Szczegóły publikacji
Opis bibliograficzny
EEG – based emotion recognition using convolutional neural networks / Maria Mamica, Paulina Kapłon, Paweł JEMIOŁO // W: Computational Science – ICCS 2021 : 21st International Conference : Krakow, Poland, June 16–18, 2021 : proceedings, Pt. 5 / eds. Maciej Paszyński, [et al.]. — Cham : Springer, cop. 2021. — (Lecture Notes in Computer Science ; ISSN 0302-9743 ; LNCS 12746. Theoretical Computer Science and General Issues ; ISSN 0302-9743). — ISBN: 978-3-030-77976-4; e-ISBN: 978-3-030-77977-1. — S. 84–90. — Bibliogr., Abstr. — Publikacja dostępna online od: 2021-06-09
Autorzy (3)
Słowa kluczowe
Dane bibliometryczne
| ID BaDAP | 134669 |
|---|---|
| Data dodania do BaDAP | 2021-06-22 |
| DOI | 10.1007/978-3-030-77977-1_7 |
| Rok publikacji | 2021 |
| Typ publikacji | materiały konferencyjne (aut.) |
| Otwarty dostęp |  |
| Wydawca | Springer |
| Konferencja | 21st International Conference on Computational Science |
| Czasopisma/serie | Lecture Notes in Computer Science, Theoretical Computer Science and General Issues |
Abstract
In this day and age, Electroencephalography-based methods for Automated Affect Recognition are becoming more and more popular. Owing to the vast amount of information gathered in EEG signals, such methods provide satisfying results in terms of Affective Computing. In this paper, we replicated and improved the CNN-based method proposed by Li et al. [11]. We tested our model using a Dataset for Emotion Analysis using EEG, Physiological and Video Signals (DEAP) [9]. Performed changes in the data preprocessing and in the model architecture led to an increase in accuracy – 74.37% for valence, 73.74% for arousal.