Nöroestetik Perspektif: EEG Verilerine Dayalı Duygu Durumu Odaklı Sanatsal Etkileşimler

An, S., Kang, M., Kim, S., Chikontwe, P., Shen, L., & Park, S. H. (2026). Subject-adaptive meta-learning for personalized BCI: A fusion of resting-state EEG signal and task-specific information. Information Fusion, 125, 103501. https://doi.org/10.1016/j.inffus.2025.103501

Andujar, M., Crawford, C. S., Nijholt, A., Jackson, F., & Gilbert, J. E. (2015). Artistic brain-computer interfaces: The expression and stimulation of the user’s affective state. Brain-Computer Interfaces, 2(2-3), 60-69. https://doi.org/10.1080/2326263x.2015.1104613

Annaby, M. H., Said, M. H., Eldeib, A. M., & Rushdi, M. A. (2021). EEG-based motor imagery classification using digraph Fourier transforms and extreme learning machines. Biomedical Signal Processing and Control, 69, 102831. https://doi.org/10.1016/j.bspc.2021.102831

Ardito, C., Colafiglio, T., Di Noia, T., & Di Sciascio, E. (2021). A biofeedback system to compose your own music while dancing. In Lecture Notes in Computer Science (ss. 309-312). Springer International Publishing. https://doi.org/10.1007/978-3-030-85607-6_27

Barwich, A.-S. (2017). Up the nose of the beholder? Aesthetic perception in olfaction as a decision-making process. New Ideas in Psychology, 47, 157-165. https://doi.org/10.1016/j.newideapsych.2017.03.013

Becker, S., Bräscher, A.-K., Bannister, S., Bensafi, M., Calma-Birling, D., Chan, R. C. K., Eerola, T., Ellingsen, D.-M., Ferdenzi, C., Hanson, J. L., Joffily, M., Lidhar, N. K., Lowe, L. J., Martin, L. J., Musser, E. D., Noll-Hussong, M., Olino, T. M., Pintos Lobo, R., & Wang, Y. (2019). The role of hedonics in the Human Affectome. Neuroscience Biobehavioral Reviews, 102, 221-241. https://doi.org/10.1016/j.neubiorev.2019.05.003

Belfi, A. M., Vessel, E. A., Brielmann, A., Isik, A. I., Chatterjee, A., Leder, H., Pelli, D. G., & Starr, G. G. (2019). Dynamics of aesthetic experience are reflected in the default-mode network. NeuroImage, 188, 584-597. https://doi.org/10.1016/j.neuroimage.2018.12.017

Beudt, S., & Jacobsen, T. (2015). On the role of mentalizing processes in aesthetic appreciation: an ERP study. Frontiers in Human Neuroscience, 9. https://doi.org/10.3389/fnhum.2015.00600

Blanco, A. D., Kroupi, E., Soria-Frisch, A., Gazzaley, A., Anadol, R., Maiques, A., & Ruffini, G. (2025). Enhancing mental well-being through AI-generated art: Insights from EEG responses to Refik Anadol’s unsupervised at MoMA. The Arts in Psychotherapy, 96, 102347. https://doi.org/10.1016/j.aip.2025.102347

Blanco-Díaz, C. F., Guerrero-Méndez, C. D., & Ruiz-Olaya, A. F. (2023). Enhancing P300 detection using a band-selective filter bank for a visual P300 speller. IRBM, 44(3), 100751. https://doi.org/10.1016/j.irbm.2022.100751

Brattico, E., Brusa, A., Dietz, M., Jacobsen, T., Fernandes, H. M., Gaggero, G., Toiviainen, P., Vuust, P., & Proverbio, A. M. (2025). Beauty and the brain - Investigating the neural and musical attributes of beauty during naturalistic music listening. Neuroscience, 567, 308-325. https://doi.org/10.1016/j.neuroscience.2024.12.008

Brodu, N., Lotte, F., & Lécuyer, A. (2012). Exploring two novel features for EEG-based brain-computer interfaces: Multifractal cumulants and predictive complexity. Neurocomputing, 79, 87-94. https://doi.org/10.1016/j.neucom.2011.10.010

Cai, M., & Zeng, Y. (2024). MAE-EEG-Transformer: A transformer-based approach combining masked autoencoder and cross-individual data augmentation pre-training for EEG classification. Biomedical Signal Processing and Control, 94, 106131. https://doi.org/10.1016/j.bspc.2024.106131

Cela-Conde, C. J., Agnati, L., Huston, J. P., Mora, F., & Nadal, M. (2011). The neural foundations of aesthetic appreciation. Progress in Neurobiology, 94(1), 39-48. https://doi.org/10.1016/j.pneurobio.2011.03.003

Chatterjee, A., & Vartanian, O. (2014). Neuroaesthetics. Trends in Cognitive Sciences, 18(7), 370-375. https://doi.org/10.1016/j.tics.2014.03.003

Chen, X., Ibrahim, Z., & Aziz, A. A. (2025). Predicting emotional responses in interactive art using Random Forests: A model grounded in enactive aesthetics. Frontiers in Psychology, 16. https://doi.org/10.3389/fpsyg.2025.1609103

Chen, Z., Liao, J., Chen, J., Zhou, C., Chai, F., Wu, Y., & Hansen, P. (2021). Paint with your mind: Designing EEG-based interactive installation for traditional Chinese artworks. Proceedings of the Fifteenth International Conference on Tangible, Embedded, and Embodied Interaction, 1-6. https://doi.org/10.1145/3430524.3442455

Cheng, S. (2021). Visual expression of emotion in dynamic 3D painting system based on emotion synthesis model. Frontiers in Psychology, 12. https://doi.org/10.3389/fpsyg.2021.730066

Cinzia, D. D., & Vittorio, G. (2009). Neuroaesthetics: A review. Current Opinion in Neurobiology, 19(6), 682-687. https://doi.org/10.1016/j.conb.2009.09.001

Cui, X., Wu, Y., Wu, J., You, Z., Xiahou, J., & Ouyang, M. (2022). A review: Music-emotion recognition and analysis based on EEG signals. Frontiers in Neuroinformatics, 16. https://doi.org/10.3389/fninf.2022.997282

Das, N., & Chakraborty, M. (2025). Optimal multimodal feature combination and classifier selection for music-based EEG signal analysis. Computers in Biology and Medicine, 196, 110696. https://doi.org/10.1016/j.compbiomed.2025.110696

Daşdemir, Y. (2024). Virtual reality-enabled high-performance emotion estimation with the most significant channel pairs. Heliyon, 10(20), e38681. https://doi.org/10.1016/j.heliyon.2024.e38681

Díaz-Vera, J. E. (2025). The situatedness of aesthetic emotions: A review of the literature and a proposal for its study in variationist linguistics. Language Sciences, 111, 101744. https://doi.org/10.1016/j.langsci.2025.101744

dos Santos, E. M., Cassani, R., Falk, T. H., & Fraga, F. J. (2020). Improved motor imagery brain-computer interface performance via adaptive modulation filtering and two-stage classification. Biomedical Signal Processing and Control, 57, 101812. https://doi.org/10.1016/j.bspc.2019.101812

Du, X., Liang, K., Lv, Y., & Qiu, S. (2024). Fast reconstruction of EEG signal compression sensing based on deep learning. Scientific Reports, 14(1). https://doi.org/10.1038/s41598-024-55334-9

Du, X., Xi, M., Ding, X., Wang, F., Qiu, S., Lv, Y., & Liu, Q. (2025). Motor imagery EEG signal classification based on deformable convolution v3 and adaptive spatial attention mechanism. Biomedical Signal Processing and Control, 99, 106905. https://doi.org/10.1016/j.bspc.2024.106905

Dünya Danışmanlık Merkezi. (2026). Beyin Bölgeleri. https://dunyadanismanlikmerkezi.com/beyin-bolgeleri/

Else, J. E., Ellis, J., & Orme, E. (2015). Art expertise modulates the emotional response to modern art, especially abstract: An ERP investigation. Frontiers in Human Neuroscience, 9. https://doi.org/10.3389/fnhum.2015.00525

Fu, X., Liu, X., & Li, Z. (2024). Catching eyes of social media wanderers: How pictorial and textual cues in visitor-generated content shape users’ cognitive-affective psychology. Tourism Management, 100, 104815. https://doi.org/10.1016/j.tourman.2023.104815

Górriz, J. M., Álvarez-Illán, I., Álvarez-Marquina, A., Arco, J. E., Atzmueller, M., Ballarini, F., Barakova, E., Bologna, G., Bonomini, P., Castellanos-Dominguez, G., Castillo-Barnes, D., Cho, S. B., Contreras, R., Cuadra, J. M., Domínguez, E., Domínguez-Mateos, F., Duro, R. J., Elizondo, D., Fernández-Caballero, A., … Ferrández-Vicente, J. M. (2023). Computational approaches to Explainable Artificial Intelligence: Advances in theory, applications and trends. Information Fusion, 100, 101945. https://doi.org/10.1016/j.inffus.2023.101945

Hamavar, R., & Asl, B. M. (2026). EEG augmentation for emotion classification using multi-branch Wasserstein conditional GAN. Biomedical Signal Processing and Control, 113, 108908. https://doi.org/10.1016/j.bspc.2025.108908

Hänselmann, S., Schneiders, M., Weidner, N., & Rupp, R. (2015). Transcranial magnetic stimulation for individual identification of the best electrode position for a motor imagery-based brain-computer interface. Journal of NeuroEngineering and Rehabilitation, 12(1). https://doi.org/10.1186/s12984-015-0063-z

Hooper, J., Stoliker, D., Wolfe, K., & Hutchison, K. (2025). Neuroaesthetics of the psychedelic state. Neuropsychologia, 217, 109238. https://doi.org/10.1016/j.neuropsychologia.2025.109238

Jiang, H., Chen, Y., Wu, D., & Yan, J. (2024). EEG-driven automatic generation of emotive music based on transformer. Frontiers in Neurorobotics, 18. https://doi.org/10.3389/fnbot.2024.1437737

Kreplin, U., & Fairclough, S. H. (2015). Effects of self-directed and other-directed introspection and emotional valence on activation of the rostral prefrontal cortex during aesthetic experience. Neuropsychologia, 71, 38-45. https://doi.org/10.1016/j.neuropsychologia.2015.03.013

Leder, H., Belke, B., Oeberst, A., & Augustin, D. (2004). A model of aesthetic appreciation and aesthetic judgments. British Journal of Psychology, 95(4), 489-508. https://doi.org/10.1348/0007126042369811

Li, J., Lee, C.-H., Duan, D., Zhou, Y., Xie, X., Wan, X., Liu, T., Li, D., Yu, H., Hasan, W. Z. W., Song, H., & Wen, D. (2025). A novel AI-driven EEG images emotion recognition generalized classification model for cross-subject analysis. Advanced Engineering Informatics, 68, 103744. https://doi.org/10.1016/j.aei.2025.103744

Li, R., & Zhang, J. (2020). Review of computational neuroaesthetics: Bridging the gap between neuroaesthetics and computer science. Brain Informatics, 7(1). https://doi.org/10.1186/s40708-020-00118-w

Lieto, A., Pozzato, G. L., Zoia, S., Patti, V., & Damiano, R. (2021). A commonsense reasoning framework for explanatory emotion attribution, generation and re-classification. Knowledge-Based Systems, 227, 107166. https://doi.org/10.1016/j.knosys.2021.107166

Lin, R. R., & Zhang, K. (2024). Survey of real-time brainmedia in artistic exploration. Visual Computing for Industry, Biomedicine, and Art, 7(1). https://doi.org/10.1186/s42492-024-00179-2

Liu, J., Wu, G., Luo, Y., Qiu, S., Yang, S., Li, W., & Bi, Y. (2020). EEG-based emotion classification using a deep neural network and sparse autoencoder. Frontiers in Systems Neuroscience, 14. https://doi.org/10.3389/fnsys.2020.00043

Liu, W., Guo, J., & Li, H. (2024). Using artworks to understand human memory and its neural mechanisms. New Ideas in Psychology, 74, 101095. https://doi.org/10.1016/j.newideapsych.2024.101095

Liu, Y., Teng, F., Zhang, S., & Du, F. (2024). Neuroaesthetic Indicators from the Perspective of Two-Stage Process Theory: N2 and LPP can Predict Aesthetic Judgment of Human-Computer Interfaces. International Journal of Human-Computer Interaction, 41(9), 5485-5497. https://doi.org/10.1080/10447318.2024.2364138

Liu, Y., Zhong, B., Wang, J., & Song, Y. (2026). A study of danmu: Detecting emotional coherence in music videos through synchronized EEG analysis. Computers in Human Behavior, 174, 108803. https://doi.org/10.1016/j.chb.2025.108803

Ma, W., Xue, H., Sun, X., Mao, S., Wang, L., Liu, Y., Wang, Y., & Lin, X. (2022). A novel multi-branch hybrid neural network for motor imagery EEG signal classification. Biomedical Signal Processing and Control, 77, 103718. https://doi.org/10.1016/j.bspc.2022.103718

Mazzacane, S., Coccagna, M., Manzella, F., Pagliarini, G., Sironi, V. A., Gatti, A., Caselli, E., & Sciavicco, G. (2023). Towards an objective theory of subjective liking: A first step in understanding the sense of beauty. PLOS ONE, 18(6), e0287513. https://doi.org/10.1371/journal.pone.0287513

Nadal, M. (2013). The experience of art. İçinde Progress in Brain Research (ss. 135-158). Elsevier. https://doi.org/10.1016/b978-0-444-63287-6.00007-5

Newton, H. B., Ferrer, A. J., Hudson, I., & King, J. (2024). Music therapy and art therapy: Functional neurobiology and applications in oncology. İçinde Neuropsychological and Psychosocial Foundations of Neuro-Oncology (ss. 419-454). Elsevier. https://doi.org/10.1016/b978-0-443-15663-2.00013-4

Nicolas-Alonso, L. F., Corralejo, R., Gomez-Pilar, J., Álvarez, D., & Hornero, R. (2015). Adaptive semi-supervised classification to reduce intersession non-stationarity in multiclass motor imagery-based brain-computer interfaces. Neurocomputing, 159, 186-196. https://doi.org/10.1016/j.neucom.2015.02.005

Nouri, M., Moradi, F., Ghaemi, H., & Motie Nasrabadi, A. (2023). Towards real-world BCI: CCSPNet, a compact subject-independent motor imagery framework. Digital Signal Processing, 133, 103816. https://doi.org/10.1016/j.dsp.2022.103816

Orlandi, A., & Candidi, M. (2025). Toward a neuroaesthetics of interactions: Insights from dance on the aesthetics of individual and interacting bodies. iScience, 28(5), 112365. https://doi.org/10.1016/j.isci.2025.112365

Ozbay, Y., Oosterwijk, S., & Stamkou, E. (2024). Beyond beauty: Does visual art facilitate social cognitive skills? PLOS ONE, 19(10), e0308392. https://doi.org/10.1371/journal.pone.0308392

Qiu, S., Chen, Y., Yang, Y., Wang, P., Wang, Z., Zhao, H., Kang, Y., & Nie, R. (2024). A review on semi-supervised learning for EEG-based emotion recognition. Information Fusion, 104, 102190. https://doi.org/10.1016/j.inffus.2023.102190

Rashid, N., Iqbal, J., Mahmood, F., Abid, A., Khan, U. S., & Tiwana, M. I. (2018). Artificial immune system-negative selection classification algorithm (NSCA) for four class electroencephalogram (EEG) signals. Frontiers in Human Neuroscience, 12. https://doi.org/10.3389/fnhum.2018.00439

Refikanadol. (2023). Şifa Duygusu: Yapay Zeka Veri Heykeli. https://refikanadol.com/works/sense-of-healing-ai-data-sculpture/

Reimann, M., Zaichkowsky, J., Neuhaus, C., Bender, T., & Weber, B. (2010). Aesthetic package design: A behavioral, neural, and psychological investigation. Journal of Consumer Psychology, 20(4), 431-441. https://doi.org/10.1016/j.jcps.2010.06.009

Riccio, P., Galati, F., Zuluaga, M. A., De Martin, J. C., & Nichele, S. (2022). Translating Emotions from EEG to Visual Arts. İçinde Lecture Notes in Computer Science (ss. 243-258). Springer International Publishing. https://doi.org/10.1007/978-3-031-03789-4_16

Righi, S., Gronchi, G., Pierguidi, G., Messina, S., & Viggiano, M. P. (2017). Aesthetic shapes our perception of every-day objects: An ERP study. New Ideas in Psychology, 47, 103-112. https://doi.org/10.1016/j.newideapsych.2017.03.007

Rimbert, S., & Fleck, S. (2023). Long-term kinesthetic motor imagery practice with a BCI: Impacts on user experience, motor cortex oscillations and BCI performances. Computers in Human Behavior, 146, 107789. https://doi.org/10.1016/j.chb.2023.107789

Sbriscia-Fioretti, B., Berchio, C., Freedberg, D., Gallese, V., & Umiltà, M. A. (2013). ERP modulation during observation of abstract paintings by Franz Kline. PLoS ONE, 8(10), e75241. https://doi.org/10.1371/journal.pone.0075241

Shen, C., & Namiki, A. (2025). A topology-aware multiscale feature fusion network for EEG-based motor imagery decoding. Knowledge-Based Systems, 330, 114540. https://doi.org/10.1016/j.knosys.2025.114540

Siri, F., Ferroni, F., Ardizzi, M., Kolesnikova, A., Beccaria, M., Rocci, B., Christov-Bakargiev, C., & Gallese, V. (2018). Behavioral and autonomic responses to real and digital reproductions of works of art. İçinde Progress in Brain Research (ss. 201-221). Elsevier. https://doi.org/10.1016/bs.pbr.2018.03.020

Su, Y., Liu, Y., Xiao, Y., Ma, J., & Li, D. (2024). A review of artificial intelligence methods enabled music-evoked EEG emotion recognition and their applications. Frontiers in Neuroscience, 18. https://doi.org/10.3389/fnins.2024.1400444

Subasi, A., Tuncer, T., Dogan, S., Tanko, D., & Sakoglu, U. (2021). EEG-based emotion recognition using tunable Q wavelet transform and rotation forest ensemble classifier. Biomedical Signal Processing and Control, 68, 102648. https://doi.org/10.1016/j.bspc.2021.102648

Thomson, P., & Jaque, S. V. (2017). Neurobiology, creativity, and performing artists. İçinde Creativity and the Performing Artist (ss. 79-102). Elsevier. https://doi.org/10.1016/b978-0-12-804051-5.00006-8

Tinio, P. P. L., & Gartus, A. (2018). Characterizing the emotional response to art beyond pleasure: Correspondence between the emotional characteristics of artworks and viewers’ emotional responses. İçinde Progress in Brain Research (ss. 319-342). Elsevier. https://doi.org/10.1016/bs.pbr.2018.03.005

Turkheimer, F. E., Fagerholm, E. D., Vignando, M., Dafflon, J., Da Costa, P. F., Dazzan, P., & Leech, R. (2020). A GABA interneuron deficit model of the art of Vincent van Gogh. Frontiers in Psychiatry, 11. https://doi.org/10.3389/fpsyt.2020.00685

van Leeuwen, J. E. P., Boomgaard, J., Bzdok, D., Crutch, S. J., & Warren, J. D. (2022). More than meets the eye: Art engages the social brain. Frontiers in Neuroscience, 16. https://doi.org/10.3389/fnins.2022.738865

Vessel, E. A. (2022). Neuroaesthetics. İçinde Encyclopedia of Behavioral Neuroscience, 2nd edition (ss. 661-670). Elsevier. https://doi.org/10.1016/b978-0-12-809324-5.24104-7

Vessel, E. A., Starr, G. G., & Rubin, N. (2013). Art reaches within: Aesthetic experience, the self and the default mode network. Frontiers in Neuroscience, 7. https://doi.org/10.3389/fnins.2013.00258

Vijay Sanker, S., Ramya Sri Bilakanti, N. B., Thomas, A., Gopi, V. P., & Palanisamy P. (2022). Emotion-recognition-based music therapy system using electroencephalography signals. İçinde Edge-of-Things in Personalized Healthcare Support Systems (ss. 217-235). Elsevier. https://doi.org/10.1016/b978-0-323-90585-5.00009-6

Wang, D., Lian, J., Cheng, H., & Zhou, Y. (2024). Music-evoked emotions classification using vision transformer in EEG signals. Frontiers in Psychology, 15. https://doi.org/10.3389/fpsyg.2024.1275142

Welter, M., & Lotte, F. (2024). Ecological decoding of visual aesthetic preference with oscillatory electroencephalogram features—A mini-review. Frontiers in Neuroergonomics, 5. https://doi.org/10.3389/fnrgo.2024.1341790

Wu, L., Chau, K. T., Wan Yahaya, W. A. J., Wang, S., & Wu, X. (2024). The effect of electroencephalogram feedback in virtual reality interactive system on creativity performance, attention value, and cognitive load. International Journal of Human-Computer Interaction, 41(10), 5955-5972. https://doi.org/10.1080/10447318.2024.2371692

Yadav, H., & Maini, S. (2025). Decoding brain signals: A comprehensive review of EEG-Based BCI paradigms, signal processing and applications. Computers in Biology and Medicine, 196, 110937. https://doi.org/10.1016/j.compbiomed.2025.110937

Yang, J., Wang, L., Cai, W., Zhang, L., Xie, C., & Wang, Z. (2025). EDANet: Efficient domain-adaptive attention neural network for EEG classification of motor imagery. Expert Systems with Applications, 294, 128783. https://doi.org/10.1016/j.eswa.2025.128783

Yang, Q., Feng, S., Zhao, T., & Kalantari, S. (2024). Design with myself: A brain-computer interface design tool that predicts live emotion to enhance metacognitive monitoring of designers. International Journal of Human-Computer Studies, 185, 103229. https://doi.org/10.1016/j.ijhcs.2024.103229

Yu, Y.-C. (2025). Research on emotion-based inspiration mechanism in art creation by generative AI. Mathematics, 13(16), 2597. https://doi.org/10.3390/math13162597