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Emo-Jev, a training-free framework with two complementary implementations that decomposes classification into task-specific atomic judgments and composes their probabilities into a final prediction, is introduced.
This work proposes 'One Model for All', a universal pre-training framework for EEG analysis across disparate datasets, and paves the way for more universal, scalable, and effective pre-trained models for diverse EEG analysis tasks.
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