This paper presents EveryBrain, a method to generate electroencephalographic (EEG) signals of visual stimuli using images. Given that individuals exhibit distinct EEG responses to the same visual stimulus, EveryBrain is capable of capturing these individual characteristics during signal generation. The framework operates in two stages. By leveraging the temporal properties of EEG signals and the spatial features of images, EveryBrain presents a self-supervised framework that simultaneously reconstruct EEG signals and perform contrastive learning between image and EEG features. Furthermore, through additional training focused on individual EEG differences, Stage2 injects an ID number (representing a specific person) into image features via a cross-modal projector. The resulting personalized EEG latent codes, supervised by the Stage1 encoder, are then decoded into vivid, individualized EEG responses. Experiments validate the accuracy of EveryBrain in generating EEG signals for various individuals in response to visual stimuli. Overall, the proposed method tackles challenges in EEG generation from images, such as cross-modal alignment, individual variability, and waveform stability, yielding promising results. Additionally, the novel approach of of joint learning between images and EEG demonstrates positive effects on decoding visual neural representations. Both quantitative and qualitative evaluations demonstrate the effectiveness of methods, marking a significant step toward portable and cost-effective "image-to-thought".

Multi-view learning aims to fully exploit the information from multiple sources to obtain better performance than using a single view. However, real-world data often contains a lot of noise, which can have a large impact on multi-view learning. Therefore, it is necessary to identify noise contained in multi-view data to achieve robust and trusted classification. In this paper, we propose a robust trusted multi-view classification framework, RTMC. Our framework uses multi-view affinity and repellence encoding to learn effective latent encodings of multi-view data. We also propose a trust-aware discriminator to estimate trust scores by identifying noise contained in the data. We adopt prototype queues, which store latent encodings of different classes, to accurately identify the noise. Finally, trusted multi-view classification is proposed to jointly predict the trust scores of classification and achieve robust classification results through a trusted fusion strategy. RTMC is validated on six challenging multi-view datasets and the experimental results demonstrate the robustness and effectiveness of our method.

Multi-view learning aims to leverage data acquired from multiple sources to achieve better performance compared to using a single view. However, the performance of multi-view learning can be negatively impacted by noisy or corrupted views in certain real-world situations. As a result, it is crucial to assess the confidence of predictions and obtain reliable learning outcomes. In this paper, we introduce CALM, an enhanced encoding and confidence evaluation framework for trustworthy multi-view classification. Our method comprises enhanced multi-view encoding, multi-view confidence-aware fusion, and multi-view classification regularization, enabling the simultaneous evaluation of prediction confidence and the yielding trustworthy classifications. Enhanced multi-view encoding takes advantage of cross-view consistency and class diversity to improve the efficacy of the learned latent representation, facilitating more reliable classification results. Multi-view confidence-aware fusion utilizes a confidence-aware estimator to evaluate the confidence scores of classification outcomes. The final multi-view classification results are then derived through confidence-aware fusion. To achieve reliable and accurate confidence scores, multivariate Gaussian distributions are employed to model the prediction distribution. The advantage of CALM lies in its ability to evaluate the quality of each view, reducing the influence of low-quality views on the multi-view fusion process and ultimately leading to improved classification performance and confidence evaluation. Comprehensive experimental results demonstrate that our method outperforms other trusted multi-view learning methods in terms of effectiveness, reliability, and robustness.