Nonlinear Methodologies Applied to Automatic Recognition of Emotions: An EEG Review

García-Martínez, Beatriz; Martínez-Rodrigo, Arturo; Alcaraz, Raúl; Fernández-Caballero, Antonio; González, Pascual

doi:10.1007/978-3-319-67585-5_73

Cited by 15 publications

(9 citation statements)

References 56 publications

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“…In the case of the EDA signal, this marker is a very good indicator of stress [33], allowing, for instance, the use of a support vector machine based approach [34]. EEG signals are another good indicator of the user's emotional state [35][36][37]. For this purpose, non-linear and other approaches based on responses in the different frequency bands can be used [38][39][40].…”

Section: Machine Learning Servicementioning

confidence: 99%

Distributed Architecture for Acquisition and Processing of Physiological Signals

Sánchez-Reolid

García

Vicente-Querol

et al. 2019

13th International Conference on Ubiquitous Computing and Ambient ‪Intelligence UCAmI 2019‬

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The increase in the number of devices equipped with physiological sensors and their low price mean that they can be used in many fields. One of these fields is health-care and home-care for the elderly or people with disabilities. The development of such devices makes it possible to monitor their condition continuously and at all times. A continuous monitoring not only establishes an image of the user’s status, but also detects possible anomalies. Therefore, it is necessary to develop a distributed architecture that allows expert analysts to access the data provided by the sensors at all times and from anywhere. This paper introduces the development and implementation of the concept of distributed architecture, focusing on the minimum requirements needed to carry it out. All the necessary modules are described for different stages: acquisition, communication and processing of physiological signals. The last stage is carried out by a machine learning system. The complete reporting and storage system is also described. Finally, the most important conclusions that have emerged during the development are reported.

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Section: Machine Learning Servicementioning

confidence: 99%

Distributed Architecture for Acquisition and Processing of Physiological Signals

Sánchez-Reolid

García

Vicente-Querol

et al. 2019

13th International Conference on Ubiquitous Computing and Ambient ‪Intelligence UCAmI 2019‬

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“…Since this work has been published, the needs for identifying patterns of information have been emphasised as a heuristic way to manage complex systems [26]. For instance, artificial intelligence and human cognition ("humanised computing") are articulated to create tools either for treating psychological disorders [27] or allowing recognition of psychological entities (emotions) using non-linear relationships [28].…”

Section: The Search For Assessment Indicatorsmentioning

confidence: 99%

Decision-Making in Complex Dynamic and Evolutive Systems: The Need for a New Paradigm

Cadet¹

2018

Decision Making

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For contemporary psychology, decision-making represents behaviours, which are very different from automatic responses. They are developed by implementing integrative cognitive functions adapted to the finalities sought and the situation to treat. Through the diversity of epistemological choices for instance, research in previous decades focused on the individual choices expressed by situations or contexts with a stable structure. The new problems of life in today's society lead to making decisions on societal problems (climate, energy, etc.), which bring into play systems and no longer variables. This chapter has four aspects. After having characterised the decision-making process as a cognitive behaviour (1), having recalled the best known traditional models (those of Economics and Psychology) (2), this chapter deals with the properties of complex systems (globality, interactivity, dynamism, and scalability), which render decision-making difficult (3), and concludes with the necessity of a change of paradigm by pointing to paths to follow (4).

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“…Potentials of these experimental and theoretical approaches still remain elusive. Some recent studies [13][14][15] imply that EEG imaging supplemented by proper nonlinear analysis can detect changes in the emotional states of the brain that play an important role in traversing from the social brain functions to social networking [16].…”

Section: Introductionmentioning

confidence: 99%

“…Currently, a consensus exists among researchers as regards the anatomical brain network [18]. On the other hand, functional brain connectivities can be significantly different from the anatomical structure that underlies them and vary in time and depending on contents (emotional and cognitive) of the processing data [13,15,19]. In this context, the social brain is a kind of functional brain network.…”

Section: Introductionmentioning

confidence: 99%

Origin of Hyperbolicity in Brain-to-Brain Coordination Networks

Tadić¹,

Andjelković²,

Šuvakov³

2018

Front. Phys.

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Hyperbolicity or negative curvature of complex networks is the intrinsic geometric proximity of nodes in the graph metric space, which implies an improved network function. Here, we investigate hidden combinatorial geometries in brain-to-brain coordination networks arising through social communications. The networks originate from correlations among EEG signals previously recorded during spoken communications comprising of 14 individuals with 24 speaker-listener pairs. We find that the corresponding networks are δ-hyperbolic with δ max = 1 and the graph diameter D = 3 in each brain. While the emergent hyperbolicity in the two-brain networks varies satisfying δ max /D/2 ≤ 1 and can be attributed to the topology of the subgraph formed around the cross-brains linking channels. We identify these subgraphs in each studied two-brain network and decompose their structure into simple geometric descriptors (triangles, tetrahedra and cliques of higher orders) that contribute to hyperbolicity. Considering topologies that exceed two separate brain networks as a measure of coordination synergy between the brains, we identify different neural correlation patterns ranging from weak coordination to super-brain structure. These topology features are in qualitative agreement with the listener's self-reported ratings of own experience and quality of the speaker, suggesting that studies of the cross-brain connector networks can reveal new insight into the neural mechanisms underlying human social behavior.

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Nonlinear Methodologies Applied to Automatic Recognition of Emotions: An EEG Review

Cited by 15 publications

References 56 publications

Distributed Architecture for Acquisition and Processing of Physiological Signals

Distributed Architecture for Acquisition and Processing of Physiological Signals

Decision-Making in Complex Dynamic and Evolutive Systems: The Need for a New Paradigm

Origin of Hyperbolicity in Brain-to-Brain Coordination Networks

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