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

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 ...

This study highlights the potential for using deep learning methods on longitudinal health data from both primary and ...

As part of their regulatory obligations, TMC subsidiaries NORI and TOML have submitted extensive datasets to the International Seabed Authority’s DeepData, covering a decade of exploration in the ...

With the rapid expansion of the new energy vehicle (NEV) market, charging and battery swapping have emerged as the two ...