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Found 2 papers, 1 papers with code
Correct block-design experiments mitigate temporal correlation bias in EEG classification
We argue that the reason why Li et al. [1] observe such high correlation in EEG data is their unconventional experimental design and settings that violate the basic cognitive neuroscience design recommendations, first and foremost the one of limiting the experiments' duration, as instead done in [2].
Decoding Brain Representations by Multimodal Learning of Neural Activity and Visual Features
After verifying that visual information can be extracted from EEG data, we introduce a multimodal approach that uses deep image and EEG encoders, trained in a siamese configuration, for learning a joint manifold that maximizes a compatibility measure between visual features and brain representations.
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