reviews: 
Comments: This paper highlights the method of generating images from EEG signals by CLIP embedding. In the visual representation and construction field, EEG is less frequently used and has better temporal resolution instead of spatial resolution, so it may be useful for future work related to brain-computer interface. The research regarding the scalp-asymmetry effect, time-swap effect, and spatiotemporal semantic map described some important internal mechanisms of visual reconstruction.
Strengths: 1. A simpler model that has state-of-the-art performance. Every stage of the whole model is highly modular. Multimodal extraction with different models. In 3.3 we can see that VDVAE-based reconstructions is useful to exctract the latent space from EEG signals.

2. The method of observing time-swap effect and spatiotemporal semantic map is fascinating, making full use of the characteristics of EEG's high time resolution. From the spatiotemporal semantic map (Figure 9), the trends show some consistent and universal dynamic patterns to help researchers understand the time-dependence reactions of the brain when dealing with these different categories.
Weaknesses: 1. The contribution of this paper to the community appears limited, as it does not provide new insights or innovative research directions. The repackaging of existing work—specifically the separation of CLIP embeddings into text and image components—does not signify substantial progress within the field. 

2. The absence of a detailed model explanations, and mathematical rigor undermines the paper's contribution. Key sections, particularly between lines 130 and 150, are inadequately polished; there is a pressing need for more precise mathematical formulations and clearer textual explanations to enhance comprehension and rigor. And the presentation of the results is unpolished, e.g. fig6, text size should be unified, and in fig.7 texts should be aligned, etc.

3. In line 349, it mentioned the symmetry about the vertical midline, especially the images that have strong diagonal components, but this conclusion is based on a few specific examples (such as columns 1 and 11).

4. In line 354, the analysis mentioned that the "animal nature" of animal images sometimes disappeared under mirror conditions, but it is not clearly stated whether this is consistent with the difference between the hemisphere between the hemisphere. 

5. The authors rely on a straightforward ridge regression model to align EEG embeddings with CLIP(Radford, et al., 2021), raising concerns about whether such a simplistic encoding structure can sufficiently capture the complex spatial and temporal dependencies inherent in EEG data. The reconstruction pipe used in this paper doesn't contain much of a trick, the framework failed to provide images with richer information to do some complex tasks. But it turned out to be much better than previous methods, and I doubt it.

6. The discussion of scalp-asymmetry effects is superficial, lacking the necessary depth and rigor. Furthermore, the placement of key figures, such as Figure 3(d), could be optimized for reader accessibility, as its current location disrupts the flow and readability of the analysis.
Recommendation: 5
Reviewer confidence: 4
Soundness/correctness: 2
Clarity: 2