Afzali, A., Khaleghi, A., Hatef, B., Akbari Movahed, R., & Pirzad Jahromi, G. (2023). Automated major depressive disorder diagnosis using a dual-input deep learning model and image generation from EEG signals. Waves in Random and Complex Media, pp. 1–16.

Anter, A. M., Zhang, Z., & RLWOA-SOFL. (2023). A new learning model-based reinforcement swarm intelligence and self-organizing deep fuzzy rules for fMRI pain decoding. Ieee Transactions on Affective Computing, 15(2), 644–656.

Google Scholar 

Awangga, R. M., Mengko, T. L. R., & Utama, N. P. (2020). A literature review of brain decoding research, in IOP Conference Series: Materials Science and Engineering, IOP Publishing, p. 032049.

Bai, T., Li, Y., Shen, Y., Zhang, X., Zhang, W., & Cui, B. (2023). Transfer learning for Bayesian optimization: A survey, arXiv preprint arXiv: 2302.05927.

Bashashati, H., Ward, R. K., & Bashashati, A. (2016). User-customized brain computer interfaces using bayesian optimization. Journal of Neural Engineering, 13(2), 026001.

PubMed  Google Scholar 

Bowring, A., Maumet, C., & Nichols, T. E. (2019). Exploring the impact of analysis software on task fMRI results. Human Brain Mapping, 40(11), 3362–3384.

PubMed  PubMed Central  Google Scholar 

Budak, Ü., Çıbuk, M., Cömert, Z., & Şengür, A. (2021). Efficient COVID-19 segmentation from CT slices exploiting semantic segmentation with integrated attention mechanism. Journal of Digital Imaging, 34(2), 263–272.

PubMed  PubMed Central  Google Scholar 

Campos-Ugaz, W. A., Garay, J. P. P., Rivera-Lozada, O., Diaz, M. A. A., Fuster-Guillén, D., & Arana, A. A. T. (2023). An overview of bipolar disorder diagnosis using machine learning approaches: Clinical opportunities and challenges. Iran J Psychiatry, 18(2), 237.

PubMed  PubMed Central  Google Scholar 

Castanho, E. N., Aidos, H., & Madeira, S. C. (2022). Biclustering fMRI time series: A comparative study. Bmc Bioinformatics, 23(1), 192.

PubMed  PubMed Central  Google Scholar 

Duncan, K. J., Pattamadilok, C., Knierim, I., & Devlin, J. T. (2009). Consistency and variability in functional localisers. Neuroimage, 46(4), 1018–1026.

PubMed  Google Scholar 

Dutta, A. K. (2024). Deep learning-based multi-head self-attention model for human epilepsy identification from EEG signal for biomedical traits. Multimed Tools Appl, pp. 1–23.

Garnett, R. (2023). Bayesian optimization. Cambridge University Press.

Glaser, J. I., Benjamin, A. S., Chowdhury, R. H., Perich, M. G., Miller, L. E., & Kording, K. P. (2020). Machine learning for neural decoding, eNeuro, vol. 7, no. 4.

Golestani, A. M., & Chen, J. J. (2022). Performance of Temporal and Spatial independent component analysis in identifying and removing low-frequency physiological and motion effects in resting-state fMRI. Front Neurosci, 16, 867243.

PubMed  PubMed Central  Google Scholar 

Guntupalli, J. S., Hanke, M., Halchenko, Y. O., Connolly, A. C., Ramadge, P. J., & Haxby, J. V. (2016). A model of representational spaces in human cortex. Cerebral Cortex, 26(6), 2919–2934.

PubMed  PubMed Central  Google Scholar 

Haxby, J. V., Connolly, A. C., & Guntupalli, J. S. (2014). Decoding neural representational spaces using multivariate pattern analysis. Annual Review of Neuroscience, 37(1), 435–456.

PubMed  CAS  Google Scholar 

Hazimeh, H., & Mazumder, R. (2020). Fast best subset selection: Coordinate descent and local combinatorial optimization algorithms. Operations Research, 68(5), 1517–1537.

Google Scholar 

Hourani, O., Charkari, N. M., & Jalili, S. (2021). An ensemble multiview learning method for visual object decoding from fMRI brain data. Signal and Data Processing, 18(3), 109–126.

Google Scholar 

Hourani, O., Charkari, N. M., & Jalili, S. (2023). New insight of human brain connectivity mapping based on Inter-correlation of Multi-view fMRI decoder. Authorea Preprints.

Huang, W., et al. (2020). Perception-to-image: Reconstructing natural images from the brain activity of visual perception. Annals of Biomedical Engineering, 48, 2323–2332.

PubMed  Google Scholar 

Huang, S., Shao, W., Wang, M. L., & Zhang, D. Q. (2021a). fmri-based decoding of visual information from human brain activity: A brief review. International Journal of Automation and Computing, 18(2), 170–184.

Google Scholar 

Huang, W., et al. (2021b). Deep natural image reconstruction from human brain activity based on conditional progressively growing generative adversarial networks. Neuroscience Bulletin, 37, 369–379.

PubMed  Google Scholar 

Huang, W., et al. (2021c). A neural decoding algorithm that generates Language from visual activity evoked by natural images. Neural Networks, 144, 90–100.

PubMed  Google Scholar 

Huang, W., et al. (2021d). A dual-channel Language decoding from brain activity with progressive transfer training. Human Brain Mapping, 42(15), 5089–5100.

PubMed  PubMed Central  Google Scholar 

Huang, W., et al. (2024). From sight to insight: A multi-task approach with the visual Language decoding model. Information Fusion, 112, 102573.

Google Scholar 

Huang, W., Tang, Y., Wang, S., Li, J., Cheng, K., & Yan, H. (2025). Unraveling the differential efficiency of dorsal and ventral pathways in visual semantic decoding. International Journal of Neural Systems, p. 2550009.

Kaheni, H., Shiran, M. B., Kamrava, S. K., & Zare-Sadeghi, A. (2024). Intra and inter-regional functional connectivity of the human brain due to Task-Evoked fMRI data classification through CNN & LSTM. Journal of Neuroradiology, 51(4), 101188.

PubMed  Google Scholar 

Khaleghi, A., Zarafshan, H., & Mohammadi, M. R. (2019). Visual and auditory steady-state responses in attention-deficit/hyperactivity disorder. Eur Arch Psychiatry Clin Neurosci, 269, 645–655.

PubMed  Google Scholar 

Khaleghi, A., Birgani, P. M., Fooladi, M. F., & Mohammadi, M. R. (2020). Applicable features of electroencephalogram for ADHD diagnosis. Research on Biomedical Engineering, 36, 1–11.

Google Scholar 

Khaleghi, A., Mohammadi, M. R., Shahi, K., & Nasrabadi, A. M. (2022). Computational neuroscience approach to psychiatry: A review on theory-driven approaches. Clinical Psychopharmacology and Neuroscience, 20(1), 26.

PubMed  PubMed Central  Google Scholar 

Khaleghi, A., Mohammadi, M. R., Shahi, K., & Nasrabadi, A. M. (2023). Possible neuropathological mechanisms underlying the increased complexity of brain electrical activity in schizophrenia: A computational study. Iran J Psychiatry, 18(2), 127.

PubMed  PubMed Central  Google Scholar 

Khaleghi, A., Mohammadi, M. R., Shahi, K., & Motie Nasrabadi, A. (2024a). A neuronal population model based on cellular automata to simulate the electrical waves of the brain. Waves in Random and Complex Media, 34(3), 1445–1464.

Google Scholar 

Khaleghi, A., Shahi, K., Saidi, M., Babaee, N., Kaveh, R., & Mohammadian, A. (2024b). Linear and nonlinear analysis of multimodal physiological data for affective arousal recognition. Cognitive Neurodynamics, pp. 1–12.

Kiliç, K., Uncu, Ö., & Türksen, I. B. (2007). Comparison of different strategies of utilizing fuzzy clustering in structure identification. Inf Sci (N Y), 177(23), 5153–5162.

Google Scholar 

Knyazev, G. G., Savostyanov, A. N., Rudych, P. D., Bocharov, A. V., & MULTI-VOXEL PATTERN ANALYSIS OF fMRI DATA DURING SELF-AND OTHER-REFERENTIAL PROCESSING. (2023). IP Pavlov Journal of Higher Nervous Activity, 73, 2, 242–255.

Google Scholar 

Krichen, M. (2023). Convolutional neural networks: A survey. Computers, 12(8), 151.

Google Scholar 

Li, R., et al. (2024). Multi-semantic decoding of visual perception with graph neural networks. International Journal of Neural Systems, 34(04), 2450016.

PubMed  Google Scholar 

Liang, Y., Bo, K., Meyyappan, S., & Ding, M. (2024). Decoding fMRI data with support vector machines and deep neural networks. Journal of Neuroscience Methods, 401, 110004.

PubMed  CAS  Google Scholar 

Liu, L., Hua, C., Cheng, Z., & Ji, Y. (2021). Intelligent diagnosis method of MRI brain image using parallel self-organizing feature maps neural network. J Med Imaging Health Inform, 11(2), 487–496.

Google Scholar 

Liu, Y., Ge, E., Kang, Z., Qiang, N., Liu, T., & Ge, B. (2024). Spatial-temporal convolutional attention for discovering and characterizing functional brain networks in task fMRI. Neuroimage, 287, 120519.

PubMed  Google Scholar 

Ma, X., Chou, C. A., Sayama, H., & Chaovalitwongse, W. A. (2016). Brain response pattern identification of fMRI data using a particle swarm optimization-based approach. Brain Inform, 3, 181–192.

PubMed  PubMed Central  Google Scholar 

Mishra, R. K., Reddy, G. Y. S., & Pathak, H. (2021). The understanding of deep learning: A comprehensive review, Math Probl Eng, 2021(1), p. 5548884.

Mohr, H., Wolfensteller, U., Frimmel, S., & Ruge, H. (2015). Sparse regularization techniques provide novel insights into outcome integration processes. Neuroimage, 104, 163–176.

PubMed  Google Scholar 

Osher, D. E., Saxe, R. R., Koldewyn, K., Gabrieli, J. D. E., Kanwisher, N., & Saygin, Z. M. (2016). Structural connectivity fingerprints predict cortical selectivity for multiple visual categories across cortex. Cerebral Cortex, 26(4), 1668–1683.

PubMed  Google Scholar 

Qiao, K., et al. (2019). Category decoding of visual stimuli from human brain activity using a bidirectional recurrent neural network to simulate bidirectional information flows in human visual cortices. Front Neurosci, 13, 692.

PubMed  PubMed Central  Google Scholar 

Shi, N., et al. (2024). Estimating and approaching the maximum information rate of noninvasive visual brain-computer interface. Neuroimage, 289, 120548.

PubMed  Google Scholar 

Van Houdt, G., Mosquera, C., & Nápoles, G. (2020). A review on the long short-term memory model. Artificial Intelligence Review, 53(8), 5929–5955.

Google Scholar 

Varkevisser, T., Geuze, E., Van Den Boom, M. A., Kouwer, K., Van Honk, J., & Van Lutterveld, R. (2023). Pattern classification based on the amygdala does not predict an individual’s response to emotional stimuli. Human Brain Mapping, 44(12), 4452–4466.

PubMed  PubMed Central  Google Scholar 

Vega, C. F., Quevedo, J., Escandón, E., Kiani, M., Ding, W., & Andreu-Perez, J. (2022). Fuzzy Temporal convolutional neural networks in P300-based Brain–computer interface for smart home interaction. Appl Soft Comput, 117, 108359.

Google Scholar 

Walz, J. M., Goldman, R. I., Carapezza, M., Muraskin, J., Brown, T. R., & Sajda, P. (2013). Simultaneous EEG-fMRI reveals Temporal evolution of coupling between supramodal cortical attention networks and the brainstem. Journal of Neuroscience, 33(49), 19212–19222.

PubMed  CAS  Google Scholar 

Wang, C., et al. (2020a). When’and ‘what’did you see? A novel fMRI-based visual decoding framework. Journal of Neural Engineering, 17(5), 056013.

PubMed  Google Scholar 

Wang, X., et al. (2020b). Decoding and mapping task States of the human brain via deep learning. Human Brain Mapping, 41(6), 1505–1519.

PubMed  CAS  Google Scholar 

Wang, X., Jin, Y., Schmitt, S., & Olhofer, M. (2023). Recent advances in Bayesian optimization, ACM Comput Surv, 55(13s), pp. 1–36.

Warren, S. L., & Moustafa, A. A. (2023). Functional magnetic resonance imaging, deep learning, and Alzheimer’s disease: A systematic review. Journal of Neuroimaging, 33(1), 5–18.

PubMed