DM-RE2I: A framework based on diffusion model for the reconstruction from EEG to image

[1]  G. Dai,et al.  DCAE: A dual conditional autoencoder framework for the reconstruction from EEG into image , 2023, Biomed. Signal Process. Control..

[2]  Ranjeet Ranjan Jha,et al.  NeuroGAN: image reconstruction from EEG signals via an attention-based GAN , 2022, Neural Computing and Applications.

[3]  R. Srinivasan,et al.  Improving classification and reconstruction of imagined images from EEG signals , 2022, bioRxiv.

[4]  W. Kong,et al.  A Bi-LSTM Based Network with Attention Mechanism for EEG Visual Classification , 2021, 2021 IEEE International Conference on Unmanned Systems (ICUS).

[5]  Deepti R. Bathula,et al.  EEG-ConvTransformer for Single-Trial EEG based Visual Stimuli Classification , 2021, ArXiv.

[6]  Prafulla Dhariwal,et al.  Diffusion Models Beat GANs on Image Synthesis , 2021, NeurIPS.

[7]  Prafulla Dhariwal,et al.  Improved Denoising Diffusion Probabilistic Models , 2021, ICML.

[8]  Ahmed Fares,et al.  Brain-media: A Dual Conditioned and Lateralization Supported GAN (DCLS-GAN) towards Visualization of Image-evoked Brain Activities , 2020, ACM Multimedia.

[9]  Pieter Abbeel,et al.  Denoising Diffusion Probabilistic Models , 2020, NeurIPS.

[10]  Mingyang Li,et al.  Decoding human brain activity with deep learning , 2020, Biomed. Signal Process. Control..

[11]  Jianmin Jiang,et al.  An Attentional-LSTM for Improved Classification of Brain Activities Evoked by Images , 2019, ACM Multimedia.

[12]  Wanzeng Kong,et al.  Visualizing Emotional States: A Method Based on Human Brain Activity , 2019, HBAI@IJCAI.

[13]  Ravi Tandon,et al.  ON THE TRADEOFF BETWEEN MODE COLLAPSE AND SAMPLE QUALITY IN GENERATIVE ADVERSARIAL NETWORKS , 2018, 2018 IEEE Global Conference on Signal and Information Processing (GlobalSIP).

[14]  Mubarak Shah,et al.  Decoding Brain Representations by Multimodal Learning of Neural Activity and Visual Features , 2018, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[15]  Mubarak Shah,et al.  ThoughtViz: Visualizing Human Thoughts Using Generative Adversarial Network , 2018, ACM Multimedia.

[16]  Han Zhang,et al.  Self-Attention Generative Adversarial Networks , 2018, ICML.

[17]  Mert R. Sabuncu,et al.  Generalized Cross Entropy Loss for Training Deep Neural Networks with Noisy Labels , 2018, NeurIPS.

[18]  Lukasz Kaiser,et al.  Attention is All you Need , 2017, NIPS.

[19]  Jin Wang,et al.  Semantic Relation Classification by Bi-directional LSTM Architecture , 2017 .

[20]  Brent Lance,et al.  EEGNet: a compact convolutional neural network for EEG-based brain–computer interfaces , 2016, Journal of neural engineering.

[21]  Wojciech Zaremba,et al.  Improved Techniques for Training GANs , 2016, NIPS.

[22]  이창기,et al.  Convolutional Neural Network를 이용한 한국어 영화평 감성 분석 , 2016 .

[23]  Thomas Brox,et al.  U-Net: Convolutional Networks for Biomedical Image Segmentation , 2015, MICCAI.

[24]  Surya Ganguli,et al.  Deep Unsupervised Learning using Nonequilibrium Thermodynamics , 2015, ICML.

[25]  Sergey Ioffe,et al.  Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift , 2015, ICML.

[26]  Yuxin Peng,et al.  Error-Driven Incremental Learning in Deep Convolutional Neural Network for Large-Scale Image Classification , 2014, ACM Multimedia.

[27]  Aaron C. Courville,et al.  Generative Adversarial Nets , 2014, NIPS.

[28]  Yoshua Bengio,et al.  Deep Sparse Rectifier Neural Networks , 2011, AISTATS.

[29]  Fei-Fei Li,et al.  ImageNet: A large-scale hierarchical image database , 2009, 2009 IEEE Conference on Computer Vision and Pattern Recognition.

[30]  Desney S. Tan,et al.  Combining brain computer interfaces with vision for object categorization , 2008, 2008 IEEE Conference on Computer Vision and Pattern Recognition.

[31]  J. Craig Henry,et al.  Creating Coordination in the Cerebellum: Progress in Brain Research, Volume 148 , 2006, Neurology.

[32]  William Stafford Noble,et al.  Support vector machine , 2013 .

[33]  C. Willmott,et al.  Advantages of the mean absolute error (MAE) over the root mean square error (RMSE) in assessing average model performance , 2005 .

[34]  Christopher Meek,et al.  Adversarial learning , 2005, KDD '05.

[35]  Margaret S. Pepe,et al.  Receiver Operating Characteristic Methodology , 2000 .

[36]  S. Hochreiter,et al.  Long Short-Term Memory , 1997, Neural Computation.

[37]  Kotaro Hirasawa,et al.  Forward propagation universal learning network , 1996, Proceedings of International Conference on Neural Networks (ICNN'96).

[38]  Geoffrey E. Hinton,et al.  Learning representations by back-propagating errors , 1986, Nature.

[39]  Sheng-hua Zhong,et al.  A Brain-Media Deep Framework Towards Seeing Imaginations Inside Brains , 2021, IEEE Transactions on Multimedia.

[40]  Asuman Ozdaglar,et al.  Do GANs always have Nash equilibria? , 2020, ICML.

[41]  M. Krasnyanskiy,et al.  Quality Assessment Method for GAN Based on Modified Metrics Inception Score and Fréchet Inception Distance , 2020 .

[42]  Yitong Li,et al.  Targeting EEG/LFP Synchrony with Neural Nets , 2017, NIPS.

[43]  Robert Susmaga,et al.  Confusion Matrix Visualization , 2004, Intelligent Information Systems.

[44]  Raúl Rojas,et al.  The Backpropagation Algorithm , 1996 .