An Integrated Approach to Emotion Recognition for Advanced Emotional Intelligence

Bamidis, Panagiotis D.; Frantzidιs, Christos A.; Konstantinidis, Evdokimos I.; Luneski, Andrej; Lithari, Chrysa; Klados, Manousos A.; Bratsas, Charalampos; Papadelis, Christos; Pappas, C.

doi:10.1007/978-3-642-02580-8_62

Cited by 19 publications

(12 citation statements)

References 14 publications

(17 reference statements)

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“…Most EEG-based emotion recognition studies employ external stimulus presentations to elicit emotional states in their participants using emotion-laden pictures (e.g., utilizing the International Affective Picture System [5,6]), sounds [7], movie clips [8,9], or music [10]. In contrast, we are here concerned with inwardly imagined and felt emotions elicited by the participants' own imagination or recall of emotionally loaded memories, an experiment design and data set reported in [11].…”

Section: Introductionmentioning

confidence: 98%

Emotion Recognition from EEG during Self-Paced Emotional Imagery

Kothe

Makeig

Onton

2013

2013 Humaine Association Conference on Affective Computing and Intelligent Interaction

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Here we present an analysis of a 12-subject electroencephalographic (EEG) data set in which participants were asked to engage in prolonged, self-paced episodes of guided emotion imagination with eyes closed. Our goal is to correctly predict, given a short EEG segment, whether the participant was imagining a positive respectively negative-valence emotional scenario during the given segment using a predictive model learned via machine learning. The challenge lies in generalizing to novel (i.e., previously unseen) emotion episodes from a wide variety of scenarios including love, awe, frustration, anger, etc. based purely on spontaneous oscillatory EEG activity without stimulus event-locked responses. Using a variant of the FilterBank Common Spatial Pattern algorithm, we achieve an average accuracy of 71.3% correct classification of binary valence rating across 12 different emotional imagery scenarios under rigorous block-wise cross-validation.

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Section: Introductionmentioning

confidence: 98%

Emotion Recognition from EEG during Self-Paced Emotional Imagery

Kothe

Makeig

Onton

2013

2013 Humaine Association Conference on Affective Computing and Intelligent Interaction

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“…However, particular emphasis should also been given to the user interfaces which are used for the interaction with the system. Regarding the fact of facing difficulties with the technological devices, software developers should adopt the new advances in the human-computer interaction field in order to get it closer to the one between two human beings [13]. Therefore, avatars able to change their facial expressions and to adapt their behavior according to the user's mood may be adopted in order to facilitate elderly people to interact successfully with the technological platforms utilized to support their independent living.…”

Section: Discussionmentioning

confidence: 99%

“…However, except from the memory enhancement other cognitive processes should also be taken into consideration. Among them, the emotional processing is one of particular importance and directly related with the overall cognitive performance [29] and with the person's ability to interact with other people [13]. Moreover, the use of neurophysiological recordings from both the central (EEG) and the autonomic (electrodermal activity) nervous system is expected to provide more reliable indices of the user's mental health status, while it may form a validation method for evaluating the performance of the cognitive training provided by the LLM technological platform.…”

Section: Discussionmentioning

confidence: 99%

“…Then, pattern recognition and data mining techniques could be integrated in order to dynamically process and analyze the collected data in order to extract the subject's mental state [10] or arousal level [11]. The results may be described in a structured way through XML format [12] in order to adjust the avatar's facial expressions and its speech according to the situation the user faces [13]. The aim of this paper is to review the technological advances and solutions utilized for supporting senior citizens according to their needs as well as with the recent social trends of modern communities.…”

Section: Introductionmentioning

confidence: 99%

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Description and future trends of ICT solutions offered towards independent living

Frantzidιs

Bamidis

2009

Proceedings of the 2nd International Conference on PErvasive Technologies Related to Assistive Environments

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Due to the increasing elderly population, healthcare systems able to promote independent living are regarded as an emerging market. Despite the significant advances aiming to support senior citizens, several issues have to be addressed in order to enable older people to live independently and to form living cells of our society. Aiming to contribute to the research of elderly care, this paper reports the open issues that should be taken into consideration in order to provide a technological solution towards Ambient Assistive Living (AAL). After reviewing the most prominent European funded projects, it presents an example of integrated elderly healthcare platform and finally attempts to highlight the future trends which should be employed in order to face with success the social and financial phenomenon of ageing population.

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“…However, there is strong evidence that Central Nervous System recorded characteristics can be reliable markers for this procedure [14], [15]. Brain oscillations and synchronization of these oscillations have theoretically and empirically linked to the dynamic organization of communication in the nervous system.…”

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confidence: 99%

Human centered computing for the development of assistive environments

Maglogiannis

2014

Proceedings of the 7th International Conference on PErvasive Technologies Related to Assistive Environments

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The paper presents the research conducted within the framework of the STHENOS project (www.sthenos.gr), which aims at the development of methodologies and systems for assistive environments. The research within the project aims at the development of methodologies and tools to compose pervasive human-centered systems, which will be able to understand the human state (identity, emotions and behavior) in assistive environments using audiovisual and biological signals. The proposed systems and applications are capable of offering services such as support for the aged/disabled/chronic patients, detection of critical situations from audiovisual content, biosignal and neurophysiology analysis for the detection of pathology (e.g. Alzheimer's disease), as well as for treatment follow-up. The paper includes an overview of the three perspectives of human centered computing studied in the STHENOS project, namely: audiovisual activity and status recognition, affective computing and neurophysiological analysis.

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An Integrated Approach to Emotion Recognition for Advanced Emotional Intelligence

Cited by 19 publications

References 14 publications

Emotion Recognition from EEG during Self-Paced Emotional Imagery

Emotion Recognition from EEG during Self-Paced Emotional Imagery

Description and future trends of ICT solutions offered towards independent living

Human centered computing for the development of assistive environments

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