Foundation Models for Cross-Domain EEG Analysis Application: A Survey

Therefore, in this pilot study, we try to focus and summarize exclusively on foundation models that have been pre-trained on large-scale non-EEG data and directly applied to EEG analysis tasks. Specifically, we exclude models fine-tuned on EEG datasets to focus exclusively on cross-domain transfer, and proposed a function-driven, modality-oriented taxonomy for involved foundation models. To this end, as concluded in T ABLE I, the existing research advances of the off-the-shelf foundation models applied in EEG analysis are categorized into five domains: native unimodal EEG decoding, EEG-to-text alignment and generation, EEG-to-vision reconstruction and retrieval, EEG-to-audio decoding and generation, and multi-modal EEG fusion. Within each category, we systematically review representative explorations, with the used models, new architecture characteristics, and application scenarios highlighted in the following sections. Our main contributions are: 1) This survey provides the first and latest comprehensive taxonomy of foundation models pre-trained on non-EEG data and applied to EEG analysis, where the progress of unimodal EEG decoding, EEG-to-text, EEG-to-vision, EEG-to-audio, and multi-modal EEG fusion under foundation models is clearly presented. Such the effort clarifies the latest EEG research application, and helps to explicitly express the rich connotation and practical value of the foundation model; 2) It elaborates in detail on the different roles played by the foundation model in the EEG decoding paradigm shift, including the noise-robust representation learning, cross-modal alignment mechanism, and zero-shot generalization strategies, providing a clear technical framework for newcomers and domain experts in the progress of foundation model reshaping the conventional EEG decoding; 3) We discussed the existing challenges and potential future research directions, aiming to provide clearer and more feasible guidance for the development of scalable, interpretable and widely applicable EEG decoding systems. The remainder of this paper is organized as follows: Section II VI describes the taxonomy of how foundation models can be adapted to classified EEG decoding applications, respectively. Section VII highlights current challenges and future directions, and the conclusion are given in Section VIII.