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Article accepted for publication: IEEE International Conference on Systems, Man, and Cybernetics 2018

We are proud to announce that our articles "Cross-paradigm pretraining of convolutional networks improves intracranial EEG decoding",  "A framework for large-scale evaluation of deep learning for EEG" and "Intracranial Error Detection via Deep Learning" are accepted for publication at the IEEE International Conference on Systems, Man, and Cybernetics 2018.  

Abstracts:

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 disposable 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. Decoding on the intracranial data without pre-transfer reaches median accuracies of (76.94 pm 2.17)% and (81.50 pm 9.49)%, respectively.

Behncke J., Schirrmeister R. T., Völker M., Schulze-Bonhage A., Burgard W., and Ball T., "Cross-paradigm pretraining of convolutional networks improves intracranial EEG decoding". IEEE International Conference on Systems, Man, and Cybernetics 2018 arxiv.org/abs/1806.09532

 

EEG is the most common signal source for noninvasive BCI applications. For such applications, the EEG signal needs to be decoded and translated into appropriate actions. A recently emerging EEG decoding approach is deep learning with Convolutional or Recurrent Neural Networks (CNNs, RNNs) with many different architectures already published. Here we present a novel framework for the large-scale evaluation of different deep-learning architectures on different EEG datasets. This framework comprises (i) a collection of EEG datasets currently comprising 100 examples (recording sessions) from six different classification problems, (ii) a collection of different EEG decoding algorithms, and (iii) a wrapper linking the decoders to the data as well as handling structured documentation of all settings and (hyper-) parameters and statistics, designed to ensure transparency and reproducibility. As an applications example we used our framework by comparing three publicly available CNN architectures: the Braindecode Deep4 ConvNet, Braindecode Shallow ConvNet, and EEGNet. We also show how our framework can be used to study similarities and differences in the performance of different decoding methods across tasks. We argue that the deep learning EEG framework as described here could help to tap the full potential of deep learning for BCI applications.

Heilmeyer F.A., Schirrmeister R.T., Fiederer L.D.J., Völker M., Behncke J., Ball T., "A framework for large-scale evaluation of deep learning for EEG". IEEE International Conference on Systems, Man, and Cybernetics 2018 https://arxiv.org/abs/1806.07741

 

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.

Völker M., Hammer J., Schirrmeister R.T., Behncke J., Fiederer L.D., Schulze-Bonhage A., Marusič P., Burgard W., and Ball T., "Intracranial Error Detection via Deep Learning". IEEE International Conference on Systems, Man, and Cybernetics 2018 https://arxiv.org/abs/1805.01667

 

 

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