A randomized controlled trial (RCT) to explore the effect of audio-visual entrainment among psychological disorders.: Neuro-Upper

A randomized controlled trial (RCT) to explore the effect of audio-visual entrainment among psychological disorders.




Entrainment, Brain-Computer-Interface, Psychological disorders, Intervention


Background and aim: Although many mental disorders have relevant proud in neurobiological dysfunctions, most intervention approaches neglect neurophysiological features or use pharmacological intervention alone. Non-invasive Brain-Computer Interfaces (BCIs), providing natural ways of modulating mood states, can be promoted as an alternative intervention to cope with neurobiological dysfunction.

Methods: A BCI prototype was proposed to feedback a person’s affective state such that a closed-loop interaction between the participant’s brain responses and the musical stimuli is established. It feedbacks in real-time flickering lights matching with the individual’s brain rhythms undergo to auditory stimuli. A RCT was carried out on 15 individuals of both genders (mean age = 49.27 years) with anxiety and depressive spectrum disorders randomly assigned to 2 groups (experimental vs. active control).

Results: Outcome measures revealed either a significant decrease in Hamilton Rating Scale for Depression (HAM-D) scores and gains in cognitive functions only for participants who undergone to the experimental treatment. Variability in HAM-D scores seems explained by the changes in beta 1, beta 2 and delta bands. Conversely, the rise in cognitive function scores appear associated with theta variations.

Conclusions: Future work needs to validate the relationship proposed here between music and brain responses. Findings of the present study provided support to a range of research examining BCI brain modulation and contributes to the understanding of this technique as instruments to alternative therapies We believe that Neuro-Upper can be used as an effective new tool for investigating affective responses, and emotion regulation (www.actabiomedica.it)

Author Biography

Olimpia Pino, University of Parma, Department of Medicine & Surgery, Neuroscience Unit

Department of Medicince and Surgery, Neuroscience Unit


Menon V. Large-scale brain networks and psychopathology: A unifying triple network model. Trends Cogn Sci 2011; 15: 483-506. doi: 10.1016/j.tics.2011.08.003.

McFarland DJ, Daly J, Boulay C, Parvaz M. Therapeutic applications of BCI technologies. Brain Comp Interf 2017; 47(1-2): 37-52. https://doi.org/10.1080/2326263X.2017.1307625

Sitaram R, Ros T, Stoeckel L, Haller S, Scharnowski F, Lewis-Peacock J, et al.. Closed-loop brain training: The science of neurofeedback. Nat Rev Neurosci 2017; 18(2): 86-100. https://doi.org/10.1038/nrn.2016.164

Sigala R, Haufe S, Roy D, Dinse HR, Ritter P. The role of alpha-rhythm states in perceptual learning: Insights from experiments and computational models. Front Comput Neurosci 2014; 8: 36. https://doi.org/10.3389/fncom.2014.00036

Zhou H, Melloni L, Poeppel D, Ding N. Interpretations of frequency domain analyses of neural entrainment: Periodicity, fundamental frequency, and harmonics. Front Hum Neurosci 2016; 10: 274. doi: 10.3389/fnhum.2016.00274

Kober SE, Witte M, Ninaus M, Neuper C, Wood G. Learning to modulate one’s own brain activity: The effect of spontaneous mental strategies. Front Hum Neurosci 2013; 7: 695. doi: 10.3389/fnhum.2013.00695.

Ros T, J Baars B, Lanius RA, Vuilleumier P. Tuning pathological brain oscillations with neurofeedback: A systems neuroscience framework. Front Hum Neurosci 2014; 8: 1008. doi:10.3389/fnhum.2014.01008

Ruhnau P, Keitel C, Lithari C, Weisz N, Neuling T. Flicker-driven responses in visual cortex change during matched-frequency transcranial alternating current stimulation. Front Hum Neurosci 2016; 10: 184. doi:10.3389/fnhum.2016.00184

Zoefel B, Huster RJ, Herrmann CS. Neurofeedback training of the upper alpha frequency band in EEG improves cognitive performance. Neuroimage 2011; 54 (2): 1427-1431. doi:10.1016/j.neuroimage.2010.08.078

Freyer F, Becker R, Dinse HR, Ritter P. State-dependent perceptual learning. J Neurosci 2013; 33 (7): 2900-2907. doi:10.1523/JNEUROSCI.4039-12.2013

Sale A, De Pasquale R, Bonaccorsi J, Pietra G, Olivieri D, Berardi N, et al. Visual perceptual learning induces long-term potentiation in the visual cortex. Neurosci 2011; 172: 219-225. 10.1016/j.neuroscience.2010.10.078

Naas A, Rodrigues J, Knirsch JP, Sonderegger A. Neurofeedback training with a low-priced EEG device leads to faster alpha enhancement but shows no effect on cognitive performance: A single-blind, sham-feedback study. PLoS One 2019; 14 (9): e0211668. doi:10.1371/journal.pone.0211668

Mühl C, Allison B, Nijholt A, Chanel G. A survey of affective brain computer interfaces: principles, state-of-the-art, and challenges. Brain-Comp Interf 2014; 1(2): 66-84. 10.1080/2326263X.2014.912881

Wang SY, Lin IM, Fan SY, et al. The effects of alpha asymmetry and high-beta down-training neurofeedback for patients with the major depressive disorder and anxiety symptoms. J Affect Disord 2019; 257: 287-296. doi:10.1016/j.jad.2019.07.026

Shibata K, Watanabe T, Sasaki Y, Kawato M. Perceptual learning incepted by decoded fMRI neurofeedback without stimulus presentation. Science 2011; 334 (6061): 1413-1415. doi:10.1126/science.1212003

Martínez-Molina N, Mas-Herrero E, Rodríguez-Fornells A, Zatorre RJ, Marco-Pallarés J. Neural correlates of specific musical anhedonia. Proc Natl Acad Sci U S A. 2016; 113 (46): E7337-E7345. doi:10.1073/pnas.1611211113

Ramirez R, Palencia-Lefler M, Giraldo S, Vamvakousis Z. Musical neurofeedback for treating depression in elderly people. Front Neurosci 2015; 9: 354. doi:10.3389/fnins.2015.00354

Knyazev GG. Cross-frequency coupling of brain oscillations: an impact of state anxiety. Int J Psychophysiol 2011; 80 (3): 236-245. doi:10.1016/j.ijpsycho.2011.03.013. PMID: 21458502.

Ruiz S, Lee S, Soekadar SR, et al. Acquired self-control of insula cortex modulates emotion recognition and brain network connectivity in schizophrenia. Hum Brain Mapp 2013; 34 (1): 200-212. doi:10.1002/hbm.21427

Lin YP, Wang CH, Jung TP, et al. EEG-based emotion recognition in music listening. IEEE Trans Biomed Eng 2010; 57 (7): 1798-1806. doi:10.1109/TBME.2010.2048568

Ehrlich SK, Agres KR, Guan C, Cheng G. A closed-loop, music-based brain-computer interface for emotion mediation. PLoS One 2019; 14 (3): e0213516. doi:10.1371/journal.pone.0213516

Coffey EBJ, Musacchia G, Zatorre RJ. Cortical correlates of the auditory frequency-following and onset responses: EEG and fMRI evidence. J Neurosci 2017; 37: 830-838. doi: 10.1523/JNEUROSCI.1265-16.2017.

Nozaradan S, Peretz I, Mouraux A. Selective neuronal entrainment to the beat and meter embedded in a musical rhythm. J Neurosci 2012; 32 (49): 17572-17581; doi: 10.1523/JNEUROSCI.3203-12.2012

Salimpoor VN, Zald DH, Zatorre RJ, Dagher A, McIntosh AR. Predictions and the brain: how musical sounds become rewarding. Trends Cogn Sci 2015; 19 (2): 86-91. doi:10.1016/j.tics.2014.12.001

Krol LR, Andreessen LM, Zander TO. Passive Brain-Computer Interfaces: A perspective on increased interactivity. In CS Nam, A Nijholt, F Lotte (Eds.), Brain-Computer Interfaces handbook: Technological and theoretical advances (pp. 69-86). Boca Raton, FL, USA: CRC Press, 2018.

Widge AS, Dougherty DD, Moritz CT. Affective Brain-Computer Interfaces as enabling technology for responsive psychiatric stimulation. Brain Comput Interf (Abingdon). 2014; 1 (2): 126-136. doi:10.1080/2326263X.2014.912885

Pino O, La Ragione F. A Brain Computer Interface for audio-visual entrainment in emotional regulation: Preliminary evidence of its effects. Online Interdiscip Res 2016; VI (II): 1-12 http://www.oiirj.org/oiirj/mar-apr2016/05.pdf

Hamilton M A rating scale for depression. J Neurol Neurosurg Psychiatr 1960; 23 (1): 56-62. doi:10.1136/jnnp.23.1.56

Spielberger CD, Gorsuch RL, Lushene R, Vagg PR, Jacobs GA. Manual for the State-Trait Anxiety Inventory. Palo Alto, CA: Consulting Psychologists Press, 1983. http://www.apa.org/pi/about/publications/caregivers/practice-settings/assessment/tools/trait-state.aspx

Folstein MF, Folstein SE, McHugh PR. “Mini-mental state”. A practical method for grading the cognitive state of patients for the clinician. J Psychiatr Res 1975; 12 (3): 189-198. doi:10.1016/0022-3956(75)90026-6

Wechsler D. Manual for the Wechsler Adult Intelligence Scale-Revised (WAIS-R). New York: The Psychological Corporation, 1981. http://journals.sagepub.com/doi/abs/10.1177/073428298300100310

Raven J, Raven JC, Court JH. Manual for Raven’s Progressive Matrices and vocabulary scales. San Antonio, TX: Harcourt Assessment, 2003. https://pdfs.semanticscholar.org/ff74/66bc742d5277862676714bf7cc4c3a655bcf.pdf

LaRocco J, Le MD, Paeng DG. A systemic review of available low-cost EEG headsets used for drowsiness detection. Front Neuroinform 2020; 14: 553352. doi:10.3389/fninf.2020.553352

Lim CA, Chia WC. Analysis of single-electrode EEG rhythms using MATLAB to elicit correlation with cognitive stress. Int J Comp Eng 2015; 7 (2): 149-155. doi:10.7763/IJCTE.2015.V7.947.

Godinez Tello RM Jr, Torres Muller SM, Ferreira A, Freire Bastos T. Comparison of the influence of stimuli color on Steady-State Visual Evoked Potentials. Res Biomed Eng 2015; 31: 218-231. doi: http://dx.doi.org/10.1590/2446-4740.0739







How to Cite

Pino O. A randomized controlled trial (RCT) to explore the effect of audio-visual entrainment among psychological disorders.: Neuro-Upper. Acta Biomed [Internet]. 2022 Jan. 19 [cited 2024 Jul. 13];92(6):e2021408. Available from: https://mattioli1885journals.com/index.php/actabiomedica/article/view/12089