A new article led by our PhD student Philemon Roussel, in collaboration with many partners, on acoustic contamination in neural data collected during speech production or perception has been published in the Journal of Neural Engineering. You can find it by following this link:
A toolbox to evaluate acoustic contamination in data obtained with neural recording system can be found in the following link:
Abstract:
To achieve proofs of concept of speech brain-computer interfaces, 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. 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 data sets howed spectrotemporal features strongly suggesting a contamination of electrophysiological recordings by sound. This study investigated the presence of acoustic contamination and its possible source. We developed analysis methods and a statistical criterion to objectively assess the presence or absence of contamination, which we used to screen several datasets from five centers worldwide. We make available a toolbox implementing the proposed approach to assess the extent of contamination in an electrophysiological recording.
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