Engagement and Arousal effects in predicting the increase of cognitive functioning following a neuromodulation program
Keywords:
Neuroenhancement, Cognitive functioning, EngagementAbstract
Background and aim: Research in the field of Brain-Computer Interfaces (BCIs) has increased exponentially over the past few years, demonstrating their effectiveness and application in several areas. The main purpose of the present paper was to explore the relevance of user engagement during interaction with a BCI prototype (Neuro-Upper, NU), which aimed at brainwave synchronization through audio-visual entrainment, in the improvement of cognitive performance.
Methods: This paper presents findings on data collected from a sample of 18 subjects with clinical disorders who completed about 55 consecutive sessions of 30 min of audio-visual stimulation. The relationship between engagement and improvement of cognitive function (measured through the Intelligence Quotient - IQ) during NU neuromodulation was evaluated through the Index of Cognitive Engagement (ICE) measured by the Pope ratio (Beta / (Alpha + Theta), and Arousal [(High Beta + Low Beta) / (High Alpha + Low Alpha)].
Results: A significant correlation between engagement and IQ improvement, but no correlation between arousal and IQ improvement emerged, as expected.
Conclusions: Future research aiming at clarifying the role of arousal in psychological disorders and related symptoms will be essential.
References
Fairclough S, Gilleade K, Ewing K, Roberts J. Capturing user engagement via psychophysiology: Measures and mechanisms for biocybernetic adaptation. Int J Auton Adapt Commun Syst 2013; 6(1) 63-79. doi: 10.1504/IJAACS.2013.050694.
Gruzelier JH. A theory of alpha/theta neurofeedback, creative performance enhancement, long distance functional connectivity and psychological integration. Cogn Process 2008; 10, 101-109. https://doi.org/10.1007/s10339-008-0248-5.
Gruzelier JH, Egner T, Vernon D. Validating the efficacy of neurofeedback for optimising performance. In: Neuper C, Klimesch W (Eds) Event-related dynamics of brain oscillations. Prog Brain Res 2006; 159, 421-431. doi:10.1016/S0079-6123(06)59027-2.
Nakamura J, Csikszentmihalyi M. Flow theory and research. In S. J. Lopez & C. R. Snyder (Eds.), Oxford library of psychology. Oxford handbook of positive psychology (p. 195-206). Oxford University Press, 2009.
Wefald AJ Downey RG. Construct dimensionality of Engagement and its relation with satisfaction. J Psychol 2009; 143, 91-112. doi: 10.3200/JRLP.143.1.91-112.
Whitton N. Game engagement theory and adult learning. Simul Gaming 2011; 42(5), 596-609. https://doi.org/10.1177/1046878110378587.
Dehais F, Lafont A, Roy R Fairclough S. A neuroergonomics approach to mental workload, engagement, and human performance. Front Neurosci 2020; 14, 268. doi: 10.3389/fnins.2020.00268.
Hanslmayr S, Sauseng P, Doppelmayr M, Schabus M, Klimesch, W. Increasing individual upper alpha power by neurofeedback improves cognitive performance in human subjects. Appl Psychophiol Biofeedback 2005; 30(1), 1-10. doi:10.1007/s10484-005-2169-8
Makada TI, Ozair D, Mohammed M, Abellanoza C. Enhancing memory retention by increasing alpha and decreasing beta brainwaves using music. Proceedings of the 9th ACM International Conference on Pervasive Technologies Related to Assistive Environments (PETRA ‘16). Association for Computing Machinery, New York, NY, USA, 2016; pp. 1-4. https://doi.org/10.1145/2910674.2935851.
Mladenović J, Frey J, Bonnet-Save M, Mattout J, Lotte F. The impact of Flow in an EEG-based Brain Computer Interface. Proceedings of the 7th International BCI Conference, Graz, Austria, 2017. https://hal.inria.fr/hal-01527748.
Edge J, Lancaster L. Phenomenological analysis of superior musical performance facilitated by neurofeedback: Enhancing musical performance through neurofeedback: Playing the tune of life. Transpers Psychol Rev 2004; 8, 23-35.
Peretz I, Zatorre R Brain organization for music processing. Annu Rev of Psychol 2005; 56(1), 89-114. doi:10.1146/annurev.psych.56.091103.070225.
Peretz I, Vuvane D, Lagrois ME, Armony JL (2015) Neural overlap in processing music and speech. Philos Trans R Soc 2015; B 370, 20140090. http://doi.org/10.1098/rstb.2014.0090.
Ramirez R, Palencia-Lefler M, Giraldo S, Vamvakousis Z (2015) Musical neurofeedback for treating depression in elderly people. Front Neurosci 2015; 9, 354. https://doi.org/10.3389/fnins.2015.00354.
Sourina O, Liu Y, Nguyen MK. Real-time EEG-based emotion recognition for music therapy. J Multimodal User Interfaces 2011; 5(1-2), 27-35. https://doi.org/10.1007/s12193-011-0080-6.
Pereira CS, Teixeira J, Figueiredo P, Xavier J, Castro SL, Brattico E. Music and emotions in the brain: familiarity matters. PLoS ONE 2011; 6, e27241. doi:10.1371/journal.pone.0027241.
Salimpoor VN, van den Bosch I, Kovacevic N, McIntosh AR, Dagher A, Zatorre RJ. Interactions between the nucleus accumbens and auditory cortices predict music reward value. Science 2013; 340, 216-219. doi: 10.1126/science.1231059.
Van der Linden D, Tops M, Bakker AB. Go with the flow: A neuroscientific view on being fully engaged. Eur J Neurosci 2020; 00, 1-17. https://doi.org/10.1111/ejn.15014.
Harmat L, de Manzano Ö, Theorell T, Högman L, Fischer H, Ullén F Physiological correlates of the flow experience during computer game playing Int J Psychophysiol 2015; 97, 1-7. https://doi.org/10.1016/j.ijpsy cho.2015.05.001.
Hancock PA, Kaplan AD, Cruit JK, Hancock GM, MacArthur KR, Szalma JL. A meta-analysis of flow effects and the perception of time. Acta Psychologica, 2019; 198, 102836. https://doi.org/10.1016/j.actpsy.2019.04.007.
Ulrich M, Keller J, Hoenig K, Waller C, Grön G. Neural correlates of experimentally induced flow experiences. NeuroImage 2014; 86, 194-202. https://doi.org/10.1016/j.neuro image.2013.08.019.
Pope AT, Bogart EH, Bartolome DS. Biocybernetic system evaluates indices of operator engagement in automated task. Biol Psychol 1995; 40, 1-2, 187-195. https://doi.org/10.1016/0301-0511(95)05116-3.
Andujar M, Gilbert JE. Let’s learn! Enhancing user’s engagement levels through passive brain-computer interfaces. CHI ‘13 Extended Abstracts on Human Factors in Computing Systems 2013; 703-708. doi: 10.1145/2468356.2468480.
Rajamani K, Ramalingam A, Bavisetti S, Abujelala M. CBREN: Computer brain entertainment system using neural feedback cognitive enhancement. In Proceedings of the 10th International Conference on Pervasive Technologies Related to Assistive Environments (PETRA ‘17). Association for Computing Machinery, New York, NY, USA, 236-237, 2017. https://doi.org/10.1145/3056540.3064971.
Reinecke L, Klatt J, Krämer N. Entertaining media use and the satisfaction of recovery needs: recovery outcomes associated with the use of interactive and non interactive entertaining media. Media Psychol 2011: 14, 192-215. doi: 10.1080/15213269.2011.573466.
Berka C, Levendowski DJ, Lumicao MN, Yau A, Davis G, Zivkovic VT, Olmstead RE, Tremoulet, PD, Craven, PL. EEG correlates of task engagement and mental workload in vigilance, learning, and memory tasks. Aviat, Space, Environ Med, 78(5 Suppl), 2007; B231-B244. https://pubmed.ncbi.nlm.nih.gov/17547324/
Daneshzand M, Faezipour M, Barkana BD. Robust desynchronization of Parkinson’s disease pathological oscillations by frequency modulation of delayed feedback deep brain stimulation. PLoS ONE 2018; 13(11), e0207761. https://doi.org/10.1371/journal.pone.0207761.
Coelli S, Sclocco R, Barbieri R, Reni G, Zucca C, Bianchi AM. EEG-based index for engagement level monitoring during sustained attention. Conference proceedings - IEEE Engineering in Medicine and Biology Society 2015; 1512-1515. doi: 10.1109/EMBC.2015.7318658.
McMahan T, Parberry I, Parsons TD. Evaluating player task engagement and arousal using electroencephalography. Procedia Manuf 2015; 3, 2303-2310. https://doi.org/10.1016/j.promfg.2015.07.376.
Maran T, Sachse P, Martini M, Weber B, Pinggera J, Zuggal S, Furtner M. Lost in time and space: States of high arousal disrupt implicit acquisition of spatial and sequential context information. Front Behav Neurosci 2017; 11, 206. doi: 10.3389/fnbeh.2017.00206.
Yu C, Li Y, Stitt IM, Zhou ZC, Sellers KK, Frohlich F. Theta oscillations organize spiking activity in higher-order visual thalamus during sustained attention. eNeuro 2018; 5. http://dx. doi.org/10.1523/ENEURO.0384-17.2018.
Helfrich RF, Breska A, Knight RT. Neural entrainment and network resonance in support of top-down guided attention. Curr Opin 2019; 29, 82-89. https://doi.org/10.1016/j.copsyc.2018.12.016.
Kienitz R, Joscha T, Schmiedt KA, Shapcott K, Saunders RC, Schmid MC. Theta rhythmic neuronal activity and reaction times arising from cortical receptive field interactions during distributed attention. Curr Biol 2018; 28, 15, 2377-2387.e5. https://doi.org/10.1016/j.cub.2018.05.086.
Fox MD, Snyder AZ, Vincent JL, Raichle ME. Intrinsic fluctuations within cortical systems account for intertrial variability in human behavior. Neuron 2007; 56, 171-184. doi: 10.1016/j.neuron.2007.08.023.
Händel BF, Haarmeier T, Jensen O. Alpha oscillations correlate with the successful inhibition of unattended stimuli. J Cogni Neurosci 2011; 23(9), 2494-502. doi:10.1162/jocn.2010.21557.
Lundqvist M, Herman P, Lansner A. Effect of prestimulus alpha power, phase, and synchronization on stimulus detection rates in a biophysical attractor network model. J Neurosci 2013; 33(29), 11817-11824. https://doi.org/10.1523/JNEUROSCI.5155-12.2013.
Thut G, Miniussi, C Gross J. The functional importance of rhythmic activity in the brain. Curr Biol 2012; 22, 16, R658-R663. https://doi.org/10.1016/j.cub.2012.06.061.
Arns M, Swatzyna RJ, Gunkelman J et al. Sleep maintenance, spindling excessive beta and impulse control: an RDoC arousal and regulatory systems approach? Neuropsychiatr Electrophysiol 2015; 1, 5. https://doi.org/10.1186/s40810-015-0005-9.
Freyer F, Becker R, Dinse HR, Ritter P. State-dependent perceptual learning. J Neurosci 2013; 33 (7), 2900-2907. https://doi.org/10.1523/JNEUROSCI.4039-12.2013.
Hamame CM, Cosmelli D, Henriquez R, Aboitiz F. Neural mechanisms of human perceptual learning: electrophysiological evidence for a two-stage process. PLoS ONE 2011; 6, e19221. doi: 10.1371/journal.pone.0019221.
Masquelier T, Hugues E, Deco G, Thorpe SJ. Oscillations, phase-of-firing coding, and spike timing-dependent plasticity: An efficient learning scheme. J Neurosci 2009; 29, 13484-13493. doi:10.1523/JNEUROSCI.2207-09.2009.
Sigala R, Haufe S, Roy D, Dinse H, 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.
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.
Pino, O. A randomized controlled trial (RCT) to explore the effect of audio-visual entrainment among psychological disorders.: Neuro-Upper. Acta Biomed 2021; 92(6), e2021408, 2022. doi: 10.23750/abm.v92i6.12089.
Pino O. Neuro-Upper, a novel technology for Audio-Visual Entrainment. A randomized controlled trial of 11 individuals with anxiety and depressive disorders. BAOJ Med Nursing 2017, 3(2), 041. http://dx.doi.org/10.24947/baojmn/3/2/141.
Ng BS, Logothetis NK, Kayser C. EEG phase patterns reflect the selectivity of neural firing. Cereb Cortex 2013; 23, 389-398. doi:10.1093/cercor/bhs031.
Siegel M, Donner T, Engel A. Spectral fingerprints of large-scale neuronal interactions. Nat Rev Neurosci 2012; 13, 121-134. https://doi.org/10.1038/nrn3137.
Hamilton M. A rating scale for depression. J Neurol Neurosurg Psychiat 1960; 23, 56-62. https://www.ncbi.nlm.nih.gov/pmc/articles/PMC495331/pdf/jnnpsyc00273-0060.pdf.
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. https://www.ncbi.nlm.nih.gov/pubmed/1202204.
American Psychiatric Association (2000) DSM-IV-TR. Diagnostic and statistical manual of mental disorders, fourth edition, Text Revision. Washington D.C. http://www.psych.org/MainMenu/Research/DSMIV.aspx
American Psychiatric Association. Diagnostic and statistical manual of mental disorders (5th ed.). Washington, DC: Author, 2013. doi: 10.1176/appi.books.9780890423349.
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.
Wechsler D. Wechsler Adult Intelligence Scale - Fourth Edition Administration and Scoring Manual. San Antonio, TX: Pearson, 2008.
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
Vasiljevic GAM, de Miranda LC. The influence of graphical elements on user’s attention and control on a neurofeedback-based game. Entertain Comput 2019; https://doi.org/10.1016/j.entcom.2018.10.003
Elliot AJ. Color and psychological functioning: A review of theoretical and empirical work. Front Psychol 2015; 6, 368. http://dx.doi.org/10.3389/fpsyg.2015.00368.
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. http://dx.doi.org/10.1590/2446-4740.0739.
R Core Team R. A language and environment for statistical computing. Vienna, Austria. http://www.R-project.org, 2014.
Vatansever D, Manktelow A, Sahakian BJ, Menon DK, Stamatakis EA. Default Mode Network engagement beyond self-referential internal mentation. Brain Connect 2018; 8, 4. https://doi.org/10.1089/brain.2017.0489.
Taruffi L, Pehrs C, Skouras S, Koelsch S. Effects of sad and happy music on mind-wandering and the Default Mode Network. Sci Rep 2017; 7, 14396. https://doi.org/10.1038/s41598-017-14849-0.
Downloads
Published
Issue
Section
License
Copyright (c) 2022 Olimpia Pino, Graziana Romano
This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License.
This is an Open Access article distributed under the terms of the Creative Commons Attribution License (https://creativecommons.org/licenses/by-nc/4.0) which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
Transfer of Copyright and Permission to Reproduce Parts of Published Papers.
Authors retain the copyright for their published work. No formal permission will be required to reproduce parts (tables or illustrations) of published papers, provided the source is quoted appropriately and reproduction has no commercial intent. Reproductions with commercial intent will require written permission and payment of royalties.
How to Cite
