This study presents valuable findings by reanalyzing previously published MEG and ECoG datasets to challenge the predictive nature of pre-onset neural encoding effects. The evidence supporting the ...

Modality-agnostic decoders leverage modality-invariant representations in human subjects' brain activity to predict stimuli irrespective of their modality (image, text, mental imagery).

This approach combined: (1) supervised machine learning for text classification, (2) comparative topic modeling with both theory-driven and data-driven Latent Dirichlet Allocation (LDA) to identify ...

The Quantum Encoding Atlas is the definitive open-source resource for understanding, comparing, and selecting quantum data encodings for machine learning applications.

Right or (as it turns out) wrong, I wasn’t a sprinkle-your-first-name-into-conversation kind of guy. I’m not hugely self-assured, and saying the other person’s name felt forced. Or awkward. Or ...

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Summary: A new brain decoding method called mind captioning can generate accurate text descriptions of what a person is seeing or recalling—without relying on the brain’s language system. Instead, it ...

The package contains a mixture of classic decoding methods and modern machine learning methods. For regression, we currently include: Wiener Filter, Wiener Cascade, Kalman Filter, Naive Bayes, Support ...