Objective. A current challenge of neurotechnologies is to develop speech brain-computer interfaces aiming at restoring communication in people unable to speak. To achieve a proof of concept of such system, neural activity of patients implanted for clinical reasons can be recorded while they speak. Using such simultaneously recorded audio and neural data, decoders can be built to predict speech features using features extracted from brain signals. A typical neural feature is the spectral power of field potentials in the high-gamma frequency band, which happens to overlap the frequency range of speech acoustic signals, especially the fundamental frequency of the voice. Here, we analyzed human electrocorticographic and intracortical recordings during speech production and perception as well as a rat microelectrocorticographic recording during sound perception. We observed that several datasets, recorded with different recording setups, contained spectrotemporal features highly correlated with those of the sound produced by or delivered to the participants, especially within the high-gamma band and above, strongly suggesting a contamination of electrophysiological recordings by the sound signal. This study investigated the presence of acoustic contamination and its possible source. Approach. We developed analysis methods and a statistical criterion to objectively assess the presence or absence of contamination-specific correlations, which we used to screen several datasets from five centers worldwide. Main results. Not all but several datasets, recorded in a variety of conditions, showed significant evidence of acoustic contamination. Three out of five centers were concerned by the phenomenon. In a recording showing high contamination, the use of high-gamma band features dramatically facilitated the performance of linear decoding of acoustic speech features, while such improvement was very limited for another recording showing no significant contamination. Further analysis and in vitro replication suggest that the contamination is caused by the mechanical action of the sound waves onto the cables and connectors along the recording chain, transforming sound vibrations into an undesired electrical noise affecting the biopotential measurements. Significance. Although this study does not per se question the presence of speech-relevant physiological information in the high-gamma range and above (multiunit activity), it alerts on the fact that acoustic contamination of neural signals should be proofed and eliminated before investigating the cortical dynamics of these processes. To this end, we make available a toolbox implementing the proposed statistical approach to quickly assess the extent of contamination in an electrophysiological recording (https://doi.org/10.5281/zenodo.3929296).

What is the information captured by neural network models of language? We address this question in the case of character-level recurrent neural language models. These models do not have explicit word representations; do they acquire implicit ones? We assess the lexical capacity of a network using the lexical decision task common in psycholinguistics: the system is required to decide whether or not a string of characters forms a word. We explore how accuracy on this task is affected by the architecture of the network, focusing on cell type (LSTM vs. SRN), depth and width. We also compare these architectural properties to a simple count of the parameters of the network. The overall number of parameters in the network turns out to be the most important predictor of accuracy; in particular, there is little evidence that deeper networks are beneficial for this task.