EEG and fNIRS Signal-Based Emotion Identification by Means of Machine Learning Algorithms During Visual Stimuli Exposure
This paper presents the identification of arousal and valence during visual stimuli exposure using electroencephalograms (EEGs) and functional near-infrared spectroscopy (fNIRS) signals. Specifically, various images were shown to several volunteers to evoke different emotions defined by their level...
| Autores: | , , , , |
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| Tipo de recurso: | artículo |
| Fecha de publicación: | 2024 |
| País: | España |
| Institución: | Universidad de Castilla-La Mancha |
| Repositorio: | RUIdeRA. Repositorio Institucional de la UCLM |
| OAI Identifier: | oai:ruidera.uclm.es:10578/40349 |
| Acceso en línea: | https://doi.org/10.3390/electronics13234797 https://hdl.handle.net/10578/40349 |
| Access Level: | acceso abierto |
| Palabra clave: | Brain–computer interface Electroencephalography |
| Sumario: | This paper presents the identification of arousal and valence during visual stimuli exposure using electroencephalograms (EEGs) and functional near-infrared spectroscopy (fNIRS) signals. Specifically, various images were shown to several volunteers to evoke different emotions defined by their level of arousal and valence, such as happiness, sadness, fear, and anger. Brain activity was recorded using the Emotiv EPOC X and NIRSport2 devices separately. The recorded signals were then processed and analyzed to identify the primary brain regions activated during the trials. Next, machine learning methods were employed to classify the evoked emotions with highest accuracy values of 71.3% for EEG data with a Multi-Layer Perceptron (MLP) method and 64.0% for fNIRS data using a Bagging Trees (BAG) algorithm. This approach not only highlights the effectiveness of using EEG and fNIRS technologies but also provides insights into the complex interplay between different brain areas during emotional experiences. By leveraging these advanced acquisition techniques, this study aims to contribute to the broader field of affective neuroscience and improve the accuracy of emotion recognition systems. The findings could have significant implications for developing intelligent systems capable of more empathetic interactions with humans, enhancing applications in areas such as mental health, human–computer interactions, or adaptive learning environments, among others. |
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