmorrow
language : en
Publisher: Springer
Release Date : 2013-07-10
Foundations Of Augmented Cognition written by Dylan D. Schmorrow and has been published by Springer this book supported file pdf, txt, epub, kindle and other format this book has been release on 2013-07-10 with Computers categories.
This book constitutes the refereed proceedings of the 5th International Conference on Augmented Cognition, AC 2013, held as part of the 15th International Conference on Human-Computer Interaction, HCII 2013, held in Las Vegas, USA in July 2013, jointly with 12 other thematically similar conferences. The total of 1666 papers and 303 posters presented at the HCII 2013 conferences was carefully reviewed and selected from 5210 submissions. These papers address the latest research and development efforts and highlight the human aspects of design and use of computing systems. The papers accepted for presentation thoroughly cover the entire field of human-computer interaction, addressing major advances in knowledge and effective use of computers in a variety of application areas. The total of 81 contributions was carefully reviewed and selected for inclusion in the AC proceedings. The papers are organized in the following topical sections: augmented cognition in training and education; team cognition; brain activity measurement; understanding and modeling cognition; cognitive load, stress and fatigue; applications of augmented cognition.
Emotion And Stress Recognition Related Sensors And Machine Learning Technologies
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Author : Kyandoghere Kyamakya
language : en
Publisher: MDPI
Release Date : 2021-09-01
Emotion And Stress Recognition Related Sensors And Machine Learning Technologies written by Kyandoghere Kyamakya and has been published by MDPI this book supported file pdf, txt, epub, kindle and other format this book has been release on 2021-09-01 with Technology & Engineering categories.
This book includes impactful chapters which present scientific concepts, frameworks, architectures and ideas on sensing technologies and machine learning techniques. These are relevant in tackling the following challenges: (i) the field readiness and use of intrusive sensor systems and devices for capturing biosignals, including EEG sensor systems, ECG sensor systems and electrodermal activity sensor systems; (ii) the quality assessment and management of sensor data; (iii) data preprocessing, noise filtering and calibration concepts for biosignals; (iv) the field readiness and use of nonintrusive sensor technologies, including visual sensors, acoustic sensors, vibration sensors and piezoelectric sensors; (v) emotion recognition using mobile phones and smartwatches; (vi) body area sensor networks for emotion and stress studies; (vii) the use of experimental datasets in emotion recognition, including dataset generation principles and concepts, quality insurance and emotion elicitation material and concepts; (viii) machine learning techniques for robust emotion recognition, including graphical models, neural network methods, deep learning methods, statistical learning and multivariate empirical mode decomposition; (ix) subject-independent emotion and stress recognition concepts and systems, including facial expression-based systems, speech-based systems, EEG-based systems, ECG-based systems, electrodermal activity-based systems, multimodal recognition systems and sensor fusion concepts and (x) emotion and stress estimation and forecasting from a nonlinear dynamical system perspective. This book, emerging from the Special Issue of the Sensors journal on “Emotion and Stress Recognition Related Sensors and Machine Learning Technologies” emerges as a result of the crucial need for massive deployment of intelligent sociotechnical systems. Such technologies are being applied in assistive systems in different domains and parts of the world to address challenges that could not be addressed without the advances made in these technologies.
Signal Processing And Machine Learning Methods With Applications In Eeg Based Emotion Recognition
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Author : Laura Piho
language : en
Publisher:
Release Date : 2019
Signal Processing And Machine Learning Methods With Applications In Eeg Based Emotion Recognition written by Laura Piho and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 2019 with categories.
Deep Learning Techniques Applied To Affective Computing
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Author : Zhen Cui
language : en
Publisher: Frontiers Media SA
Release Date : 2023-06-14
Deep Learning Techniques Applied To Affective Computing written by Zhen Cui and has been published by Frontiers Media SA this book supported file pdf, txt, epub, kindle and other format this book has been release on 2023-06-14 with Science categories.
Affective computing refers to computing that relates to, arises from, or influences emotions. The goal of affective computing is to bridge the gap between humans and machines and ultimately endow machines with emotional intelligence for improving natural human-machine interaction. In the context of human-robot interaction (HRI), it is hoped that robots can be endowed with human-like capabilities of observation, interpretation, and emotional expression. The research on affective computing has recently achieved extensive progress with many fields contributing including neuroscience, psychology, education, medicine, behavior, sociology, and computer science. Current research in affective computing concentrates on estimating human emotions through different forms of signals such as speech, face, text, EEG, fMRI, and many others. In neuroscience, the neural mechanisms of emotion are explored by combining neuroscience with the psychological study of personality, emotion, and mood. In psychology and philosophy, emotion typically includes a subjective, conscious experience characterized primarily by psychophysiological expressions, biological reactions, and mental states. The multi-disciplinary features of understanding “emotion” result in the fact that inferring the emotion of humans is definitely difficult. As a result, a multi-disciplinary approach is required to facilitate the development of affective computing. One of the challenging problems in affective computing is the affective gap, i.e., the inconsistency between the extracted feature representations and subjective emotions. To bridge the affective gap, various hand-crafted features have been widely employed to characterize subjective emotions. However, these hand-crafted features are usually low-level, and they may hence not be discriminative enough to depict subjective emotions. To address this issue, the recently-emerged deep learning (also called deep neural networks) techniques provide a possible solution. Due to the used multi-layer network structure, deep learning techniques are capable of learning high-level contributing features from a large dataset and have exhibited excellent performance in multiple application domains such as computer vision, signal processing, natural language processing, human-computer interaction, and so on. The goal of this Research Topic is to gather novel contributions on deep learning techniques applied to affective computing across the diverse fields of psychology, machine learning, neuroscience, education, behavior, sociology, and computer science to converge with those active in other research areas, such as speech emotion recognition, facial expression recognition, Electroencephalogram (EEG) based emotion estimation, human physiological signal (heart rate) estimation, affective human-robot interaction, multimodal affective computing, etc. We welcome researchers to contribute their original papers as well as review articles to provide works regarding the neural approach from computation to affective computing systems. This Research Topic aims to bring together research including, but not limited to: • Deep learning architectures and algorithms for affective computing tasks such as emotion recognition from speech, face, text, EEG, fMRI, and many others. • Explainability of deep Learning algorithms for affective computing. • Multi-task learning techniques for emotion, personality and depression detection, etc. • Novel datasets for affective computing • Applications of affective computing in robots, such as emotion-aware human-robot interaction and social robots, etc.
Bridging The Gap Between Machine Learning And Affective Computing
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Author : Zhen Cui
language : en
Publisher: Frontiers Media SA
Release Date : 2023-01-05
Bridging The Gap Between Machine Learning And Affective Computing written by Zhen Cui and has been published by Frontiers Media SA this book supported file pdf, txt, epub, kindle and other format this book has been release on 2023-01-05 with Science categories.
Affective computing refers to computing that relates to, arises from, or influences emotions, as pioneered by Rosalind Picard in 1995. The goal of affective computing is to bridge the gap between human and machines and ultimately enable robots to communicate with human naturally and emotionally. Recently, the research on affective computing has gained considerable progress with many fields contributing including neuroscience, psychology, education, medicine, behavior, sociology, and computer science. Current research in affective computing mainly focuses on estimating of human emotions through different forms of signals, e.g., face video, EEG, Speech, PET scans or fMRI. Inferring the emotion of humans is difficult, as emotion is a subjective, unconscious experience characterized primarily by psycho-physiological expressions and biological reactions. It is influenced by hormones and neurotransmitters such as dopamine, noradrenaline, serotonin, oxytocin, GABA… etc. The physiology of emotion is closely linked to arousal of the nervous system with various states and strengths relating, apparently, to different particular emotions. To understand “emotion” or “affect” merely by machine learning or big data analysis is not enough, but the understanding and applications from the intrinsic features of emotions from the neuroscience aspect is essential.
An Exploration Of Eeg Based Non Stationary Emotion Classification For Affective Computing
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Author : Nicole Bendrich
language : en
Publisher:
Release Date : 2020
An Exploration Of Eeg Based Non Stationary Emotion Classification For Affective Computing written by Nicole Bendrich and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 2020 with categories.
The monitoring of emotional state is important in the prevention and management of mental health problems and is increasingly being used to support affective computing. Researchers are exploring various modalities from which emotion can be inferred, such as through facial images or via electroencephalography (EEG) signals. Current research commonly investigates the performance of machine-learning-based emotion recognition systems by exposing users to short films that are assumed to elicit a single known emotional response. Assuming static emotions, even for these brief periods, however, does not consider that emotions evolve. Moreover, in order to demonstrate better results, many existing models are not tested in ways that reflect realistic real-world implementations. In this thesis, the dynamic evolution of emotions induced using longer and variable stimuli is explored using EEG signals from the publicly available dataset, AMIGOS. A variety of feature engineering and selection techniques are applied and evaluated across four different cross-validation frameworks. The role of imperfect labelling of ground truth emotions and both data and gender-imbalances in the dataset are also investigated. Improved feature design and selection lead to up to 13% absolute improvement relative to comparable previously reported studies using this dataset. Alternative training configurations and a selective confidence-based classification scheme are proposed, leading to further possible improvements.
Artificial Intelligence Enabled Signal Processing Based Models For Neural Information Processing
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Author : Rajesh Kumar Tripathy
language : en
Publisher: CRC Press
Release Date : 2024-06-06
Artificial Intelligence Enabled Signal Processing Based Models For Neural Information Processing written by Rajesh Kumar Tripathy and has been published by CRC Press this book supported file pdf, txt, epub, kindle and other format this book has been release on 2024-06-06 with Technology & Engineering categories.
The book provides details regarding the application of various signal processing and artificial intelligence-based methods for electroencephalography data analysis. It will help readers in understanding the use of electroencephalography signals for different neural information processing and cognitive neuroscience applications. The book: Covers topics related to the application of signal processing and machine learning-based techniques for the analysis and classification of electroencephalography signals Presents automated methods for detection of neurological disorders and other applications such as cognitive task recognition, and brain-computer interface Highlights the latest machine learning and deep learning methods for neural signal processing Discusses mathematical details for the signal processing and machine learning algorithms applied for electroencephalography data analysis Showcases the detection of dementia from electroencephalography signals using signal processing and machine learning-based techniques It is primarily written for senior undergraduates, graduate students, and researchers in the fields of electrical engineering, electronics and communications engineering, and biomedical engineering.
Deep Learning Approaches To Cloud Security
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Author : Pramod Singh Rathore
language : en
Publisher: John Wiley & Sons
Release Date : 2022-01-26
Deep Learning Approaches To Cloud Security written by Pramod Singh Rathore and has been published by John Wiley & Sons this book supported file pdf, txt, epub, kindle and other format this book has been release on 2022-01-26 with Technology & Engineering categories.
DEEP LEARNING APPROACHES TO CLOUD SECURITY Covering one of the most important subjects to our society today, cloud security, this editorial team delves into solutions taken from evolving deep learning approaches, solutions allowing computers to learn from experience and understand the world in terms of a hierarchy of concepts, with each concept defined through its relation to simpler concepts. Deep learning is the fastest growing field in computer science. Deep learning algorithms and techniques are found to be useful in different areas like automatic machine translation, automatic handwriting generation, visual recognition, fraud detection, and detecting developmental delay in children. However, applying deep learning techniques or algorithms successfully in these areas needs a concerted effort, fostering integrative research between experts ranging from diverse disciplines from data science to visualization. This book provides state of the art approaches of deep learning in these areas, including areas of detection and prediction, as well as future framework development, building service systems and analytical aspects. In all these topics, deep learning approaches, such as artificial neural networks, fuzzy logic, genetic algorithms, and hybrid mechanisms are used. This book is intended for dealing with modeling and performance prediction of the efficient cloud security systems, thereby bringing a newer dimension to this rapidly evolving field. This groundbreaking new volume presents these topics and trends of deep learning, bridging the research gap, and presenting solutions to the challenges facing the engineer or scientist every day in this area. Whether for the veteran engineer or the student, this is a must-have for any library. Deep Learning Approaches to Cloud Security: Is the first volume of its kind to go in-depth on the newest trends and innovations in cloud security through the use of deep learning approaches Covers these important new innovations, such as AI, data mining, and other evolving computing technologies in relation to cloud security Is a useful reference for the veteran computer scientist or engineer working in this area or an engineer new to the area, or a student in this area Discusses not just the practical applications of these technologies, but also the broader concepts and theory behind how these deep learning tools are vital not just to cloud security, but society as a whole Audience: Computer scientists, scientists and engineers working with information technology, design, network security, and manufacturing, researchers in computers, electronics, and electrical and network security, integrated domain, and data analytics, and students in these areas
Handbook Of Neuroengineering
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Author : Nitish V. Thakor
language : en
Publisher: Springer Nature
Release Date : 2023-02-02
Handbook Of Neuroengineering written by Nitish V. Thakor and has been published by Springer Nature this book supported file pdf, txt, epub, kindle and other format this book has been release on 2023-02-02 with Technology & Engineering categories.
This Handbook serves as an authoritative reference book in the field of Neuroengineering. Neuroengineering is a very exciting field that is rapidly getting established as core subject matter for research and education. The Neuroengineering field has also produced an impressive array of industry products and clinical applications. It also serves as a reference book for graduate students, research scholars and teachers. Selected sections or a compendium of chapters may be used as “reference book” for a one or two semester graduate course in Biomedical Engineering. Some academicians will construct a “textbook” out of selected sections or chapters. The Handbook is also meant as a state-of-the-art volume for researchers. Due to its comprehensive coverage, researchers in one field covered by a certain section of the Handbook would find other sections valuable sources of cross-reference for information and fertilization of interdisciplinary ideas. Industry researchers as well as clinicians using neurotechnologies will find the Handbook a single source for foundation and state-of-the-art applications in the field of Neuroengineering. Regulatory agencies, entrepreneurs, investors and legal experts can use the Handbook as a reference for their professional work as well.