Action-perception cycle learning for incremental emotion recognition in a movie clip using 3D fuzzy GIST based on visual and EEG signals

Lee, Giyoung; Kwon, Mingu; Kavuri, Swathi

doi:10.3233/ica-140464

Cited by 24 publications

(3 citation statements)

References 41 publications

(59 reference statements)

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“…A number of design innovation papers (265) were published in the period of 2005-2016. Technical aspects of design and design implications are important, and there are still many technological challenges and obstacles to the growth of EEG-based emotion detection systems.…”

Section: Design Innovation (Experimental) Papermentioning

confidence: 99%

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Review and Classification of Emotion Recognition Based on EEG Brain-Computer Interface System Research: A Systematic Review

et al. 2017

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Abstract:Recent developments and studies in brain-computer interface (BCI) technologies have facilitated emotion detection and classification. Many BCI studies have sought to investigate, detect, and recognize participants' emotional affective states. The applied domains for these studies are varied, and include such fields as communication, education, entertainment, and medicine. To understand trends in electroencephalography (EEG)-based emotion recognition system research and to provide practitioners and researchers with insights into and future directions for emotion recognition systems, this study set out to review published articles on emotion detection, recognition, and classification. The study also reviews current and future trends and discusses how these trends may impact researchers and practitioners alike. We reviewed 285 articles, of which 160 were refereed journal articles that were published since the inception of affective computing research. The articles were classified based on a scheme consisting of two categories: research orientation and domains/applications. Our results show considerable growth of EEG-based emotion detection journal publications. This growth reflects an increased research interest in EEG-based emotion detection as a salient and legitimate research area. Such factors as the proliferation of wireless EEG devices, advances in computational intelligence techniques, and machine learning spurred this growth.

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Section: Design Innovation (Experimental) Papermentioning

confidence: 99%

“…These proposed systems aim to explore or improve EEG-based emotion recognition systems. [2,39,41,42,49,50,57,61,63,92,104,108,109,117,131,136,149,152,157,173,174,185,186,189,191,[195][196][197][198][199][200][201][202][203][204][205][206][207][208][209]217,219,[223][224][225]229,[262][263][264][265][266]<...>…”

Section: Monitoringmentioning

confidence: 99%

Review and Classification of Emotion Recognition Based on EEG Brain-Computer Interface System Research: A Systematic Review

et al. 2017

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“…Machine learning-based approaches are widely utilized in medical applications [16] and have gained significant attention in the past few decades [17].…”

Section: A Machine Learning Based Techniquesmentioning

confidence: 99%

A Data Augmentation-Based Framework to Handle Class Imbalance Problem for Alzheimer’s Stage Detection

et al. 2019

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Alzheimer's Disease (AD) is the most common form of dementia. It gradually increases from mild stage to severe, affecting the ability to perform common daily tasks without assistance. It is a neurodegenerative illness, presently having no specified cure. Computer-Aided Diagnostic Systems have played an important role to help physicians to identify AD. However, the diagnosis of AD into its four stages; No Dementia, Very Mild Dementia, Mild Dementia, and Moderate Dementia remains an open research area. Deep learning assisted computer-aided solutions are proved to be more useful because of their high accuracy. However, the most common problem with deep learning architecture is that large training data is required. Furthermore, the samples should be evenly distributed among the classes to avoid the class imbalance problem. The publicly available dataset (OASIS) has serious class imbalance problem. In this research, we employed a transfer learning-based technique using data augmentation for 3D Magnetic Resonance Imaging (MRI) views from OASIS dataset. The accuracy of the proposed model utilizing a single view of the brain MRI is 98.41% while using 3D-views is 95.11%. The proposed system outperformed the existing techniques for Alzheimer disease stages.

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New method for modal identification of super high-rise building structures using discretized synchrosqueezed wavelet and Hilbert transforms

Park

Adeli

2016

Struct Design Tall Spec Build

131

106

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Summary Measured signals obtained by sensors during dynamic events such as earthquake, wind, and wave contain nonlinear, nonstationary, and noisy properties. In this paper, a new approach is presented for modal parameter identification of structures particularly suitable for very large real‐life structures such as super high‐rise building structures based on the integration of discretized synchrosqueezed wavelet transform, the Hilbert transform, and the linear least‐square fit. Its effectiveness is demonstrated first by application to a two‐dimensional frames from the literature, and then to the 123‐story Lotte World Tower (LWT) under construction in Seoul, Korea. The LWT measurements are very low‐amplitude ambient vibrations. Extracting the natural frequencies and damping ratios from such low‐amplitude signals are known to be very challenging. Further, the new methodology was compared with the empirical mode decomposition. It is demonstrated that the new method is capable of extracting both natural frequencies and damping rations from low‐amplitude signals effectively and with a higher accuracy compared with the empirical mode decomposition approach. The results of this research indicate a super high‐rise building like LWT has a damping ratio in the range 0.7–3.4%. The new method is quite promising for practical implementations of health monitoring of large real‐life structures.

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Action-perception cycle learning for incremental emotion recognition in a movie clip using 3D fuzzy GIST based on visual and EEG signals

Cited by 24 publications

References 41 publications

Review and Classification of Emotion Recognition Based on EEG Brain-Computer Interface System Research: A Systematic Review

Review and Classification of Emotion Recognition Based on EEG Brain-Computer Interface System Research: A Systematic Review

A Data Augmentation-Based Framework to Handle Class Imbalance Problem for Alzheimer’s Stage Detection

New method for modal identification of super high-rise building structures using discretized synchrosqueezed wavelet and Hilbert transforms

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