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Analyzing and reconstructing visual stimuli from brain signals effectively advances the understanding of human visual system.
Identifying natural images from human brain activity
Kay, K. N.; Naselaris, T.; Prenger, R. J.; and Gallant, J. L. 2008 · 2008
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Correct block-design experiments mitigate temporal correlation bias in EEG classification
Palazzo, S.; Spampinato, C.; Schmidt, J.; Kavasidis, I.; Giordano, D.; and Shah, M. 2020b · 2012
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Methods of EEG signal features extraction using linear analysis in frequency and time-frequency domains
Al-Fahoum, A. S.; and Al-Fraihat, A. A. 2014 · 2014
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Generative adversarial nets
Goodfellow, I.; Pouget-Abadie, J.; Mirza, M.; Xu, B.; Warde-Farley, D.; Ozair, S.; Courville, A.; and Bengio, Y. 2014 · 2014
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Deep neural networks reveal a gradient in the complexity of neural representations across the ventral stream
Güçlü, U.; and van Gerven, M. A. 2015 · 2015
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Imagenet large scale visual recognition challenge
Russakovsky, O.; Deng, J.; Su, H.; Krause, J.; Satheesh, S.; Ma, S.; Huang, Z.; Karpathy, A.; Khosla, A.; Bernstein, M.; et al. 2015 · 2015
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Improved techniques for training gans
Salimans, T.; Goodfellow, I.; Zaremba, W.; Cheung, V.; Radford, A.; and Chen, X. 2016 · 2016
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Brain2image: Converting brain signals into images
Kavasidis, I.; Palazzo, S.; Spampinato, C.; Giordano, D.; and Shah, M. 2017 · 2017
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Deep learning human mind for automated visual classification
Spampinato, C.; Palazzo, S.; Kavasidis, I.; Giordano, D.; Souly, N.; and Shah, M. 2017 · 2017
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Neural discrete representation learning
Van Den Oord, A.; Vinyals, O.; et al. 2017 · 2017
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MOABB: trustworthy algorithm benchmarking for BCIs
Jayaram, V.; and Barachant, A. 2018 · 2018
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Artifacts and noise removal for electroencephalogram (EEG): A literature review
Lai, C. Q.; Ibrahim, H.; Abdullah, M. Z.; Abdullah, J. M.; Suandi, S. A.; and Azman, A. 2018 · 2018
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Thoughtviz: Visualizing human thoughts using generative adversarial network
Tirupattur, P.; Rawat, Y. S.; Spampinato, C.; and Shah, M. 2018 · 2018
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Neural encoding and decoding with deep learning for dynamic natural vision
Wen, H.; Shi, J.; Zhang, Y.; Lu, K.-H.; Cao, J.; and Liu, Z. 2018 · 2018
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From voxels to pixels and back: Self-supervision in natural-image reconstruction from fMRI
Beliy, R.; Gaziv, G.; Hoogi, A.; Strappini, F.; Golan, T.; and Irani, M. 2019 · 2019
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Why is there so much more research on vision than on any other sensory modality?
Hutmacher, F. 2019 · 2019
Cited alongside, same era.
Deep image reconstruction from human brain activity
Shen, G.; Horikawa, T.; Majima, K.; and Kamitani, Y. 2019 · 2019
Cited alongside, same era.
An image is worth 16x16 words: Transformers for image recognition at scale
Dosovitskiy, A.; Beyer, L.; Kolesnikov, A.; Weissenborn, D.; Zhai, X.; Unterthiner, T.; Dehghani, M.; Minderer, M.; Heigold, G.; Gelly, S.; et al. 2020 · 2020
Cited alongside, same era.
Reconstructing perceptive images from brain activity by shape-semantic gan
Fang, T.; Qi, Y.; and Pan, G. 2020 · 2020
Cited alongside, same era.
Brain-media: A dual conditioned and lateralization supported gan (dcls-gan) towards visualization of image-evoked brain activities
Fares, A.; Zhong, S.-h.; and Jiang, J. 2020 · 2020
Cited alongside, same era.
High-resolution image synthesis with latent diffusion models
Rombach, R.; Blattmann, A.; Lorenz, D.; Esser, P.; and Ommer, B. 2022 · 2022
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Improving classification and reconstruction of imagined images from EEG signals
Shimizu, H.; and Srinivasan, R. 2022 · 2022
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Towards language-free training for text-to-image generation
Zhou, Y.; Zhang, R.; Chen, C.; Li, C.; Tensmeyer, C.; Yu, T.; Gu, J.; Xu, J.; and Sun, T. 2022 · 2022
Later among the works it cites.
DreamDiffusion: Generating High-Quality Images from Brain EEG Signals
Bai, Y.; Wang, X.; Cao, Y.; Ge, Y.; Yuan, C.; and Shan, Y. 2023 · 2023
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Seeing beyond the brain: Conditional diffusion model with sparse masked modeling for vision decoding
Chen, Z.; Qing, J.; Xiang, T.; Yue, W. L.; and Zhou, J. H. 2023 · 2023
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A comparative analysis of signal processing and classification methods for different applications based on EEG signals
Khosla, A.; Khandnor, P.; and Chand, T. 2020 · 2020
Cited alongside, same era.
Hybrid deep learning (hDL)-based brain-computer interface (BCI) systems: a systematic review
Alzahab, N. A.; Apollonio, L.; Di Iorio, A.; Alshalak, M.; Iarlori, S.; Ferracuti, F.; Monteriù, A.; and Porcaro, C. 2021 · 2021
Cited alongside, same era.
The role of the superior parietal lobule in lexical processing of sign language: Insights from fMRI and TMS
Banaszkiewicz, A.; Matuszewski, J.; Szczepanik, M.; Kossowski, B.; Mostowski, P.; Rutkowski, P.; Śliwińska, M.; Jednoróg, K.; Emmorey, K.; Marchewka, A.; et al. 2021 · 2021
Cited alongside, same era.
Electroencephalography-based auditory attention decoding: Toward neurosteered hearing devices
Geirnaert, S.; Vandecappelle, S.; Alickovic, E.; de Cheveigne, A.; Lalor, E.; Meyer, B. T.; Miran, S.; Francart, T.; and Bertrand, A. 2021 · 2021
Cited alongside, same era.
Learning transferable visual models from natural language supervision
Radford, A.; Kim, J. W.; Hallacy, C.; Ramesh, A.; Goh, G.; Agarwal, S.; Sastry, G.; Askell, A.; Mishkin, P.; Clark, J.; et al. 2021 · 2021
Cited alongside, same era.
fmri brain decoding and its applications in brain–computer interface: A survey
Du, B.; Cheng, X.; Duan, Y.; and Ning, H. 2022 · 2022
Cited alongside, same era.
Masked autoencoders are scalable vision learners
He, K.; Chen, X.; Xie, S.; Li, Y.; Dollár, P.; and Girshick, R. 2022 · 2022
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Cheng, M.; Liu, Q.; Liu, Z.; Zhang, H.; Zhang, R.; and Chen, E. 2023 · 2023
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Towards voice reconstruction from EEG during imagined speech
Lee, Y.-E.; Lee, S.-H.; Kim, S.-H.; and Lee, S.-W. 2023 · 2023
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Lu, Y.; Du, C.; Wang, D.; and He, H. 2023 · 2023
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Natural scene reconstruction from fMRI signals using generative latent diffusion
Ozcelik, F.; and VanRullen, R. 2023 · 2023
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EEG2IMAGE: Image reconstruction from EEG brain signals
Singh, P.; Pandey, P.; Miyapuram, K.; and Raman, S. 2023 · 2023
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An investigation of olfactory-enhanced video on eeg-based emotion recognition
Wu, M.; Teng, W.; Fan, C.; Pei, S.; Li, P.; and Lv, Z. 2023 · 2023
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Decoding EEG signals of visual brain representations with a CLIP based knowledge distillation
Ferrante, M.; Boccato, T.; Bargione, S.; and Toschi, N. 2024 · 2024
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Learning Robust Deep Visual Representations from EEG Brain Recordings
Singh, P.; Dalal, D.; Vashishtha, G.; Miyapuram, K.; and Raman, S. 2024 · 2024
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MB2C: Multimodal Bidirectional Cycle Consistency for Learning Robust Visual Neural Representations
Wei, Y.; Cao, L.; Li, H.; and Dong, Y. 2024 · 2024
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