  Recent work on intracranial brain-machine interfaces has demonstrated that
spoken speech can be decoded with high accuracy, essentially by treating the
problem as an instance of supervised learning and training deep neural networks
to map from neural activity to text. However, such networks pay for their
expressiveness with very large numbers of labeled data, a requirement that is
particularly burdensome for invasive neural recordings acquired from human
patients. On the other hand, these patients typically produce speech outside of
the experimental blocks used for training decoders. Making use of such data,
and data from other patients, to improve decoding would ease the burden of data
collection -- especially onerous for dys- and anarthric patients. Here we
demonstrate that this is possible, by reengineering wav2vec -- a simple,
self-supervised, fully convolutional model that learns latent representations
of audio using a noise-contrastive loss -- for electrocorticographic (ECoG)
data. We train this model on unlabelled ECoG recordings, and subsequently use
it to transform ECoG from labeled speech sessions into wav2vec's representation
space, before finally training a supervised encoder-decoder to map these
representations to text. We experiment with various numbers of labeled blocks;
for almost all choices, the new representations yield superior decoding
performance to the original ECoG data, and in no cases do they yield worse.
Performance can also be improved in some cases by pretraining wav2vec on
another patient's data. In the best cases, wav2vec's representations decrease
word error rates over the original data by upwards of 50%.
