Intracranial Error Detection via Deep Learning

TL;DR

This study demonstrates that deep learning, specifically CNNs, significantly improves intracranial EEG error detection accuracy and can predict errors up to 200 ms before they occur, revealing detailed brain error processing.

Contribution

First application of CNNs to intracranial EEG error detection, showing superior performance and detailed spatio-temporal error mapping compared to traditional methods.

Findings

CNNs outperform linear discriminant analysis in error classification

Errors can be decoded up to 200 ms before button press

Deeper networks yield higher accuracy in all-channel decoding

Abstract

Deep learning techniques have revolutionized the field of machine learning and were recently successfully applied to various classification problems in noninvasive electroencephalography (EEG). However, these methods were so far only rarely evaluated for use in intracranial EEG. We employed convolutional neural networks (CNNs) to classify and characterize the error-related brain response as measured in 24 intracranial EEG recordings. Decoding accuracies of CNNs were significantly higher than those of a regularized linear discriminant analysis. Using time-resolved deep decoding, it was possible to classify errors in various regions in the human brain, and further to decode errors over 200 ms before the actual erroneous button press, e.g., in the precentral gyrus. Moreover, deeper networks performed better than shallower networks in distinguishing correct from error trials in all-channel decoding. In single recordings, up to 100 % decoding accuracy was achieved. Visualization of the networks' learned features indicated that multivariate decoding on an ensemble of channels yields related, albeit non-redundant information compared to single-channel decoding. In summary, here we show the usefulness of deep learning for both intracranial error decoding and mapping of the spatio-temporal structure of the human error processing network.