Dataset of Speech Production in intracranial.Electroencephalography

Abstract

Speech production is an intricate process involving a large number of muscles and cognitive processes. The neural processes underlying speech production are not completely understood. As speech is a uniquely human ability, it can not be investigated in animal models. High-fidelity human data can only be obtained in clinical settings and is therefore not easily available to all researchers. Here, we provide a dataset of 10 participants reading out individual words while we measured intracranial EEG from a total of 1103 electrodes. The data, with its high temporal resolution and coverage of a large variety of cortical and sub-cortical brain regions, can help in understanding the speech production process better. Simultaneously, the data can be used to test speech decoding and synthesis approaches from neural data to develop speech Brain-Computer Interfaces and speech neuroprostheses.

Fig. 1

Intracranial EEG and acoustic data are recorded simultaneously while participants read Dutch words shown on a laptop screen. Traces on the right of the figure represent 30 seconds of iEEG, audio and stimulus data. The colors in the iEEG traces represent different electrode shafts.

Fig. 2

Electrode locations of each participant in the surface reconstruction of their native anatomical MRI. Each red sphere represents an implanted electrode channel.

Fig. 3

Number of electrode contacts in cortical and subcortical areas across all participants. Colors indicate participants. Lengths of the bars show the number of electrodes in the specified region. Note the deviant x-axis for the white matter and unknown regions.

Fig. 4

Results for the spectral reconstruction. (a) Mean correlation coefficients for each participant across all spectral bins and folds. Reconstruction of the spectrogram is possible for all 10 participants. Whiskers indicate standard deviations. Results of individual folds are illustrated by points. (b) Mean correlation coefficients for each spectral bin. Correlations are stable across all spectral bins. Shaded areas show standard errors.

Fig. 5

Spectrograms (a) and waveforms (b) of the original (top) and reconstructed (bottom) audio. The example contains five individual words from sub-06. While the linear regression approach captures speech and silent intervals very accurately, the finer spectral dynamics within speech are lost.