Learning discriminative features from electroencephalography recordings by encoding similarity constraints

This paper introduces a pre-training technique for learning discriminative features from electroencephalography (EEG) recordings using deep neural networks. EEG data are generally only available in small quantities, they are high-dimensional with a poor signal-to-noise ratio, and there is considerable variability between individual subjects and recording sessions. Similarity-constraint encoders as introduced in this paper specifically address these challenges for feature learning. They learn features that allow to distinguish between classes by demanding that encodings of two trials from the same class are more similar to each other than to encoded trials from other classes. This tuple-based training approach is especially suitable for small datasets. The proposed technique is evaluated using the publicly available OpenMIIR dataset of EEG recordings taken while participants listened to and imagined music. For this dataset, a simple convolutional filter can be learned that significantly improves the signal-to-noise ratio while aggregating the 64 EEG channels into a single waveform.