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Objective: With the rapid rise of wearable sleep monitoring devices withnon-conventional electrode configurations, there is a need for automatedalgorithms that can perform sleep staging on configurations with small amountsof labeled data. Transfer learning has the ability to adapt neural networkweights from a source modality (e.g. standard electrode configuration) to a newtarget modality (e.g. non-conventional electrode configuration). Methods: Wepropose feature matching, a new transfer learning strategy as an alternative tothe commonly used finetuning approach. This method consists of training a modelwith larger amounts of data from the source modality and few paired samples ofsource and target modality. For those paired samples, the model extractsfeatures of the target modality, matching these to the features from thecorresponding samples of the source modality. Results: We compare featurematching to finetuning for three different target domains, with two differentneural network architectures, and with varying amounts of training data.Particularly on small cohorts (i.e. 2 - 5 labeled recordings in thenon-conventional recording setting), feature matching systematicallyoutperforms finetuning with mean relative differences in accuracy ranging from0.4% to 4.7% for the different scenarios and datasets. Conclusion: Our findingssuggest that feature matching outperforms finetuning as a transfer learningapproach, especially in very low data regimes. Significance: As such, weconclude that feature matching is a promising new method for wearable sleepstaging with novel devices.

Feature matching as improved transfer learning technique for wearable EEG