Electroencephalogram-based emotion assessment system using ontology and data mining techniques

Chen, Jing; Hu, Bin; Moore, Philip; Zhang, Xiaowei; Ma, Xu

doi:10.1016/j.asoc.2015.01.007

Cited by 110 publications

(48 citation statements)

References 54 publications

(82 reference statements)

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“…The computational methods to extract and classify emotional features from EEG are summarized in Table 5. Artificial Neural Network (ANN) 7 [105,176,190,204,216,223,227] It is noteworthy to mention that a single feature extraction technique is not optimal across all of the applications. Besides, existing signals are not enough for high accuracy feature extraction.…”

Section: Eeg Correlates Of Emotion (Signals)mentioning

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: Eeg Correlates Of Emotion (Signals)mentioning

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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“…Important work is being carried out using electroencephalography (EEG) [28] and Functional magnetic resonance imaging (fMRI) scanning (see: [27,37]. The small scale research studies have reported significant results in the medical domain, for example see [27,34,[38][39][40].…”

Section: Research Into the Human Brainmentioning

confidence: 99%

“…CAS are designed to target service provision to individuals and entities [2] however the capability to effectively utilize the potential range of CI has been historically limited [2,26] with the domains typically addressed being generally limited to simple recommender systems providing for example tourist information [2,7]. Thus, the development of CAS may be considered to be at an embryonic stage and while an increasing range and scope of data and information is being used in context-aware systems (e.g., see [27,28], emotion (more accurately stated a emotive response) has not been effectively addressed [26] and AC in CAS presents opportunities to implement emotion in intelligent CAS.…”

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confidence: 99%

Do We Understand the Relationship between Affective Computing, Emotion and Context-Awareness?

Moore

2017

Machines

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Historically, the utilization of context, the range and scope of context-aware systems, and the levels of computational intelligence in such systems have been very limited. While the inherent complexity is a significant factor, a principal reason for these limitations lies in the failure to incorporate the emotional component. Affective computing technologies are designed to implement innate emotional capabilities and the capability to simulate emotions and empathy; thus, intelligent context-aware systems with affective computing provide a basis upon which we may effectively enable the emotional component. Moreover, machine cognition relies upon affective computing technologies to provide a basis upon which the emotional component may be incorporated. This paper poses the question: do we understand the relationship between affective computing, emotion and context-awareness? The conclusion drawn is that while affective computing and the need for the incorporation of the emotional component is generally understood and domain-specific strategies to enable implementation have been proposed, there remain important challenges and open research questions in relation to the cognitive modelling and the effective incorporation of affective computing and the emotional component in intelligent context-aware systems.

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“…Up to now, researchers and neuroscientists have studied continuously to improve the performances of the emotion and atention recognition systems (e.g., [1][2][3][4][5][6][7][8][9][10]). In spite of all of these eforts, there is still an abundance of scope for the additional researches in emotion and atention recognition based on biological signals and images.…”

Section: Emotion and Atention Recognition Based On Biological Signalsmentioning

confidence: 99%

Introductory Chapter: Emotion and Attention Recognition Based on Biological Signals and Images

Hosseini¹

2017

Emotion and Attention Recognition Based on Biological Signals and Images

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Electroencephalogram-based emotion assessment system using ontology and data mining techniques

Cited by 110 publications

References 54 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

Do We Understand the Relationship between Affective Computing, Emotion and Context-Awareness?

Introductory Chapter: Emotion and Attention Recognition Based on Biological Signals and Images

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