Singular Spectrum Analysis of Pupillometry Data. Identification of the Sympathetic and Parasympathetic Activity

Franco, Ana; Marques-Neves, Carlos; Quintão, Carla; Vigário, Ricardo; Vieira, Pedro

doi:10.1016/j.protcy.2014.10.237

Cited by 5 publications

(8 citation statements)

References 11 publications

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“…Such cues include eye gaze, eye saccades, pupillometry, fixational eye movements, and various forms of eye opening and closure events. The eyes can provide information on attention [2]- [4], perception [5], [6], social and emotional cues [5]- [10], cognitive load [11], [12], locomotion [2], and mental and physical pathology [3], [9], [10], [13].…”

Section: Introductionmentioning

confidence: 99%

“…Pupillometry studies from psychology showed the pupils to be responsive during emotional arousal [5], [6] and monetary incentive or penalty (therefore exhibiting valence potential) [14] during a memory task. In a neurophysiological study [10], it was demonstrated that the pupils were responsive to This work was supported by the Irish Research Council (Grant No. GOIPG/2018/2030).…”

Section: Introductionmentioning

confidence: 99%

“…Pupillometry studies from psychology showed the pupils to be responsive during emotional arousal [5], [6] and monetary incentive or penalty (therefore exhibiting valence potential) [14] during a memory task. In a neurophysiological study [10], it was demonstrated that the pupils were responsive to autonomic nervous system stimulation, which is known to generate response output under numerous emotional states [15]. Neuropsychological evidence has been provided for pupilliary responses to reward expectation (positive valence event) [9].…”

Section: Introductionmentioning

confidence: 99%

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Eye-based Continuous Affect Prediction

O'Dwyer

Murray

Flynn

2019

2019 8th International Conference on Affective Computing and Intelligent Interaction (ACII)

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Eye-based information channels include the pupils, gaze, saccades, fixational movements, and numerous forms of eye opening and closure. Pupil size variation indicates cognitive load and emotion, while a person's gaze direction is said to be congruent with the motivation to approach or avoid stimuli. The eyelids are involved in facial expressions that can encode basic emotions. Additionally, eye-based cues can have implications for human annotators of emotions or feelings. Despite these facts, the use of eye-based cues in affective computing is in its infancy, however, and this work is intended to start to address this. Eye-based feature sets, incorporating data from all of the aforementioned information channels, that can be estimated from video are proposed. Feature set refinement is provided by way of continuous arousal and valence learning and prediction experiments on the RECOLA validation set. The eye-based features are then combined with a speech feature set to provide confirmation of their usefulness and assess affect prediction performance compared with group-of-humans-level performance on the RECOLA test set. The core contribution of this paper, a refined eye-based feature set, is shown to provide benefits for affect prediction. It is hoped that this work stimulates further research into eye-based affective computing.

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Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

“…Pupillometry studies from psychology showed the pupils to be responsive during emotional arousal [5], [6] and monetary incentive or penalty (therefore exhibiting valence potential) [14] during a memory task. In a neurophysiological study [10], it was demonstrated that the pupils were responsive to autonomic nervous system stimulation, which is known to generate response output under numerous emotional states [15]. Neuropsychological evidence has been provided for pupilliary responses to reward expectation (positive valence event) [9].…”

Section: Introductionmentioning

confidence: 99%

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Eye-based Continuous Affect Prediction

O'Dwyer

Murray

Flynn

2019

2019 8th International Conference on Affective Computing and Intelligent Interaction (ACII)

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“…Negative correlation between blink rates and pupillometry in high attention has been found as can be seen on fig 7. In this study, pupillometry in spectral and temporal analysis has been calculated. Spectral analysis also has been done by [24]. That research mentioned that pupillometry area associated with the sympathetic (SNS) and parasympathetic autonomus nervous system (PsNS).…”

Section: Discussionmentioning

confidence: 99%

Quantitative Formula of Blink Rates-Pupillometry for Attention Level Detection in Supervised Machine Learning

Zennifa

Iramina

2019

IEEE Access

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Attention is a condition when someone concentrates on a specific task while ignoring other perceivable information. Numerous methods of attention level detection such as observation, self-assessment, and objective performance have been applied especially in supervised machine learning. But those methods tend to be delayed, sporadic, not at the moment in time, and based on participant cognitive ability. This study proposed a new labeling method for attention level detection by using quantitative evaluation formula based on blink rates and pupillometry. Comparison in error detection between self-assessment, observation, and objective performance has been done in this study. After that, this study investigated the effect of attention level based on self-assessment toward blink rates and pupillometry. The result shown blink rates in low attention is higher than high attention. On the other hand, pupillometry in low attention is smaller than high attention. The effect of attention levels toward pupillometry and blink rates are extracted into several algorithms. The result from experimental procedure shown quantitative evaluation formula has percentage error less than 15% compared with self-assessment. Overall, these results demonstrated that the proposed method can be used to be data labeling for other physiological signals such as electroencephalograph (EEG), electrocardiograph (ECG), and near-infrared spectroscopy (NIRS). After that, this quantitative formula was applied to EEG-ECG-NIRS for attention level detection. Two-electrode wireless EEG, a wireless ECG, and two wireless channels NIRS has been used to detect attention level during tasks load. Our result has shown the accuracy system 82.31%. INDEX TERMS Attention, blink rates, pupillometry, supervised machine learning, electroencephalograph (EEG), electrocardiograph (ECG), near-infrared spectroscopy (NIRS).

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“…Eye-based cues have been linked with various emotional displays for a long time [25] and numerous works suggest various eye signals to elucidate one's affective state. The shared signal hypothesis [5], [6] suggests that eye gaze is congruent with emotion expressions when the gaze direction matches the underlying motivation to approach or avoid stimuli while pupil size variation occurs during monetary reward or penalty [26], while viewing emotionally arousing stimuli [27], [28], during reward expectation [29] and during autonomic nervous system stimulation [30]. In affective computing, eyebased cues have been employed for emotion recognition [31], [32].…”

Section: Related Workmentioning

confidence: 99%

Speech, Head, and Eye-based Cues for Continuous Affect Prediction

O'Dwyer

2019

2019 8th International Conference on Affective Computing and Intelligent Interaction Workshops and Demos (ACIIW)

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Continuous affect prediction involves the discrete time-continuous regression of affect dimensions. Dimensions to be predicted often include arousal and valence. Continuous affect prediction researchers are now embracing multimodal model input. This provides motivation for researchers to investigate previously unexplored affective cues. Speech-based cues have traditionally received the most attention for affect prediction, however, non-verbal inputs have significant potential to increase the performance of affective computing systems and in addition, allow affect modelling in the absence of speech. However, nonverbal inputs that have received little attention for continuous affect prediction include eye and head-based cues. The eyes are involved in emotion displays and perception while headbased cues have been shown to contribute to emotion conveyance and perception. Additionally, these cues can be estimated noninvasively from video, using modern computer vision tools. This work exploits this gap by comprehensively investigating head and eye-based features and their combination with speech for continuous affect prediction. Hand-crafted, automatically generated and CNN-learned features from these modalities will be investigated for continuous affect prediction. The highest performing feature sets and feature set combinations will answer how effective these features are for the prediction of an individuals affective state.

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Singular Spectrum Analysis of Pupillometry Data. Identification of the Sympathetic and Parasympathetic Activity

Cited by 5 publications

References 11 publications

Eye-based Continuous Affect Prediction

Eye-based Continuous Affect Prediction

Quantitative Formula of Blink Rates-Pupillometry for Attention Level Detection in Supervised Machine Learning

Speech, Head, and Eye-based Cues for Continuous Affect Prediction

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