Our survey connects language tasks, neural measurements and decoding methods to evaluation and practical use. It reveals complementary routes toward richer expression and the evidence needed to compare and connect them.
DEFT reorganizes pretrained EEG representations into task-oriented content and subject-consistent style before classification. Across six subject-disjoint EEG datasets and four foundation-model backbones, it improves mean Balanced Accuracy by 0.0460 over matched vanilla fine-tuning.
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Our survey connects language tasks, neural measurements and decoding methods to evaluation and practical use. It reveals complementary routes toward richer expression and the evidence needed to compare and connect them.
DEFT reorganizes pretrained EEG representations into task-oriented content and subject-consistent style before classification. Across six subject-disjoint EEG datasets and four foundation-model backbones, it improves mean Balanced Accuracy by 0.0460 over matched vanilla fine-tuning.
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