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[10] Li Q, Wu X, Liu TM. Differentiable neural architecture search for optimal spatial/temporal brain function network decomposition.
Medical Image Analysis, 2021, 69: 101974.
[11] Ellermann J, Garwood M, Hendrich K, et al. Functional imaging of the brain by nuclear magnetic resonance. In: Proc. of the NMR
in Physiology and Biomedicine. 1994. 137−150.
[12] Schcter DL. EEG theta waves and psychological phenomena: a review and analysis. Biological Psychology, 1977, 5(1): 47−82.
[13] Madsen PL, Secher NH. Near-infrared oximetry of the brain. Progress in Neurobiology, 1999, 58(6): 541−560.
[14] Brown R, Cheng YC, Haacke EM, et al. Magnetic Resonance Imaging: Physical Principles and Sequence Design. Wiley, 1999.
[15] Le Bihan D. Looking into the functional architecture of the brain with diffusion MRI. Nature Reviews Neuroscience, 2006, 1290:
1−24.
[16] Heinz ER, DuBois P, Osborne D, et al. Dynamic computed tomography study of the brain. Journal of Computer Assisted
Tomography, 1979, 3(5): 641−649.
[17] Huang H, Hu XT, Zhao Y, et al. Modeling task fmri data via deep convolutional autoencoder. IEEE Trans. on Medical Imaging,
2018, 37(7): 1551−1561.
[18] Zhang W, Zhao SJ, Hu XT, et al. Hierarchical organization of functional brain networks revealed by hybrid spatiotemporal deep
learning. Brain Connectivity, 2020, 10(2): 72−82.
[19] Li C, Zhang ZZ, Song RC, et al. EEG-based emotion recognition via transformer neural architecture search. IEEE Trans. on
Industrial Informatics, 2023, 19(4): 6016−6025.
[20] Nguyen KP, Fatt CC, Treacher A, et al. Predicting response to the antidepressant bupropion using pretreatment fmri. In: Proc. of
the Int’l Workshop on Machine Learning in Medical Imaging-PRedictive Intelligence in Medicine (MICCAI-PRIME), Vol.11843.
2019. 53−62.
[21] Dai HX, Li Q, Zhao L, et al. Graph representation neural architecture search for optimal spatial/temporal functional brain network
decomposition. In: Proc. of the Int’l Workshop on Machine Learning in Medical Imaging-Machine Learning in Medical Imaging
(MICCAI-MLMI). 2022. 279−287.
[22] Li Q, Zhang W, Zhao L, et al. Evolutional neural architecture search for optimization of spatiotemporal brain network
decomposition. IEEE Trans. on Bio-medical Engineering, 2022, 69(2): 624−634.
[23] Liu HX, Simonyan K, Yang YM. DARTS: Differentiable architecture search. In: Proc. of the Int’l Conf. on Learning
Representations (ICLR). 2019. 1−12.
[24] Xiao AQ, Shen BL, Shi XJ, et al. Intraoperative glioma grading using neural architecture search and multi-modal imaging. IEEE
Trans. on Medical Imaging, 2022, 41(10): 2570−2581.
[25] Rapaport E, Shriki O, Puzis R. EEGNAS: Neural architecture search for electroencephalography data analysis and decoding. In:
Proc. of the Int’l Joint Conf. on Artificial Intelligence-human Brain and Artificial Intelligence (IJCAI-HBAI). 2019. 3−20.
[26] Dong HY, Chen D, Zhang L, et al. Subject sensitive eeg discrimination with fast reconstructable cnn driven by reinforcement
learning: A case study of asd evaluation. Neurocomputing, 2021, 449: 136−145.
[27] Du YP, Liu J, Wang X, et al. SSVEP-based emotion recognition for iot via multiobjective neural architecture search. IEEE Internet
of Things Journal, 2022, 9(21): 21432−21443.
[28] Duan YQ, Wang Z, Li Y, et al. Cross task neural architecture search for EEG signal classifications. Neurocomputing, 2022, 545:
126260.
[29] Kong GW, Li C, Peng H, et al. EEG-based sleep stage classification via neural architecture search. IEEE Trans. on Neural Systems
and Rehabilitation Engineering, 2023, 31: 1075−1085.
[30] Wang H, Zhu XS, Chen PY, et al. A gradient-based automatic optimization cnn framework for EEG state recognition. Journal of
Neural Engineering, 2022, 19(1): 016009.
[31] Zhang W, Zhao L, Li Q, et al. Identify hierarchical structures from task-based FMRI data via hybrid spatiotemporal neural
architecture search net. In: Proc. of the Medical Image Computing and Computer Assisted Intervention (MICCAI). 2019. 745−753.
[32] Qiang N, Dong QL, Zhang W, et al. Modeling task-based fmri data via deep belief network with neural architecture search. In: Proc.
of the Computerized Medical Imaging and Graphics. 2020. 101747.
[33] Ren YD, Xu SH, Tao ZY, et al. Hierarchical spatio-temporal modeling of naturalistic functional magnetic resonance imaging
signals via two-stage deep belief network with neural architecture search. Frontiers in Neuroscience, 2021, 15: 794955.
[34] Duan FX, Cao CH, Gao XP. SA-NAS-BFNR: Spatiotemporal attention neural architecture search for task-based brain functional
network representation. In: Proc. of the Int’l Conf. on Multimedia Retrieval (ICMR). 2022. 661−667.