Neural Correlates of Phrase Quadrature Perception in Harmonic Rhythm: An EEG Study Using a Brain–Computer Interface

Fernández-Soto, Alicia; Martínez-Rodrigo, Arturo; Moncho-Bogani, José; Postigo, José Miguel Latorre; Fernández-Caballero, Antonio

doi:10.1142/s012906571750054x

Cited by 17 publications

(13 citation statements)

References 38 publications

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“…EEG connectivity features were reported to play an essential role in the recognition tasks [37]. Another study, using the same device, observed a correlation of EEG with classical music rhythm [38], [39]. Music therapy is one of the applications emanating from music-elicited emotion [40].…”

Section: Consumer Grade Eeg For Emotion Recognitionmentioning

confidence: 99%

Consumer Grade Brain Sensing for Emotion Recognition

et al. 2019

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For several decades, electroencephalography (EEG) has featured as one of the most commonly used tools in emotional state recognition via monitoring of distinctive brain activities. An array of datasets have been generated with the use of diverse emotion-eliciting stimuli and the resulting brainwave responses conventionally captured with high-end EEG devices. However, the applicability of these devices is to some extent limited by practical constraints and may prove difficult to be deployed in highly mobile context omnipresent in everyday happenings. In this study, we evaluate the potential of OpenBCI to bridge this gap by first comparing its performance to research grade EEG system, employing the same algorithms that were applied on benchmark datasets. Moreover, for the purpose of emotion classification, we propose a novel method to facilitate the selection of audio-visual stimuli of high/low valence and arousal. Our setup entailed recruiting 200 healthy volunteers of varying years of age to identify the top 60 affective video clips from a total of 120 candidates through standardized self assessment, genre tags, and unsupervised machine learning. Additional 43 participants were enrolled to watch the pre-selected clips during which emotional EEG brainwaves and peripheral physiological signals were collected. These recordings were analyzed and extracted features fed into a classification model to predict whether the elicited signals were associated with a high or low level of valence and arousal. As it turned out, our prediction accuracies were decidedly comparable to those of previous studies that utilized more costly EEG amplifiers for data acquisition.

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Section: Consumer Grade Eeg For Emotion Recognitionmentioning

confidence: 99%

Consumer Grade Brain Sensing for Emotion Recognition

et al. 2019

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“…Thus, we expect that parameters or measures with different origins are considered in a multivariate way [ 1 , 4 ]. Furthermore, recent developments in sensor technology enabling routine recordings of multi-channel signals have led to an increasing popularity of this kind of analysis on physiological data [ 1 , 2 , 3 , 5 , 6 ].…”

Section: Introductionmentioning

confidence: 99%

Multivariate Multiscale Dispersion Entropy of Biomedical Times Series

Azami

Fernández

Escudero

2019

Entropy

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Objective: Due to the non-linearity of numerous biomedical signals, non-linear analysis of multi-channel time series, notably multivariate multiscale entropy (mvMSE), has been extensively used in biomedical signal processing. However, mvMSE has three drawbacks: 1) mvMSE values are either undefined or unreliable for short signals; 2) mvMSE is not fast enough for real-time applications; and 3) the computation of mvMSE for signals with a large number of channels requires the storage of a huge number of elements. Methods: To deal with these problems and improve the stability of mvMSE, we introduce multivariate multiscale dispersion entropy (MDE -mvMDE), as an extension of our recently developed MDE, to quantify the complexity of multivariate time series. Results: We assess mvMDE, in comparison with mvMSE and multivariate multiscale fuzzy entropy (mvMFE), on correlated and uncorrelated multichannel noise signals, bivariate autoregressive processes, and three biomedical datasets. The results show that mvMDE takes into account dependencies in patterns across both the time and spatial domains. The mvMDE, mvMSE, and mvMFE methods are consistent in that they lead to similar conclusions about the underlying physiological conditions. However, the proposed mvMDE discriminates various physiological states of the biomedical recordings better than mvMSE and mvMFE. In addition, for both the short and long time series, the mvMDE-based results are noticeably more stable than the mvMSE-and mvMFE-based ones. Conclusion: For short multivariate time series, mvMDE, unlike mvMSE, does not result in undefined values. Furthermore, mvMDE is noticeably faster than mvMFE and mvMSE and also needs to store a considerably smaller number of elements. Significance: Due to its ability to detect different kinds of dynamics of multivariate signals, mvMDE has great potential to analyse various physiological signals.

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“…This paper introduces arousal detection from EDA signals using musical stimuli. Several studies have reported that using music to elicit emotions is one of the most effective methods of emotion induction [27][28][29][30]. Music plays a key role in most people's lives, frequently being used to explore and regulate emotions.…”

Section: Introductionmentioning

confidence: 99%

Arousal Detection in Elderly People from Electrodermal Activity Using Musical Stimuli

Bartolomé-Tomás

Sánchez-Reolid

Fernández-Sotos³

et al. 2020

Sensors

Self Cite

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The detection of emotions is fundamental in many areas related to health and well-being. This paper presents the identification of the level of arousal in older people by monitoring their electrodermal activity (EDA) through a commercial device. The objective was to recognize arousal changes to create future therapies that help them to improve their mood, contributing to reduce possible situations of depression and anxiety. To this end, some elderly people in the region of Murcia were exposed to listening to various musical genres (flamenco, Spanish folklore, Cuban genre and rock/jazz) that they heard in their youth. Using methods based on the process of deconvolution of the EDA signal, two different studies were carried out. The first, of a purely statistical nature, was based on the search for statistically significant differences for a series of temporal, morphological, statistical and frequency features of the processed signals. It was found that Flamenco and Spanish Folklore presented the highest number of statistically significant parameters. In the second study, a wide range of classifiers was used to analyze the possible correlations between the detection of the EDA-based arousal level compared to the participants’ responses to the level of arousal subjectively felt. In this case, it was obtained that the best classifiers are support vector machines, with 87% accuracy for flamenco and 83.1% for Spanish Folklore, followed by K-nearest neighbors with 81.4% and 81.5% for Flamenco and Spanish Folklore again. These results reinforce the notion of familiarity with a musical genre on emotional induction.

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Neural Correlates of Phrase Quadrature Perception in Harmonic Rhythm: An EEG Study Using a Brain–Computer Interface

Cited by 17 publications

References 38 publications

Consumer Grade Brain Sensing for Emotion Recognition

Consumer Grade Brain Sensing for Emotion Recognition

Multivariate Multiscale Dispersion Entropy of Biomedical Times Series

Arousal Detection in Elderly People from Electrodermal Activity Using Musical Stimuli

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