Cross-paradigm pretraining of convolutional networks improves intracranial EEG decoding

TL;DR

This study demonstrates that transfer learning with deep convolutional neural networks enhances intracranial EEG decoding across different tasks and paradigms, especially with limited data, by transferring error-specific brain signal patterns.

Contribution

It shows the effectiveness of cross-paradigm transfer learning in intracranial EEG classification, highlighting transfer of error-specific information and improved performance with minimal data.

Findings

Transfer learning improves decoding accuracy in intracranial EEG.

Error-specific brain signal patterns can be transferred across tasks.

Decoding accuracy increases significantly with pre-training, especially with limited data.

Abstract

When it comes to the classification of brain signals in real-life applications, the training and the prediction data are often described by different distributions. Furthermore, diverse data sets, e.g., recorded from various subjects or tasks, can even exhibit distinct feature spaces. The fact that data that have to be classified are often only available in small amounts reinforces the need for techniques to generalize learned information, as performances of brain-computer interfaces (BCIs) are enhanced by increasing quantity of available data. In this paper, we apply transfer learning to a framework based on deep convolutional neural networks (deep ConvNets) to prove the transferability of learned patterns in error-related brain signals across different tasks. The experiments described in this paper demonstrate the usefulness of transfer learning, especially improving performances when only little data can be used to distinguish between erroneous and correct realization of a task. This effect could be delimited from a transfer of merely general brain signal characteristics, underlining the transfer of error-specific information. Furthermore, we could extract similar patterns in time-frequency analyses in identical channels, leading to selective high signal correlations between the two different paradigms. Classification on the intracranial data yields in median accuracies up to $(81.50 \pm 9.49)\,\%$. Decoding on only $10\%$ of the data without pre-training reaches performances of $(54.76 \pm 3.56)\,\%$, compared to $(64.95 \pm 0.79)\,\%$ with pre-training.