Denoising Autoencoder for Biosignals: Denoising Autoencoder for Biosignals
Term
4. term
Education
Publication year
2021
Submitted on
2022-06-17
Pages
16
Abstract
This paper introduces a self-supervised framework for pre-training on EEG data. The goal is to create a model that produces good features, such that the model can be used for transfer learning. Our framework is based on a denoising autoencoder architecture. We have a model that receives an input that is augmented using token masking, and tries to reconstruct the original input. The pre-training is done using a subset of the Temple University Hospital EEG Data Corpus (TUEG) datasets. Our model is a transformer based model, inspired by the likes of BERT. For our results we ran benchmarks on the 3 different datasets we did fine tuning on. Here we compare to some supervised methods. The results show that our model, though not the best, is comparable to the supervised models. The benchmark proves that the model has learned transferable features.
Denoising Autoencoder for Biosignals
Keywords
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