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Fully automated decoding of human activities and intentions from directneural recordings is a tantalizing challenge in brain-computer interfacing.Most ongoing efforts have focused on training decoders on specific, stereotypedtasks in laboratory settings. Implementing brain-computer interfaces (BCIs) innatural settings requires adaptive strategies and scalable algorithms thatrequire minimal supervision. Here we propose an unsupervised approach todecoding neural states from human brain recordings acquired in a naturalisticcontext. We demonstrate our approach on continuous long-termelectrocorticographic (ECoG) data recorded over many days from the brainsurface of subjects in a hospital room, with simultaneous audio and videorecordings. We first discovered clusters in high-dimensional ECoG recordingsand then annotated coherent clusters using speech and movement labels extractedautomatically from audio and video recordings. To our knowledge, thisrepresents the first time techniques from computer vision and speech processinghave been used for natural ECoG decoding. Our results show that ourunsupervised approach can discover distinct behaviors from ECoG data, includingmoving, speaking and resting. We verify the accuracy of our approach bycomparing to manual annotations. Projecting the discovered cluster centers backonto the brain, this technique opens the door to automated functional brainmapping in natural settings.

Unsupervised decoding of long-term, naturalistic human neural recordings with automated video and audio annotations