The semantic matching approach in the SEE model aims to align EEG features with text representations while mitigating the impact of false negatives. However, potential limitations include the reliance on the quality of the pre-trained language model for semantic understanding and the challenge of accurately identifying false negatives in the absence of explicit labels. False negatives can introduce noise into the training process, leading to misalignment between EEG signals and their corresponding text representations.
To improve robustness, the model could incorporate a more sophisticated noise-robust training strategy, such as adversarial training, where the model learns to distinguish between true and false pairs more effectively. Additionally, implementing a dynamic thresholding mechanism that adapts based on the distribution of semantic similarities could help in better identifying and mitigating the influence of false negatives. Furthermore, integrating external knowledge sources or contextual embeddings could enhance the model's ability to discern semantic relationships, thereby improving the overall alignment process.