Titulo:
Entrenamiento en regulación emocional impulsado por neurofeedback en un entorno de realidad virtual: un enfoque de aprendizaje automático utilizando OpenBCI
.
Sumario:
Este artículo aborda el diseño y desarrollo de un sistema avanzado de neurofeedback para el entrenamiento en habilidades y competencias de regulación emocional; el sistema integra una plataforma de Realidad Virtual (VR) con un dispositivo OpenBCI de 16 canales para la captura en tiempo real de señales electroencefalográficas (EEG). El principal objetivo de la investigación radica en la aplicación de algoritmos de aprendizaje automático, concretamente Random Forest y K-Nearest Neighbors (KNN), para la clasificación de estados emocionales en términos de valencia y excitación. Estos algoritmos logran una precisión de hasta el 83% para la clasificación de la excitación y del 90% para la valencia. Las señales de EEG se procesan y clasifican en t... Ver más
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Entrenamiento en regulación emocional impulsado por neurofeedback en un entorno de realidad virtual: un enfoque de aprendizaje automático utilizando OpenBCI Neurofeedback Artículo de revista Entrenamiento Open BCI aprendizaje automatico realidad virtual Entrenamiento en regulación emocional impulsado por neurofeedback en un entorno de realidad virtual: un enfoque de aprendizaje automático utilizando OpenBCI regulación emocional, Este artículo aborda el diseño y desarrollo de un sistema avanzado de neurofeedback para el entrenamiento en habilidades y competencias de regulación emocional; el sistema integra una plataforma de Realidad Virtual (VR) con un dispositivo OpenBCI de 16 canales para la captura en tiempo real de señales electroencefalográficas (EEG). El principal objetivo de la investigación radica en la aplicación de algoritmos de aprendizaje automático, concretamente Random Forest y K-Nearest Neighbors (KNN), para la clasificación de estados emocionales en términos de valencia y excitación. Estos algoritmos logran una precisión de hasta el 83% para la clasificación de la excitación y del 90% para la valencia. Las señales de EEG se procesan y clasifican en tiempo real y los resultados se integran en un entorno de realidad virtual creado en Unity. Este entorno adaptativo cambia según los estados emocionales detectados, permitiendo una regulación más precisa. Además, se ha desarrollado un protocolo de respiración diafragmática dentro del entorno de realidad virtual como estrategia de intervención para la regulación emocional. El sistema se encuentra en su etapa final de prueba para establecer la eficacia del sistema. info:eu-repo/semantics/article Gross, J. J. (2014). Emotion regulation: Conceptual and empirical foundations. En J. J. Gross (Ed.), Handbook of emotion regulation (2ª. ed., pp. 3-20). Guilford Press. Hermann, E. (2022). Neural responses to positive and negative valence: How can valence influence frontal alpha asymmetry? Tilburg University. Honda, S., Ishikawa, Y., Konno, R., Imai, E., Nomiyama, N., Sakurada, K., … Nakatani, M. (2020). Proximal Binaural Sound Can Induce Subjective Frisson. Frontiers in Psychology, 11(March, Article 316), 1–10. https://doi.org/10.3389/fpsyg.2020.00316 Inglés http://creativecommons.org/licenses/by-nc-nd/4.0 This work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License. REFERENCES Adorni, R., Brugnera, A., Gatti, A., Tasca, G. A., Sakatani, K., & Compare, A. (2019). Psychophysiological Responses to Stress Related to Anxiety in Healthy Aging: A Near- Infrared Spectroscopy (NIRS) Study. Journal of Psychophysiology, 33(3), 188–197. https://doi.org/10.1027/0269-8803/a000221 Allen, J. J. B., Coan, J. A., & Nazarian, M. (2004). Issues and assumptions on the road from raw signals to metrics of frontal EEG asymmetry in emotion. Biological Psychology, 67(1–2), 183–218. https://doi.org/10.1016/j.biopsycho.2004.03.007 Drossos, K., Floros, A., & Giannakoulopoulos, A. (2014). BEADS: A dataset of Binaural Emotionally Annotated Digital Sounds. IISA 2014 - 5th International Conference on Information, Intelligence, Systems and Applications, (July), 158–163. Eisenberg, N., Hofer, C. and Vaughan, J. (2007) Effortful control and its socio-emotional consequences. In: Gross, J., Ed., Handbook of Emotion Regulation, 287-306. Hughes, S., & Kearney, G. (2015). Fear and Localisation: Emotional Fine-Tuning Utlising Multiple Source Directions. AES: Journal of the Audio Engineering Society, (56th International Conference, London, UK). Hsu, B. W., & Wang, M. J. J. (2013). Evaluating the effectiveness of using electroencephalogram power indices to measure visual fatigue. Perceptual and Motor Skills, 116(1), 235–252. https://doi.org/10.2466/29.15.24.PMS.116.1.235-252 Katsigiannis, S., & Ramzan, N. (2018). DREAMER: A Database for Emotion Recognition Through EEG and ECG Signals from Wireless Low-cost Off-the-Shelf Devices. IEEE Journal of Biomedical and Health Informatics, 22(1), 98–107. https://doi.org/10.1109/JBHI.2017.2688239 Navea, R. F., & Dadios, E. (2015). Beta/Alpha power ratio and alpha asymmetry characterization of EEG signals due to musical tone stimulation. Project Einstein 2015, (October). Posner, J., Russel, J. A., & Peterson, B. S. (2005). The circumplex model of affect: An integrative approach to affective neuroscience, cognitive development, and psychopathology. National Institude of Health, (17), 715–734. Sotgiu, A. De, Coccoli, M., & Vercelli, G. (2020). Comparing the perception of “sense of presence” between a stereo mix and a binaural mix in immersive music. 148th Audio Engineering Society Convention 2020, (Convention e-Brief 588), 1–5. Subramanian, R., Wache, J., Abadi, M. K., Vieriu, R. L., Winkler, S., & Sebe, N. (2018). ASCERTAIN: Emotion and personality recognition using commercial sensors. IEEE Transactions on Affective Computing, 9(2), 147–160. https://doi.org/10.1109/TAFFC.2016.2625250 Suhaimi, N. S., Mountstephens, J., & Teo, J. (2020). EEG-Based Emotion Recognition: A State- of-the-Art Review of Current Trends and Opportunities. Computational Intelligence and Neuroscience, 2020. https://doi.org/10.1155/2020/8875426 Yang, Y., Wu, Q., Qiu, M., Wang, Y., & Chen, X. (2018). Emotion Recognition from Multi- Channel EEG through Parallel Convolutional Recurrent Neural Network. In 2018 International Joint Conference on Neural Networks (IJCNN) (pp. 1-7). https://doi.org/10.1109/IJCNN.2018.8489331 Shen, F., Dai, G., Lin, G., Zhang, J., Kong, W., & Zeng, H. (2020). EEG-based emotion recognition using 4D convolutional recurrent neural network. Cognitive Neurodynamics, 14(6), 815-828. https://doi.org/10.1007/s11571-020-09634 Text http://purl.org/coar/access_right/c_abf2 info:eu-repo/semantics/openAccess http://purl.org/coar/version/c_970fb48d4fbd8a85 info:eu-repo/semantics/publishedVersion http://purl.org/coar/resource_type/c_6501 Kim, J., Kim, W., & Kim, J.-T. (2015). Psycho-physiological responses of drivers to road section types and elapsed driving time on a freeway. Can. J. Civ. Eng., 42, 881–888. https://doi.org/https://doi.org/10.1139/cjce-2014-0392 Li, Y., Cai, J., Dong, Q., Wu, L., & Chen, Q. (2020). Psychophysiological responses of young people to soundscapes in actual rural and city environments. AES: Journal of the Audio Engineering Society, 68 (12), 910–925. https://doi.org/10.17743/JAES.2020.0060 Lepa, S., Weinzierl, S., Maempel, H. J., & Ungeheuer, E. (2014). Emotional impact of different forms of spatialization in everyday mediatized music listening: Placebo or technology effects? 136th Audio Engineering Society Convention 2014, (Convention Paper 9024), 141–148. Nair, S. (2016). Reverse Engineering Emotions in an Immersive Audio Mix Format. (IBC), 1–5. https://revistas.usb.edu.co/index.php/IJPR/article/view/7467 Universidad San Buenaventura - USB (Colombia) International Journal of Psychological Research 17 This paper addresses the design and development of an advanced neurofeedback system for training in emotional regulation skills and competencies; the system integrates a Virtual Reality (VR) platform with a 16-channel OpenBCI device for real-time capture of electroencephalographic (EEG) signals. The main objective of the research lies in the application of machine learning algorithms, specifically Random Forest and K-Nearest Neighbors (KNN), for the classification of emotional states in terms of valence and arousal. These algorithms achieve an accuracy of up to 83% for arousal classification and 90% for valence. EEG signals are processed and classified in real time and the results are integrated into a virtual reality environment created in Unity. This adaptive environment changes according to the detected emotional states, allowing for more precise regulation. In addition, a diaphragmatic breathing protocol has been developed within the virtual reality environment as an intervention strategy for emotional regulation. The system is in its final stage of piloting to establish the efficacy of the system. Camelo Roa, Sandra Milena Rodríguez, Belman Jahir Neurofeedback Emotional regulation Virtual reality Machine learning OpenBCI Training 2 Núm. 2 , Año 2024 : Interdisciplinary Approaches for Human Cognition: Expanding Perspectives on the Mind Journal article application/pdf Publication 2011-2084 118 113 https://revistas.usb.edu.co/index.php/IJPR/article/download/7467/5567 2024-09-03T00:00:00Z 2024-09-03T00:00:00Z 2024-09-03 https://doi.org/10.21500/20112084.7467 10.21500/20112084.7467 2011-7922 |
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UNIVERSIDAD DE SAN BUENAVENTURA |
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Colombia |
| collection |
International Journal of Psychological Research |
| title |
Entrenamiento en regulación emocional impulsado por neurofeedback en un entorno de realidad virtual: un enfoque de aprendizaje automático utilizando OpenBCI |
| spellingShingle |
Entrenamiento en regulación emocional impulsado por neurofeedback en un entorno de realidad virtual: un enfoque de aprendizaje automático utilizando OpenBCI Camelo Roa, Sandra Milena Rodríguez, Belman Jahir Neurofeedback Entrenamiento Open BCI aprendizaje automatico realidad virtual regulación emocional, Neurofeedback Emotional regulation Virtual reality Machine learning OpenBCI Training |
| title_short |
Entrenamiento en regulación emocional impulsado por neurofeedback en un entorno de realidad virtual: un enfoque de aprendizaje automático utilizando OpenBCI |
| title_full |
Entrenamiento en regulación emocional impulsado por neurofeedback en un entorno de realidad virtual: un enfoque de aprendizaje automático utilizando OpenBCI |
| title_fullStr |
Entrenamiento en regulación emocional impulsado por neurofeedback en un entorno de realidad virtual: un enfoque de aprendizaje automático utilizando OpenBCI |
| title_full_unstemmed |
Entrenamiento en regulación emocional impulsado por neurofeedback en un entorno de realidad virtual: un enfoque de aprendizaje automático utilizando OpenBCI |
| title_sort |
entrenamiento en regulación emocional impulsado por neurofeedback en un entorno de realidad virtual: un enfoque de aprendizaje automático utilizando openbci |
| description |
Este artículo aborda el diseño y desarrollo de un sistema avanzado de neurofeedback para el entrenamiento en habilidades y competencias de regulación emocional; el sistema integra una plataforma de Realidad Virtual (VR) con un dispositivo OpenBCI de 16 canales para la captura en tiempo real de señales electroencefalográficas (EEG). El principal objetivo de la investigación radica en la aplicación de algoritmos de aprendizaje automático, concretamente Random Forest y K-Nearest Neighbors (KNN), para la clasificación de estados emocionales en términos de valencia y excitación. Estos algoritmos logran una precisión de hasta el 83% para la clasificación de la excitación y del 90% para la valencia. Las señales de EEG se procesan y clasifican en tiempo real y los resultados se integran en un entorno de realidad virtual creado en Unity. Este entorno adaptativo cambia según los estados emocionales detectados, permitiendo una regulación más precisa. Además, se ha desarrollado un protocolo de respiración diafragmática dentro del entorno de realidad virtual como estrategia de intervención para la regulación emocional. El sistema se encuentra en su etapa final de prueba para establecer la eficacia del sistema.
|
| description_eng |
This paper addresses the design and development of an advanced neurofeedback system for training in emotional regulation skills and competencies; the system integrates a Virtual Reality (VR) platform with a 16-channel OpenBCI device for real-time capture of electroencephalographic (EEG) signals. The main objective of the research lies in the application of machine learning algorithms, specifically Random Forest and K-Nearest Neighbors (KNN), for the classification of emotional states in terms of valence and arousal. These algorithms achieve an accuracy of up to 83% for arousal classification and 90% for valence. EEG signals are processed and classified in real time and the results are integrated into a virtual reality environment created in Unity. This adaptive environment changes according to the detected emotional states, allowing for more precise regulation. In addition, a diaphragmatic breathing protocol has been developed within the virtual reality environment as an intervention strategy for emotional regulation. The system is in its final stage of piloting to establish the efficacy of the system.
|
| author |
Camelo Roa, Sandra Milena Rodríguez, Belman Jahir |
| author_facet |
Camelo Roa, Sandra Milena Rodríguez, Belman Jahir |
| topicspa_str_mv |
Neurofeedback Entrenamiento Open BCI aprendizaje automatico realidad virtual regulación emocional, |
| topic |
Neurofeedback Entrenamiento Open BCI aprendizaje automatico realidad virtual regulación emocional, Neurofeedback Emotional regulation Virtual reality Machine learning OpenBCI Training |
| topic_facet |
Neurofeedback Entrenamiento Open BCI aprendizaje automatico realidad virtual regulación emocional, Neurofeedback Emotional regulation Virtual reality Machine learning OpenBCI Training |
| citationvolume |
17 |
| citationissue |
2 |
| citationedition |
Núm. 2 , Año 2024 : Interdisciplinary Approaches for Human Cognition: Expanding Perspectives on the Mind |
| publisher |
Universidad San Buenaventura - USB (Colombia) |
| ispartofjournal |
International Journal of Psychological Research |
| source |
https://revistas.usb.edu.co/index.php/IJPR/article/view/7467 |
| language |
Inglés |
| format |
Article |
| rights |
http://creativecommons.org/licenses/by-nc-nd/4.0 This work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License. http://purl.org/coar/access_right/c_abf2 info:eu-repo/semantics/openAccess |
| references_eng |
Gross, J. J. (2014). Emotion regulation: Conceptual and empirical foundations. En J. J. Gross (Ed.), Handbook of emotion regulation (2ª. ed., pp. 3-20). Guilford Press. Hermann, E. (2022). Neural responses to positive and negative valence: How can valence influence frontal alpha asymmetry? Tilburg University. Honda, S., Ishikawa, Y., Konno, R., Imai, E., Nomiyama, N., Sakurada, K., … Nakatani, M. (2020). Proximal Binaural Sound Can Induce Subjective Frisson. Frontiers in Psychology, 11(March, Article 316), 1–10. https://doi.org/10.3389/fpsyg.2020.00316 REFERENCES Adorni, R., Brugnera, A., Gatti, A., Tasca, G. A., Sakatani, K., & Compare, A. (2019). Psychophysiological Responses to Stress Related to Anxiety in Healthy Aging: A Near- Infrared Spectroscopy (NIRS) Study. Journal of Psychophysiology, 33(3), 188–197. https://doi.org/10.1027/0269-8803/a000221 Allen, J. J. B., Coan, J. A., & Nazarian, M. (2004). Issues and assumptions on the road from raw signals to metrics of frontal EEG asymmetry in emotion. Biological Psychology, 67(1–2), 183–218. https://doi.org/10.1016/j.biopsycho.2004.03.007 Drossos, K., Floros, A., & Giannakoulopoulos, A. (2014). BEADS: A dataset of Binaural Emotionally Annotated Digital Sounds. IISA 2014 - 5th International Conference on Information, Intelligence, Systems and Applications, (July), 158–163. Eisenberg, N., Hofer, C. and Vaughan, J. (2007) Effortful control and its socio-emotional consequences. In: Gross, J., Ed., Handbook of Emotion Regulation, 287-306. Hughes, S., & Kearney, G. (2015). Fear and Localisation: Emotional Fine-Tuning Utlising Multiple Source Directions. AES: Journal of the Audio Engineering Society, (56th International Conference, London, UK). Hsu, B. W., & Wang, M. J. J. (2013). Evaluating the effectiveness of using electroencephalogram power indices to measure visual fatigue. Perceptual and Motor Skills, 116(1), 235–252. https://doi.org/10.2466/29.15.24.PMS.116.1.235-252 Katsigiannis, S., & Ramzan, N. (2018). DREAMER: A Database for Emotion Recognition Through EEG and ECG Signals from Wireless Low-cost Off-the-Shelf Devices. IEEE Journal of Biomedical and Health Informatics, 22(1), 98–107. https://doi.org/10.1109/JBHI.2017.2688239 Navea, R. F., & Dadios, E. (2015). Beta/Alpha power ratio and alpha asymmetry characterization of EEG signals due to musical tone stimulation. Project Einstein 2015, (October). Posner, J., Russel, J. A., & Peterson, B. S. (2005). The circumplex model of affect: An integrative approach to affective neuroscience, cognitive development, and psychopathology. National Institude of Health, (17), 715–734. Sotgiu, A. De, Coccoli, M., & Vercelli, G. (2020). Comparing the perception of “sense of presence” between a stereo mix and a binaural mix in immersive music. 148th Audio Engineering Society Convention 2020, (Convention e-Brief 588), 1–5. Subramanian, R., Wache, J., Abadi, M. K., Vieriu, R. L., Winkler, S., & Sebe, N. (2018). ASCERTAIN: Emotion and personality recognition using commercial sensors. IEEE Transactions on Affective Computing, 9(2), 147–160. https://doi.org/10.1109/TAFFC.2016.2625250 Suhaimi, N. S., Mountstephens, J., & Teo, J. (2020). EEG-Based Emotion Recognition: A State- of-the-Art Review of Current Trends and Opportunities. Computational Intelligence and Neuroscience, 2020. https://doi.org/10.1155/2020/8875426 Yang, Y., Wu, Q., Qiu, M., Wang, Y., & Chen, X. (2018). Emotion Recognition from Multi- Channel EEG through Parallel Convolutional Recurrent Neural Network. In 2018 International Joint Conference on Neural Networks (IJCNN) (pp. 1-7). https://doi.org/10.1109/IJCNN.2018.8489331 Shen, F., Dai, G., Lin, G., Zhang, J., Kong, W., & Zeng, H. (2020). EEG-based emotion recognition using 4D convolutional recurrent neural network. Cognitive Neurodynamics, 14(6), 815-828. https://doi.org/10.1007/s11571-020-09634 Kim, J., Kim, W., & Kim, J.-T. (2015). Psycho-physiological responses of drivers to road section types and elapsed driving time on a freeway. Can. J. Civ. Eng., 42, 881–888. https://doi.org/https://doi.org/10.1139/cjce-2014-0392 Li, Y., Cai, J., Dong, Q., Wu, L., & Chen, Q. (2020). Psychophysiological responses of young people to soundscapes in actual rural and city environments. AES: Journal of the Audio Engineering Society, 68 (12), 910–925. https://doi.org/10.17743/JAES.2020.0060 Lepa, S., Weinzierl, S., Maempel, H. J., & Ungeheuer, E. (2014). Emotional impact of different forms of spatialization in everyday mediatized music listening: Placebo or technology effects? 136th Audio Engineering Society Convention 2014, (Convention Paper 9024), 141–148. Nair, S. (2016). Reverse Engineering Emotions in an Immersive Audio Mix Format. (IBC), 1–5. |
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