Inferring Students’ Emotions Using a Hybrid Approach that Combine Cognitive and Physical Data

Gottardo, Ernani; Pimentel, Andrey Ricardo

doi:10.1007/978-3-030-26169-6_14

Cited by 1 publication

(1 citation statement)

References 29 publications

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“…Valence is an individual's reaction toward an event, while arousal is the excitement to behave accordingly or express the feeling [3]. A combination of arousal and valence labels is categorized into four quadrants: the 1 st represents high arousal and positive valence (HAPV), 2 nd depicts high arousal and negative valence (HANV), 3 rd implies low arousal and negative valence (LANV), while the 4 th describes low arousal and positive valence (LAPV) [4,5]. The emotional reactions in each quadrant represent mental health and human performance [6].…”

Section: -Introductionmentioning

confidence: 99%

Continuous Capsule Network Method for Improving Electroencephalogram-Based Emotion Recognition

Wirawan

Wardoyo²,

Lelono³

et al. 2022

Emerg Sci J

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The convolution process in the Capsule Network method can result in a loss of spatial data from the Electroencephalogram signal, despite its ability to characterize spatial information from Electroencephalogram signals. Therefore, this study applied the Continuous Capsule Network method to overcome problems associated with emotion recognition based on Electroencephalogram signals using the optimal architecture of the (1) 1st, 2nd, 3rd, and 4th Continuous Convolution layers with values of 64, 128, 256, and 64, respectively, and (2) kernel sizes of 2×2×4, 2×2×64, and 2×2×128 for the 1st, 2nd, and 3rd Continuous Convolution layers, and 1×1×256 for the 4th. Several methods were also used to support the Continuous Capsule Network process, such as the Differential Entropy and 3D Cube methods for the feature extraction and representation processes. These methods were chosen based on their ability to characterize spatial and low-frequency information from Electroencephalogram signals. By testing the DEAP dataset, these proposed methods achieved accuracies of 91.35, 93.67, and 92.82% for the four categories of emotions, two categories of arousal, and valence, respectively. Furthermore, on the DREAMER dataset, these proposed methods achieved accuracies of 94.23, 96.66, and 96.05% for the four categories of emotions, the two categories of arousal, and valence, respectively. Finally, on the AMIGOS dataset, these proposed methods achieved accuracies of 96.20, 97.96, and 97.32% for the four categories of emotions, the two categories of arousal, and valence, respectively. Doi: 10.28991/ESJ-2023-07-01-09 Full Text: PDF

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Section: -Introductionmentioning

confidence: 99%