Publicado:
2025-06-25Número:
Vol. 19 Núm. 1 (2025)Sección:
Visión InvestigadoraEEG Verification of Relaxation Processes with Tibetan Stimuli
Verificación de Procesos de Relajación con Estímulos Tibetanos con EEG
Palabras clave:
Brain wave synchronization, EEG, Musicotherapy, Relaxation states, Tibetan singing bowls (en).Palabras clave:
Sincronización de Ondas Cerebrales, EEG, Musicoterapia, Estados de Relajación, Cuencos Tibetanos (es).Descargas
Resumen (en)
This project seeks to analyze the brain activity of people while experiencing the stimulation of Tibetan bowls to induce states of relaxation. The measurement of the polar pattern of the bowls was performed to characterize their acoustic stimulation and, through an electroencephalogram (EEG), and the brain activity of the participants was recorded during the stimulation with the bowls. Data were collected from the brain waves and other relevant parameters. The analysis of the data allows to evaluate the efficacy of the singing bowls in inducing relaxation states and its impact on brain activity, as well as to identify specific brain response patterns associated with the stimulation of the singing bowls.
Resumen (es)
Este proyecto busca analizar la actividad cerebral de las personas mientras experimentan la estimulación de los cuencos tibetanos para inducir estados de relajación. Se realiza la medición del patrón polar de los cuencos para caracterizar su estimulación acústica y, a través de un electroencefalograma (EEG), se registró la actividad cerebral de los participantes durante la estimulación con los cuencos. Se recopilaron datos de las ondas cerebrales y otros parámetros relevantes. El análisis de los datos permite evaluar la eficacia de los cuencos tibetanos para inducir estados de relajación y su impacto en la actividad cerebral, así como identificar patrones de respuesta cerebral específicos asociados a la estimulación de los cuencos tibetanos.
Referencias
World Health Organization, “Mental health action plan 2013–2020,” WHO, Geneva, 2013.
D. Goleman, The Science of Meditation, Penguin, 2017.
A. Goldsby, M. Goldsby, M. McWalters, and P. Mills, “Effects of Singing Bowl Sound Meditation on Mood, Tension, and Well-being: An Observational Study,” J. Evid.-Based Complementary Altern. Med., vol. 22, no. 3, pp. 401–406, 2017.
C. Chang et al., “A study on using EEG and soundscape to analyze stress reduction in urban parks,” Landscape Urban Plan., vol. 207, p. 103993, 2021.
Y. Kim and H. Lee, “EEG-based emotion classification using deep learning for music therapy,” Sensors, vol. 22, no. 4, p. 1391, 2022.
G. D. Davis, “Music Therapy and Anxiety in Adults,” J. Music Ther., vol. 59, no. 1, pp. 78–101, 2022.
C. Jiménez-Moreno et al., "Classification of Facial Expression of Post-Surgical Pain in Children: Evaluation of Convolutional Neural Networks", Vision Electronica, vol. 15, no. 1, pp. 7–16, 2021.
L. F. Vargas-Pardo and F. N. Giraldo-Ramos, "Firefly Algorithm for Facility Layout Problem Optimization", Vision Electronica, vol. 15, no. 2, pp. 218–225, 2021.
R. A. González Bustamante et al., “Smart cities in collaboration with the internet of things,” Vision Electronica, vol. 14, no. 2, pp. 185–195, 2020.
L. L. Hurtado-Cortés et al., “Artificial vision applied to manufacturing process,” Vision Electronica, vol. 15, no. 1, pp. 113–122, 2021.
A. Smith, “Vibrational healing through Tibetan bowls: A review,” J. Integr. Med., vol. 18, no. 3, pp. 175–182, 2020.
S. Patel and K. Krishna, “Sound Healing Therapies: Science and Evidence,” Alternative Health Pract., vol. 29, no. 1, pp. 20–25, 2021.
M. Zhang et al., “Auditory stimulation and anxiety: Meta-analysis,” Neuropsychologia, vol. 168, p. 108157, 2022.
S. López-González, “EEG en terapia multisensorial Snoezelen”, TFG, Universidad de Valladolid, 2021.
M. Rivera et al., “EEG neurorehabilitation study for trauma patients,” NeuroRehabilitation, vol. 47, no. 1, pp. 95–104, 2020.
Q. Wang et al., “EEG emotion recognition using attention-based convolutional transformer neural network,” IEEE Trans. Neural Syst. Rehabil. Eng., vol. 29, pp. 269–279, 2021.
H. Zhang et al., “Emotion Recognition Using CNN-LSTM on EEG Signals,” IEEE Access, vol. 7, pp. 89350–89360, 2019.
T. Li et al., “Neural responses to vegetation density and sound in urban parks,” Urban For. Urban Green., vol. 61, p. 127094, 2021.
M. Liu et al., “Restorative effects of natural soundscapes on brain activity,” Scientific Reports, vol. 12, p. 5123, 2022.
H. S. Yoon et al., “Effect of vibration therapy on EEG analysis using deep learning,” Healthcare Technol. Lett., vol. 7, no. 4, pp. 94–101, 2020.
R. Prakash et al., “Deep learning-based EEG analysis for neuromodulation therapies,” IEEE Rev. Biomed. Eng., vol. 14, pp. 250–262, 2021.
C. J. Ko et al., “User-centered design of neurofeedback meditation system using EEG,” Sensors, vol. 20, no. 22, p. 6575, 2020.
J. Qian et al., “Meditation State Recognition using EEG Neurofeedback,” IEEE Trans. Instrum. Meas., vol. 70, pp. 1–9, 2021.
Y. Cahn and J. Polich, “Meditation states and traits: EEG, ERP, and neuroimaging studies,” Psychol. Bull., vol. 132, no. 2, pp. 180–211, 2006.
J. Thomas and J. Cohen, “Zen meditation and brain connectivity,” Front. Hum. Neurosci., vol. 13, p. 195, 2019.
S. Scheuber, and M. Vanhoy, "Emotional and Neurological Responses to Timbre in Electric Guitar and Voice," Paper 10505, (2021 May.).
J. Stanhope, and P. Weinstein, “The human health effects of singing bowls: A systematic review”, Complementary therapies in medicine, 51, 102412, (2020 Apr.).
C. J. Bless, “Análisis de la actividad EEG durante una sesión de estimulación multisensorial en una sala Snoezelen”, Universidad de Valladolid. Escuela Técnica Superior de Ingenieros de Telecomunicación, 2020.
L. Gong, M. Li, T. Zhang, W. Chen, “EEG emotion recognition using attention-based convolutional transformer neural network”, Biomedical Signal Processing and Control, Vol. 84, 2023.
C. Zeng, W. Lin, N. Li, Y. Wen, Y. Wang, W. Jiang, J. Zhang, H. Zhong, X. Chen, W. Luo, et al. “Electroencephalography (EEG)-Based Neural Emotional Response to the Vegetation Density and Integrated Sound Environment in a Green Space”, Forests, 2021.
S. N. Safder, M. U. Akram, M. N. Dar, A. A. Khan, S. G. Khawaja, A. R. Subhani, I. K. Niazi, S. Gul, “Analysis of EEG signals using deep learning to highlight effects of vibration-based therapy on brain”, Biomedical Signal Processing and Control, Vol. 83, 2023.
A. E. Nieto-Vallejo, O. F. Ramírez-Pérez, L. E. Ballesteros-Arroyave, and A. Aragón, “Design of a Neurofeedback Training System for Meditation Based on EEG Technology”, Revista Facultad de Ingeniería, 30(55), 2021
H.Y. Huang & P.C. Lo (2019) EEG dynamics of experienced Zen meditation practitioners probed by complexity index and spectral measure, Journal of Medical Engineering & Technology, 33:4, 314-321, DOI: 10.1080/03091900802602677.
F. Ramos-Argüelles, G. Morales, S. Egozcue, R.M. Pabón, M.T. Alonso, “Técnicas básicas de electroencefalografía: principios y aplicaciones clínicas”, vol. 32, 2009.
J. Zain, “El uso de cuencos tibetanos como recurso vibroacústico en Musicoterapia Receptiva”, XVIII Forum estadual de Musicoterapia, 2012.
A. Ramírez Sánchez, C. Espinosa Calderón, A. F. Herrera Montenegro, E. Espinosa Calderón, A. Ramírez Moyano, “Beneficios de la psicoeducación de entrenamiento en técnicas de relajación en pacientes con ansiedad”, Revista Enfermería Docente, 2014.
M. Tobal, “Actividad Cerebral y Deporte: Un Estudio Mediante Mapas de Actividad Eléctrica Cerebral”, Universidad Complutense de Madrid, 1992.
EMOTIV. (2023, 6 abril). EMOTIV Insight 2 with 5 Channel EEG Headset | EMOTIV. https://www.emotiv.com/product/emotiv-insight-5-channel-mobile-brainwear/.
A. Lee, K. Kim, and J. Lee, “Effects of binaural beats on EEG signals: A systematic review,” IEEE Access, vol. 10, pp. 12234–12245, 2022.
P. A. Pérez, J. A. González, and F. J. Vargas, “Evaluación de la actividad cerebral mediante EEG en estados de relajación inducidos por música instrumental,” Revista Colombiana de Psicología, vol. 31, no. 1, pp. 11–25, 2022.
Y. Zhang, H. Zhang, Y. Sun, and H. Zhou, “A hybrid CNN-LSTM model for EEG-based emotion recognition,” IEEE Trans. Cogn. Dev. Syst., vol. 14, no. 2, pp. 179–190, 2022.
L. Castillo and F. Ortega, “Análisis de la actividad electroencefalográfica en estudiantes expuestos a sonidos de la naturaleza,” Rev. Iberoamericana de Neurociencia, vol. 8, no. 2, pp. 65–74, 2020.
C. T. Lin, C. H. Chuang, Y. K. Wang, S. F. Tsai, and C. H. Ko, “EEG-based relaxation assessment using non-intrusive auditory stimuli,” Front. Neurosci., vol. 14, p. 746, 2020.
M. Picard and C. Craig, “EEG microstates and emotion regulation in mindfulness meditation,” NeuroImage, vol. 203, p. 116206, 2019.
M. Chou, C. Shih, and T. Hsiao, “Effects of Tibetan Singing Bowl Sound Meditation on EEG Coherence,” Complement. Ther. Med., vol. 45, pp. 22–29, 2019.
S. J. Kim, K. S. Lee, and D. Park, “Neural effects of instrumental versus lyrical music on relaxation: An EEG analysis,” Healthcare, vol. 11, no. 2, p. 145, 2023.
A. M. Pineda, “Mapeo de la actividad cerebral inducida por sonidos relajantes naturales,” Revista de Ciencias Cognitivas, vol. 10, no. 1, pp. 43–52, 2021.
Y. Zhang, Y. Zhou, L. Wang, and L. Sun, “Neurofeedback and EEG rhythms in stress reduction: a systematic literature review,” Biomed. Signal Process. Control, vol. 67, p. 102519, 2021.
H. J. Lee and M. K. Lee, “Effects of nature sound-based audio therapy on HRV and EEG in anxious patients,” Journal of Integrative Medicine, vol. 19, no. 4, pp. 329–336, 2021.
P. D. Welch and R. D. Cheyne, “Meditative states tracked through EEG: A framework for biofeedback applications,” IEEE Trans. Biomed. Eng., vol. 68, no. 6, pp. 1853–1864, 2021.
Cómo citar
APA
ACM
ACS
ABNT
Chicago
Harvard
IEEE
MLA
Turabian
Vancouver
Descargar cita
Visitas
Descargas
Licencia
Derechos de autor 2024 Visión electrónica
Esta obra está bajo una licencia internacional Creative Commons Atribución-NoComercial 4.0.
.png)
atribución- no comercial 4.0 International
.jpg)
