1Pegaso University, Faculty for Human Sciences, Napoli, Italy, 2University of Camerino, School of advanced studies, Camerino, Italy
Bridging the Gap between the Body and the Machine: Embodied Learning with Interventional Brain Computer Interfaces?
This review aims to understand how the body and the brain interact with different brain computer interfaces (BCI) and to analyze the implications of these tools on embodied learning in the educational field. Through a theoretical approach a review of the literature is developed by studying the relationship between the body, the brain and BCI. To conduct this research, the keywords “embodied learning”, “cognition”, “digital learning”, “body”, “brain-computer interface” were used in Pubmed, Frontiers, Google Scholar and Researchgate. There are multiple concepts related to digitization and they can vary from owning digital tools such as computers, phones, virtual reality devices to even using interventional BCI. BCI are being reported safe and are capable of reversing physical and cognitive disabilities. The impact of these tools is variable according to their nature, the environmental factors linked to their use, and the condition of the brain and body while using them. With the massive development of technology nowadays many interrogations are coming into surface about the relationship between the human and the machine, and at what level the digital world will be able to interfere with our lives and integrate our bodies.
brain augmentation, digital learning, neuroscience, education
Abrahamson, D., & Lindgren, R. (2014). Embodiment and Embodied Design. In R. Sawyer (Ed.), The Cambridge Handbook of the Learning Sciences (Cambridge Handbooks in Psychology), 358-376.
Acar, A. Z., & Makeig, S. (2013). Effects of forward model errors on EEG source localization. Brain Topography, 26(3), 378–396.
Sereshkeh, A. R., Yousefi, R., Wong, A. T., Rudzicz, F., & Chau, T. (2019) Development of a ternary hybrid fNIRS-EEG brain–computer interface based on imagined speech. Brain-Computer Interfaces, 6(4), 128-140.
Arpaia P., Duraccio L., Moccaldi N., & Rossi S. (2020). Wearable brain-computer interface instrumentation for robot-based rehabilitation by augmented reality. IEEE Transactions on Instrumentation and Measurement, 69, 6362–6371.
Bedir, D., & Erhan, S. E. (2021). The Effect of Virtual Reality Technology on the Imagery Skills and Performance of Target-Based Sports Athletes. Frontiers in Psychology, 11, 2073.
Botrel, L., Holz, E., & Kübler, A. (2015). Brain painting v2: evaluation of p300-based brain-computer interface for creative expression by an end-user following the user-centered design. Brain-Computer Interfaces, 2, 135–149.
Brunoni, A. R., Moffa, A. H., Fregni, F., Palm, U., Padberg, F., Blumberger, D. M., … & Loo, C. K. (2016). Transcranial direct current stimulation for acute major depressive episodes: meta-analysis of individual patient data. The British Journal of Psychiatry: The Journal of Mental Science, 208(6), 522–531.
Caramazza, A., Anzellotti, S., Strnad, L., & Lingnau, A. (2014). Embodied cognition and mirror neurons: A critical assessment. Annual Review of Neuroscience, 37, 1–15.
Castillo, P. R., Middlebrooks, E. H., Grewal, S. S., Okromelidze, L., Meschia, J. F., Quinones-Hinojosa, A., Uitti, R. J., & Wharen, R. E., (2020). Globus Pallidus Externus Deep Brain Stimulation Treats Insomnia in a Patient With Parkinson Disease. Mayo Clinic Proceedings, 95(2), 419–422.
Ceciliani, A. (2018). Dall’Embodied Cognition all’Embodied Education nelle scienze dell’attività motoria e sportiva. Encyclopaideia, 22(51), 51.
Chang S. Nam, Nijholt A., & Lotte F. (2018). Brain-Computer Interfaces Handbook: Technological and Theoretical Advances. Oxford, UK: CRC Press, Taylor & Francis Group.
Choi, B., & Jo, S. (2013). A low-cost EEG system-based hybrid brain-computer interface for humanoid robot navigation and recognition. PloS One, 8(9), e74583.
Church, R. B., Alibali, M., & Kelly, S. (2017). Why Gesture? How the hands function in speaking, thinking and communicating. Journal of Linguistics, 56(2), 441-445.
Cinel, C., Valeriani, D., & Poli, R. (2019). Neurotechnologies for Human Cognitive Augmentation: Current State of the Art and Future Prospects. Frontiers in Human Neuroscience, 13, 13.
Coffman, B. A., Clark, V. P., & Parasuraman, R. (2014). Battery powered thought: Enhancement of attention, learning, and memory in healthy adults using transcranial direct current stimulation. Neuroimage, 85, 895–908.
Cook, S. W., Friedman, H. S., Duggan, K. A., Cui, J., & Popescu, V. (2017). Hand Gesture and Mathematics Learning: Lessons from an Avatar. Cognitive Science, 41(2), 518–535.
Dockery, C. A., Hueckel-Weng, R., Birbaumer, N., & Plewnia, C. (2009). Enhancement of planning ability by transcranial direct current stimulation. The Journal of Neuroscience: The Official Journal of the Society for Neuroscience, 29(22), 7271–7277.
Dougherty, D. D. (2018). Deep Brain Stimulation: Clinical Applications. The Psychiatric clinics of North America, 41(3), 385–394.
Engström, L. M., Redelius, K., & Larsson, H. (2018). Logics of practice in movement culture: Lars-Magnus Engström’s contribution to understanding participation in movement cultures. Sport, Education and Society, 892–904.
Gonzales, M. G., Backer, K. C., Mandujano, B., & Shahin, A. J. (2021). Rethinking the Mechanisms Underlying the McGurk Illusion. Frontiers in Human Neuroscience, 15.
Grau, C., Ginhoux, R., Riera, A., Nguyen, T. L., Chauvat, H., Berg, M., … & Ruffini, G. (2014). Conscious brain-to-brain communication in humans using non-invasive technologies. PloS One, 9(8), e105225.
Grodal, T. (2009). Character Simulation and Emotion. In T. Grodal, Embodied Visions. Oxford University Press New York, 1, 181–204.
Guidetti, M., Marceglia, S., Loh, A., Harmsen, I. E., Meoni, S., Foffani, G., … & Priori, A. (2021). Clinical perspectives of adaptive deep brain stimulation. Brain Stimulation, 14(5), 1238–1247.
Harpaintner, M., Sim, E.-J., Trumpp, N. M., Ulrich, M., & Kiefer, M. (2020). The grounding of abstract concepts in the motor and visual system: An fMRI study. Cortex, 124, 1–22.
Heth, I., & Lavidor, M. (2015). Improved reading measures in adults with dyslexia following transcranial direct current stimulation treatment. Neuropsychologia, 70, 107–113.
Hildt, E. (2019). Multi-Person Brain-To-Brain Interfaces: Ethical Issues. Frontiers in Neuroscience, 13, 1177.
Ho, E., Hettick, M., Papageorgiou, D., Poole, A. J., Monge, M., Vomero, M., … & Rapoport, B. I. (2022). The Layer 7 Cortical Interface: A Scalable and Minimally Invasive Brain–Computer Interface Platform. bioRxiv, (p. 2022.01.02.474656).
Howison, M., Trninic, D., Reinholz, D., & Abrahamson, D. (2011). The Mathematical Imagery Trainer: From embodied interaction to conceptual learning. In Proceedings of the SIGCHI conference on human factors in computing systems (pp. 1989-1998).
Jangwan, N. S., Ashraf, G. M., Ram, V., Singh, V., Alghamdi, B. S., Abuzenadah, A. M., & Singh, M. F. (2022). Brain augmentation and neuroscience technologies: Current applications, challenges, ethics and future prospects. Frontiers in Systems Neuroscience, 16, 1000459.
Kosmas, P., Ioannou, A., & Zaphiris, P. (2019). Implementing embodied learning in the classroom: Effects on children’s memory and language skills. Educational Media International, 56(1), 59–74.
Lefaucheur, J. P., Aleman, A., Baeken, C., Benninger, D. H., Brunelin, J., Di Lazzaro, V., … & Ziemann, U. (2020). Evidence-based guidelines on the therapeutic use of repetitive transcranial magnetic stimulation (rTMS): An update (2014-2018). Clinical Neurophysiology: Official Journal of the International Federation of Clinical Neurophysiology, 131(2), 474–528.
Lim, H., & Ku, J. (2018). A Brain-Computer Interface-Based Action Observation Game That Enhances Mu Suppression. IEEE Transactions on Neural Systems and Rehabilitation Engineering: A Publication of the IEEE Engineering in Medicine and Biology Society, 26(12), 2290–2296.
Macedonia, M. (2019). Embodied Learning: Why at School the Mind Needs the Body. Frontiers in Psychology, 10, 2098.
Macrine, S. L., & Fugate, J. M. B. (2021). Translating Embodied Cognition for Embodied Learning in the Classroom. Frontiers in Education, 6, 712626.
Matheson, H. E., & Barsalou, L. W. (2018). Embodiment and Grounding in Cognitive Neuroscience. In J. T. Wixted (Ed.), Stevens’ Handbook of Experimental Psychology and Cognitive Neuroscience, 1–27.
Meeres, J., & Hariz, M. (2022). Deep Brain Stimulation for Post-Traumatic Stress Disorder: A Review of the Experimental and Clinical Literature. Stereotactic and Functional Neurosurgery, 100(3), 143–155.
Mills, C., Fridman, I., Soussou, W., Waghray, D., Olney, A. M., & D'Mello, S. K. (2017, March). Put your thinking cap on: detecting cognitive load using EEG during learning. In Proceedings of the seventh international learning analytics & knowledge conference (pp. 80-89).
Mitchell, P., Lee, S. C. M., Yoo, P. E., Morokoff, A., Sharma, R. P., … & Campbell, B. C. V. (2023). Assessment of Safety of a Fully Implanted Endovascular Brain-Computer Interface for Severe Paralysis in 4 Patients: The Stentrode With Thought-Controlled Digital Switch (SWITCH) Study. JAMA Neurology, e224847.
Munakata, Y., & Pfaffly, J. (2004). Hebbian learning and development. Developmental Science, 7(2), 141–148.
Musk, E., & Neuralink (2019). An Integrated Brain-Machine Interface Platform with Thousands of Channels. Journal of Medical Internet Research, 21(10), e16194.
Nathan, M. J., & Walkington, C. (2017). Grounded and embodied mathematical cognition: Promoting mathematical insight and proof using action and language. Cognitive Research: Principles and Implications, 2(1), 9.
Norman, S. L., Maresca, D., Christopoulos, V. N., Griggs, W. S., Demene, C., Tanter, M., Shapiro, M. G., & Andersen, R. A. (2021). Single-trial decoding of movement intentions using functional ultrasound neuroimaging. Neuron, 109(9), 1554–1566.e4.
Oku, A. Y. A., & Sato, J. R. (2021). Predicting Student Performance Using Machine Learning in fNIRS Data. Frontiers in Human Neuroscience, 15, 622224.
Orsborn, A. L., Moorman, H. G., Overduin, S. A., Shanechi, M. M., Dimitrov, D. F., & Carmena, J. M. (2014). Closed-loop decoder adaptation shapes neural plasticity for skillful neuroprosthetic control. Neuron, 82(6), 1380–1393.
Parvizi, J., & Kastner, S. (2018). Human Intracranial EEG: Promises and Limitations. Nature Neuroscience, 21(4), 474–483.
Rao, R. P. N., Stocco, A., Bryan, M., Sarma, D., Youngquist, T. M., Wu, J., & Prat, C. S. (2014). A direct brain-to-brain interface in humans. PloS One, 9(11), e111332.
Ravn, S. (2022). Embodied Learning in Physical Activity: Developing Skills and Attunement to Interaction. Frontiers in Sports and Active Living, 4, 795733.
Rebolledo-Mendez, G. et al. (2009). Assessing NeuroSky’s Usability to Detect Attention Levels in an Assessment Exercise. Human-Computer Interaction: Lecture Notes in Computer Science. Springer, 5610, 149–158.
Saha, S., Mamun, K. A., Ahmed, K., Mostafa, R., Naik, G. R., Darvishi, S., Khandoker, A. H., & Baumert, M. (2021). Progress in Brain Computer Interface: Challenges and Opportunities. Frontiers in Systems Neuroscience, 15, 578875.
Serim, B., Spapé, M., & Jacucci, G. (2023). Revisiting embodiment for brain–computer interfaces. Human–Computer Interaction, 0(0), 1–27.
Smith, C., King, B., & Hoyte, J. (2014). Learning angles through movement: Critical actions for developing understanding in an embodied activity. The Journal of Mathematical Behavior, 36, 95–108.
Soloukey, S., Vincent, A. J. P. E., Smits, M., De Zeeuw, C. I., Koekkoek, S. K. E., Dirven, C. M. F., & Kruizinga, P. (2023). Functional Imaging of the Exposed Brain. Frontiers in Neuroscience, 17, 1087912.
Spüler, M. (2017). A high-speed brain-computer interface (BCI) using dry EEG electrodes. PloS One, 12(2), e0172400.
Sullivan, J. V. (2018). Learning and Embodied Cognition: A Review and Proposal. Psychology Learning & Teaching, 17(2), 128–143.
Tan, K. M., Daitch, A. L., Pinheiro-Chagas, P., Fox, K. C. R., Parvizi, J., & Lieberman, M. D. (2022). Electrocorticographic evidence of a common neurocognitive sequence for mentalizing about the self and others. Nature Communications, 13(1), 1919.
Tremmel, C., Herff, C., Sato, T., Rechowicz, K., Yamani, Y., & Krusienski, D. J. (2019). Estimating Cognitive Workload in an Interactive Virtual Reality Environment Using EEG. Frontiers in Human Neuroscience, 13, 401.
Vidal J. J. (1973). Toward direct brain-computer communication. Annual Review of Biophysics and Bioengineering, 2, 157–180.
Verkijika, S. F., & De Wet, L. (2015). Using a brain-computer interface (BCI) in reducing math anxiety: Evidence from South Africa. Computers & Education, 81, 113–122.
Vourvopoulos, A., & Badia, S. B. (2016). Usability and Cost-effectiveness in Brain-Computer Interaction: Is it User Throughput or Technology Related? Proceedings of the 7th Augmented Human International Conference, 16, 1–8.
Walter, C., Rosenstiel, W., Bogdan, M., Gerjets, P., & Spüler, M. (2017). Online EEG-based workload adaptation of an arithmetic learning environment. Frontiers in Human Neuroscience, 11, 286.
Wang, J., Conder, J. A., Blitzer, D. N., & Shinkareva, S. V. (2010). Neural representation of abstract and concrete concepts: a meta-analysis of neuroimaging studies. Human Brain Mapping, 31(10), 1459–1468.
Watanabe, K., Tanaka, H., Takahashi, K., Niimura, Y., Watanabe, K., & Kurihara, Y. (2016). NIRS-Based Language Learning BCI System. IEEE Sensors Journal, 16(8), 2726–2734.
Wegemer, C. (2019). Brain-computer interfaces and education: The state of technology and imperatives for the future. International Journal of Learning Technology, 14, 141.
Winslow, A. T., Brantley, J., Zhu, F., Contreras Vidal, J. L., & Huang, H. (2016). Corticomuscular coherence variation throughout the gait cycle during overground walking and ramp ascent: A preliminary investigation. Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference, 4634–4637.
Younger, J. W., Randazzo Wagner, M., & Booth, J. R. (2016). Weighing the Cost and Benefit of Transcranial Direct Current Stimulation on Different Reading Subskills. Frontiers in Neuroscience, 10, 262.
Zehr, E. P. (2015). The Potential Transformation of Our Species by Neural Enhancement. Journal of Motor Behavior, 47(1), 73–78.
Zeng, Y., Sun, K., & Lu, E. (2021). Declaration on the ethics of brain–computer interfaces and augment intelligence. AI and Ethics, 1(3), 209–211.
Zhang, H., & Jacobs, J. (2015). Traveling Theta Waves in the Human Hippocampus. The Journal of Neuroscience: The Official Journal of the Society for Neuroscience, 35(34), 12477–12487.