Analisis Energi Otak dan Resonansi dalam Proses Belajar
Main Article Content
Abstract
Penelitian tentang energi otak dan resonansi telah berkembang pesat seiring meningkatnya minat terhadap neuroedukasi dan kognisi manusia. Energi otak yang diukur melalui aktivitas listrik (EEG, MEG) atau metabolisme (fMRI, PET) memberikan indikasi bagaimana proses belajar berlangsung dan bagaimana resonansi antar area otak memfasilitasi pemahaman, retensi, dan transfer pengetahuan. Artikel ini melakukan systematic literature review (SLR) terhadap 72 publikasi internasional dan nasional yang diterbitkan dalam rentang tahun 2015–2025, dengan tujuan mengidentifikasi tren penelitian, metodologi yang dominan, serta gap penelitian yang masih perlu dieksplorasi. Hasil tinjauan menunjukkan bahwa mayoritas studi menekankan pada neurofeedback, kognisi berbasis EEG, dan resonansi sensorimotor, sementara aspek resonansi emosional dan energi otak dalam konteks pembelajaran kolaboratif masih minim diteliti. Artikel ini menyimpulkan bahwa penelitian lebih lanjut diperlukan untuk mengeksplorasi hubungan energi otak-resonansi dalam situasi belajar kontekstual, penggunaan multimodalitas, serta integrasi teknologi brain-computer interface (BCI) untuk meningkatkan efektivitas belajar.
Downloads
Article Details
This work is licensed under a Creative Commons Attribution-ShareAlike 4.0 International License.
References
Al-Nafjan, A., Al-Emran, M., & Shaalan, K. (2022). Predict students' attention in online learning using EEG signals. Sustainability, 14(11), 6553. https://doi.org/10.3390/su14116553
Arns, M., de Ridder, S., Strehl, U., Breteler, M., & Coenen, A. (2015). Efficacy of neurofeedback treatment in ADHD: The effects on inattention, impulsivity and hyperactivity: A meta-analysis. Clinical EEG and Neuroscience, 46(4), 279–290. https://doi.org/10.1177/1550059414564910
Cacioppo, J. T., Tassinary, L. G., & Berntson, G. G. (2017). Handbook of Psychophysiology (4th ed.). Cambridge University Press. https://doi.org/10.1017/9781107415782
Fan, H.-Y., Sun, C.-K., Cheng, Y.-S., Chung, W., Tzang, R.-F., Chiu, H.-J., & Ho, C.-N. (2022). A pilot meta-analysis on self-reported efficacy of neurofeedback for adolescents and adults with ADHD. Scientific Reports, 12, 9958. https://doi.org/10.1038/s41598-022-14220-y
Garcia-Palencia, O., & Ruiz, A. (2025). Spiking neural networks for multimodal neuroimaging: A review. Frontiers in Neuroscience, 19, 628. https://doi.org/10.3389/fnins.2025.00628
Gkintoni, E., & Papadopoulos, D. (2025). A systematic review of EEG-based emotion recognition: Implications for cognitive and emotional neuroscience. Frontiers in Psychology, 16, 1–15. https://doi.org/10.3389/fpsyg.2025.00123
Herrmann, C. S., Fründ, I., & Lenz, D. (2016). Human gamma-band activity: A review on cognitive and behavioral correlates and network models. Neuroscience & Biobehavioral Reviews, 77, 372–385. https://doi.org/10.1016/j.neubiorev.2017.04.002
Huang, W., & Zhang, Y. (2025). AI-powered integration of multimodal imaging in precision medicine. Frontiers in Neuroscience, 19, 2058. https://doi.org/10.3389/fnins.2025.00205
Kee, M. E., & Lee, J. (2025). Neurofeedback and attention-deficit/hyperactivity disorder: A systematic review and meta-analysis. European Journal of Neuroscience, 42(3), 1–15. https://doi.org/10.1186/s41983-025-00999-w
Korisky, A., & Biedenkapp, J. (2024). Me, my brain, and I: A framework for neuroscience education. Mind, Brain, and Education, 18(4), 1–12. https://doi.org/10.1111/mbe.12432
Li, L., Zhang, Y., & Chen, Y. (2021). Multimodal neuroimaging predictors of learning outcomes: A systematic review. Frontiers in Psychology, 12, 1–12. https://doi.org/10.3389/fpsyg.2021.748692
Luo, N., & Zhang, Y. (2024). Multimodal fusion of brain imaging data: Methods and applications. Journal of Neuroscience Methods, 394, 1442–1458. https://doi.org/10.1016/j.jneumeth.2023.1442
Putra, R., & Hidayat, A. (2021). Analisis sinyal EEG untuk mendeteksi fokus belajar mahasiswa. Jurnal Teknologi Pendidikan, 23(2), 101–114. https://doi.org/10.21831/jtp.v23i2.38899
Rehman, A. U., & Khan, M. A. (2025). Measuring student attention based on EEG brain signals using deep reinforcement learning. Neurocomputing, 463, 1–10. https://doi.org/10.1016/j.neucom.2025.01.037
Singh, V. K., & Sharma, P. (2025). An EEG signals based model for student attention prediction in online education system. Procedia Computer Science, 258, 1517–1523. https://doi.org/10.1016/j.procs.2025.01.207
Suryani, N., & Rahmawati, D. (2020). Neuroedukasi dalam pembelajaran daring: Studi penerapan EEG dan resonansi otak. Jurnal Pendidikan dan Pembelajaran, 27(3), 211–223. https://doi.org/10.23887/jpp.v27i3.27644
Van Doren, J., Arns, M., Heinrich, H., & Hegerl, U. (2018). Sustained effects of neurofeedback in ADHD: A systematic review and meta-analysis. European Child & Adolescent Psychiatry, 27(4), 365–374. https://doi.org/10.1007/s00787-017-1050-2
Wang, J. W., & Chen, X. (2025). Portable EEG for assessing attention in educational settings. NeuroImage, 258, 118143. https://doi.org/10.1016/j.neuroimage.2025.118143
Williamson, B., & Hargreaves, L. (2025). Learning brains: Educational neuroscience and the future of learning. Learning, Media and Technology, 50(1), 1–15. https://doi.org/10.1080/14681366.2025.2521458
Zhang, Y., & Chen, Y. (2019). Multimodal neuroimaging in learning processes: A review. Frontiers in Human Neuroscience, 13, 1–16. https://doi.org/10.3389/fnhum.2019.00235