Selected Publications

  • Hatami, M., Yaghmaee, F., & Ebrahimpour, R. (2025). Improving Alzheimer’s disease classification using novel rewards in deep reinforcement learning, Biomedical Signal Processing and Control, Volume 100, Part C. https://doi.org/10.1016/j.bspc.2024.106920. [BSPC2025]
  • Hatami, M., Yaghmaee, F., & Ebrahimpour, R. (2024). Investigating the potential of reinforcement learning and deep learning in improving Alzheimer’s disease classification. Neurocomputing, 128119. https://doi.org/10.1016/j.neucom.2024.128119. [Nc2024]
  • Payedar-Ardakani, P., Gorji-Mahlabani, Y., Ghanbaran, A. H., & Ebrahimpour, R. (2024e). The impact of changes in daylight illuminance levels on architectural experiences in office environments using virtual reality and electroencephalogram. Journal of Building Engineering, 110487. https://doi.org/10.1016/j.jobe.2024.110487. [JoBE2024]
  • Payedar-Ardakani, P., Gorji-Mahlabani, Y., Ghanbaran, A. H., & Ebrahimpour, R. (2024b). Daylight illumination levels, user preferences, and cognitive performance in office environments: Exploring an optimal illumination range using virtual reality. Building and Environment, 258, 111638. https://doi.org/10.1016/j.buildenv.2024.111638. [B&E2024]

  • Roshan, S. S., Sadeghnejad, N., Sharifizadeh, F., & Ebrahimpour, R. (2024). A neurocomputational model of decision and confidence in object recognition task. Neural Networks, 175, 106318. https://doi.org/10.1016/j.neunet.2024.106318. [NN2024]

  • Asaadi, A. H., Amiri, S. H., Bosaghzadeh, A., & Ebrahimpour, R. (2024b). Effects and prediction of cognitive load on encoding model of brain response to auditory and linguistic stimuli in educational multimedia. Scientific Reports, 14(1). https://doi.org/10.1038/s41598-024-59411-x. [SR2024]

  • Sadeghnejad, N., Ezoji, M., Ebrahimpour, R., Qodosi, M., & Zabbah, S. (2024). A fully spiking coupled model of a deep neural network and a recurrent attractor explains dynamics of decision making in an object recognition task. Journal of Neural Engineering, 21(2), 026011. https://doi.org/10.1088/1741-2552/ad2d30. [JoNE2024]

  • Khaliran, M. U. K., Zabbah, I., Faraji, M., & Ebrahimpour, R. (2024). Improving deep learning in arrhythmia Detection: The application of modular quality and quantity controllers in data augmentation. Biomedical Signal Processing and Control, 91, 105940. https://doi.org/10.1016/j.bspc.2023.105940. [BSP&C2024]

  • Yahyaie, L., Ebrahimpour, R., & Koochari, A. (2024b). Pupil size variations reveal information about hierarchical Decision-Making processes. Cognitive Computation. https://doi.org/10.1007/s12559-024-10246-8. [CC2024]

  • Esmaily, J., Zabbah, S., Ebrahimpour, R., & Bahrami, B. (2023). Interpersonal alignment of neural evidence accumulation to social exchange of confidence. eLife, 12. https://doi.org/10.7554/elife.83722. [eLife2023]

  • Ghorbani, M., Izadi, F. S., Roshan, S. S., & Ebrahimpour, R. (2023). Assessing Prospective Teachers’ Geometric Transformations Thinking: A Van Hiele Theory-Based Analysis with Eye Tracking Cognitive Science Method. Technology of Education Journal (TEJ), 18(1), 67-88 .https://doi.org/10.22061/tej.2023.10350.2997. [ToEJ2023]

  • Mokari-Mahallati, M., Ebrahimpour, R., Bagheri, N., & Karimi-Rouzbahani, H. (2023). Deeper neural network models better reflect how humans cope with contrast variation in object recognition. Neuroscience Research, 192, 48–55. https://doi.org/10.1016/j.neures.2023.01.007. [NR2023]

  • Lee, J., Beirami, M. J., Ebrahimpour, R., Puyana, C., Tsoukas, M., & Avanaki, K. (2023). Optical coherence tomography confirms non‐malignant pigmented lesions in phacomatosis pigmentokeratotica using a support vector machine learning algorithm. Skin Research and Technology, 29(6). https://doi.org/10.1111/srt.13377. [SR&T2023]

  • Ghaderi-Kangavari, A., Parand, K., Ebrahimpour, R., Nunez, M. D., & Rad, J. A. (2023). How spatial attention affects the decision process: looking through the lens of Bayesian hierarchical diffusion model & EEG analysis. Journal of Cognitive Psychology, 35(4), 456–479. https://doi.org/10.1080/20445911.2023.2187714. [JoCP2023]

  • Alipour, M., Aminifar, E., Geary, D. C., & Ebrahimpour, R. (2023). Framing mathematical content in evolutionarily salient contexts improves students’ learning motivation. Learning and Motivation, 82, 101894. https://doi.org/10.1016/j.lmot.2023.101894. [L&M2023]

  • Harris, A. M. M., Esmaily, J., Zabbah, S., & Ebrahimpour, R. (2022). There is a Threshold for Decision’s Confidence in Hierarchical Perceptual Decision-Making. The Neuroscience Journal of Shefaye Khatam, 11(1), 25–36. https://doi.org/10.52547/shefa.11.1.25. [NJoFK2023]

  • Mousavi Harris, A. M., & Ebrahimpour, R. (2023). Investigation of Decision-Making Computational cortex-like mechanism models‎ in Neuroeducation. Technology of Education Journal (TEJ), 17(2), 249-264. https://doi.org/10.22061/tej.2023.8786.2728. [ToEJ2023]

  • Azizi, Z., & Ebrahimpour, R. (2023). Explaining Integration of Evidence Separated by Temporal Gaps with Frontoparietal Circuit Models. Neuroscience, 509, 74–95. https://doi.org/10.1016/j.neuroscience.2022.10.019. [N2023]

  • Moatari, M., Pazouki, E., Ebrahimpour, R., & Rezaee, M. R. (2022). Intelligent learners’ cognitive style detection based on their interaction in the English language teaching system. Technology of Education Journal (TEJ), 17(1), 209-232. https://doi.org/10.22061/tej.2023.8895.2749. [ToEJ2023]

  • Sadeghnejad, N., Ezoji, M., Ebrahimpour, R., & Zabbah, S. (2023). Resolving the neural mechanism of core object recognition in space and time: A computational approach. Neuroscience Research, 190, 36–50. https://doi.org/10.1016/j.neures.2022.12.002. [NR2022]

  • Darparnian, K., Azizi, Z., & Ebrahimpour, R. (2022). Investigating Decision-Making with Insufficient Evidence Using Behavioral Modeling. The Neuroscience Journal of Shefaye Khatam, 10(4), 10–19. https://doi.org/10.52547/shefa.10.4.10. [NJoSK2022]

  • Farkish, A., Bosaghzadeh, A., Amiri, S. H., & Ebrahimpour, R. (2022). Evaluating the effects of educational multimedia design principles on cognitive load using EEG signal analysis. Education and Information Technologies, 28(3), 2827–2843. https://doi.org/10.1007/s10639-022-11283-2. [E&IT2022]

  • Sarailoo, R., Latifzadeh, K., Amiri, S. H., Bosaghzadeh, A., & Ebrahimpour, R. (2022). Assessment of instantaneous cognitive load imposed by educational multimedia using electroencephalography signals. Frontiers in Neuroscience, 16. https://doi.org/10.3389/fnins.2022.744737. [FiN2022]

  • Alavi, S. M., Mirzaei, A., Valizadeh, A., & Ebrahimpour, R. (2022). Excitatory deep brain stimulation quenches beta oscillations arising in a computational model of the subthalamo-pallidal loop. Scientific Reports, 12(1). https://doi.org/10.1038/s41598-022-10084-4. [SR2022]

  • Merrikhi, Y., Shams-Ahmar, M., Karimi-Rouzbahani, H., Clark, K., Ebrahimpour, R., & Noudoost, B. (2021). Dissociable contribution of extrastriate responses to representational enhancement of gaze targets. Journal of Cognitive Neuroscience, 1–14. https://doi.org/10.1162/jocn_a_01750. [JoCN2021]

  • Farzmahdi, A., Fallah, F., Rajimehr, R., & Ebrahimpour, R. (2021). Task‐dependent neural representations of visual object categories. European Journal of Neuroscience, 54(7), 6445–6462. https://doi.org/10.1111/ejn.15440. [EJoN2021]

  • Karimi, M. H., Ebrahimpour, R., & Bagheri, N. (2021). A recurrent temporal model for semantic levels categorization based on human visual system. Computational Intelligence and Neuroscience, 2021, 1–20. https://doi.org/10.1155/2021/8895579. [CIN2021]

  • Heidari-Gorji, H., Ebrahimpour, R., & Zabbah, S. (2021). A temporal hierarchical feedforward model explains both the time and the accuracy of object recognition. Scientific Reports, 11(1). https://doi.org/10.1038/s41598-021-85198-2. [SR2021]‏

  • Jam, F., Azemati, H. R., Ghanbaran, A., Esmaily, J., & Ebrahimpour, R. (2022). The role of expertise in visual exploration and aesthetic judgment of residential building façades: An eye-tracking study. Psychology of Aesthetics Creativity and the Arts, 16(1), 148–163. https://doi.org/10.1037/aca0000377. [PoAC&A2021]

  • Modarres, M. H., Khazaie, V. R., Ghorbani, M., Ghoreyshi, A. M., AkhavanPour, A., Ebrahimpour, R., Vahabi, Z., Kalafatis, C., & Razavi, S. K. (2020). Early diagnosis of Alzheimer’s dementia with the artificial intelligence‐based Integrated Cognitive Assessment. Alzheimer S & Dementia, 16(S6). https://doi.org/10.1002/alz.042863. [A&D2020]

  • Eghdam, R., Ebrahimpour, R., Zabbah, I., & Zabbah, S. (2020). Inherent importance of early visual features in attraction of human attention. Computational Intelligence and Neuroscience, 2020, 1–15. https://doi.org/10.1155/2020/3496432. [CIN2020]

  • Farzmahdi, A., Ebrahimpour, R., & Freiwald, W. (2020). Mechanisms of facial tuning in a brain-inspired deep network. Journal of Vision, 20(11), 1463. https://doi.org/10.1167/jov.20.11.1463. [JoV2020]

  • Majdabadi, F., & Ebrahimpour, R. (2020). The role of explicit and implicit confidence in multi stage decisions. Advances in Cognitive Science, 22(3), 37–47. https://doi.org/10.30699/icss.22.3.37. [AC2020]

  • Anbaran, A. M., Torkzadeh, P., Ebrahimpour, R., & Bagheri, N. (2020). Modification and hardware implementation of cortex‐like object recognition model. IET Image Processing, 14(14), 3490–3498. https://doi.org/10.1049/iet-ipr.2019.0264. [IETIP2020]

  • Vafaei, S., Ebrahimpour, R., & Zabbah, S. (2020). The relationship between pupil diameter data and confidence in Multi-Stage decisions. The Neuroscience Journal of Shefaye Khatam, 8(4), 70–79. https://doi.org/10.29252/shefa.8.4.70. [NJoSK2020]

  • Sadeghnejad, N., Ezoji, M., & Ebrahimpour, R. (2020). A Temporal Computational Model for Object Recognition inspired by Human Visual System. Iranian Journal of Biomedical Engineering, 14(1), 69-79. https://doi.org/10.22041/ijbme.2020.119227.1548. [IJoBE2020]

  • Esmaily, J., Ebrahimpour, R., & Zabbah, S. (2019). Changing in the reaction time causes the confidence matching in group decision making. The Neuroscience Journal of Shefaye Khatam, 7(4), 61–70. https://doi.org/10.29252/shefa.7.4.61. [NJoSK2019]

  • Tohidi-Moghaddam, M., Zabbah, S., Olianezhad, F., & Ebrahimpour, R. (2019). Sequence-dependent sensitivity explains the accuracy of decisions when cues are separated with a gap. Attention Perception & Psychophysics, 81(8), 2745–2754. https://doi.org/10.3758/s13414-019-01810-8. [AP&P2019]

  • Rajaei, K., Mohsenzadeh, Y., Ebrahimpour, R., & Khaligh-Razavi, S. (2019). Beyond core object recognition: Recurrent processes account for object recognition under occlusion. PLoS Computational Biology, 15(5), e1007001. https://doi.org/10.1371/journal.pcbi.1007001. [PCB2019]

  • Shooshtari, S. V., Sadrabadi, J. E., Azizi, Z., & Ebrahimpour, R. (2019). Confidence representation of perceptual decision by EEG and eye data in a random dot motion task. Neuroscience, 406, 510–527. https://doi.org/10.1016/j.neuroscience.2019.03.031. [N2019]

  • Karimi-Rouzbahani, H., Vahab, E., Ebrahimpour, R., & Menhaj, M. B. (2019). Spatiotemporal analysis of category and target-related information processing in the brain during object detection. Behavioural Brain Research, 362, 224–239. https://doi.org/10.1016/j.bbr.2019.01.025. [BBR2019]

  • Olianezhad, F., Zabbah, S., Tohidi-Moghaddam, M., & Ebrahimpour, R. (2019). Residual information of previous decision affects evidence accumulation in current decision. Frontiers in Behavioral Neuroscience, 13. https://doi.org/10.3389/fnbeh.2019.00009. [FiBN2019]

  • Alavi, M., Mirzaei, A., & Ebrahimpour, R. (2019). Effects of regular and irregular deep brain stimulation on the basal ganglia dynamics: a computational approach. The Neuroscience Journal of Shefaye Khatam, 7(1), 1–12. https://doi.org/10.29252/shefa.7.1.1. [NJoSK2019]

  • Rajaei, K., Mohsenzadeh, Y., Ebrahimpour, R., & Razavi, S. M. K. (2018). The essential role of recurrent processing during object recognition under occlusion. Journal of Vision, 18(10), 906. https://doi.org/10.1167/18.10.906.

  • Merrikhi, Y., Ebrahimpour, R., & Daliri, A. (2018). Perceptual manifestations of auditory modulation during speech planning. Experimental Brain Research, 236(7), 1963–1969. https://doi.org/10.1007/s00221-018-5278-3. [EBR2018]

  • Arani, S. a. a. A., Kabir, E., & Ebrahimpour, R. (2018). Combining RtL and LtR HMMs to recognise handwritten Farsi words of small‐ and medium‐sized vocabularies. IET Computer Vision, 12(6), 925–932. https://doi.org/10.1049/iet-cvi.2017.0645. [IETCV2018]

  • Karimi-Rouzbahani, H., Bagheri, N., & Ebrahimpour, R. (2017). Invariant object recognition is a personalized selection of invariant features in humans, not simply explained by hierarchical feed-forward vision models. Scientific Reports, 7(1). https://doi.org/10.1038/s41598-017-13756-8. [SR2017]

  • Moini, S., Alizadeh, B., Emad, M., & Ebrahimpour, R. (2017). A Resource-Limited hardware accelerator for convolutional neural networks in embedded vision applications. IEEE Transactions on Circuits & Systems II Express Briefs, 64(10), 1217–1221. https://doi.org/10.1109/tcsii.2017.2690919. [IEEETCS2017]

  • Karimi-Rouzbahani, H., Bagheri, N., & Ebrahimpour, R. (2017a). Hard-wired feed-forward visual mechanisms of the brain compensate for affine variations in object recognition. Neuroscience, 349, 48–63. https://doi.org/10.1016/j.neuroscience.2017.02.050. [N2017]

  • Karimi-Rouzbahani, H., Bagheri, N., & Ebrahimpour, R. (2017a). Average activity, but not variability, is the dominant factor in the representation of object categories in the brain. Neuroscience, 346, 14–28. https://doi.org/10.1016/j.neuroscience.2017.01.002. [N2017]

  • Farzmahdi, A., Rajaei, K., Ghodrati, M., Ebrahimpour, R., & Khaligh-Razavi, S. (2016). A specialized face-processing model inspired by the organization of monkey face patches explains several face-specific phenomena observed in humans. Scientific Reports, 6(1). https://doi.org/10.1038/srep25025. [SR2016]

  • Heidari-Gorji, H., Zabbah, S., Akhavan, M., Bagheri, N., & Ebrahimpour, R. (2015). STDP based HAMX behavior in response to homogeneous and heterogeneous categories. In Bernstein Conference. Germany. [BC2015]

  • Kheradpisheh, S. R., Nowzari-Dalini, A., Ebrahimpour, R., & Ganjtabesh, M. (2014). An Evidence-Based combining classifier for brain signal analysis. PLoS ONE, 9(1), e84341. https://doi.org/10.1371/journal.pone.0084341. [PLoSOne2014]

  • Ghodrati, M., Farzmahdi, A., Rajaei, K., Ebrahimpour, R., & Khaligh-Razavi, S. (2014). Feedforward object-vision models only tolerate small image variations compared to human. Frontiers in Computational Neuroscience, 8. https://doi.org/10.3389/fncom.2014.00074. [FCN2014]

  • Zabbah, S., Rajaei, K., Mirzaei, A., Ebrahimpour, R., & Khaligh-Razavi, S. (2014). The impact of the lateral geniculate nucleus and corticogeniculate interactions on efficient coding and higher-order visual object processing. Vision Research, 101, 82–93. https://doi.org/10.1016/j.visres.2014.05.006. [VS2014]

  • Ghodrati, M., Rajaei, K., & Ebrahimpour, R. (2014). The importance of visual features in generic vs. specialized object recognition: a computational study. Frontiers in Computational Neuroscience, 8. https://doi.org/10.3389/fncom.2014.00078. [FCN2014]

  • Ahangi, A., Karamnejad, M., Mohammadi, N., Ebrahimpour, R., & Bagheri, N. (2012). Multiple classifier system for EEG signal classification with application to brain–computer interfaces. Neural Computing and Applications, 23(5), 1319–1327. https://doi.org/10.1007/s00521-012-1074-3. [NC&A2013]

  • Mirzaei, A., Khaligh-Razavi, S., Ghodrati, M., Zabbah, S., & Ebrahimpour, R. (2013). Predicting the human reaction time based on natural image statistics in a rapid categorization task. Vision Research, 81, 36–44. https://doi.org/10.1016/j.visres.2013.02.003. [VR2013]

  • Mirnaziri, M., Rahimi, M., Alavikakhaki, S., & Ebrahimpour, R. (2013). Using combination of µ, β and γ bands in classification of eeg signals. Basic and clinical neuroscience, 4(1), 76. [B&CN2013]

  • Rajaei, K., Khaligh-Razavi, S., Ghodrati, M., Ebrahimpour, R., & Abadi, M. E. S. A. (2012). A stable biologically motivated learning mechanism for visual feature extraction to handle facial categorization. PLoS ONE, 7(6), e38478. https://doi.org/10.1371/journal.pone.0038478. [PLoSOne2012]

  • Ghodrati, M., Khaligh-Razavi, S., Ebrahimpour, R., Rajaei, K., & Pooyan, M. (2012). How can selection of biologically inspired features improve the performance of a robust object recognition model? PLoS ONE, 7(2), e32357. https://doi.org/10.1371/journal.pone.0032357. [PLoSOne2012]