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Transfer Learning and SpecAugment applied to SSVEP Based BCI Classification

2020-10-08 00:30:12
Pedro R. A. S. Bassi, Willian Rampazzo, Romis Attux
arXiv_AI
               
Abstract

In this work, we used a deep convolutional neural network (DCNN) to classify electroencephalography (EEG) signals in a steady-state visually evoked potentials (SSVEP) based brain-computer interface (BCI). The raw EEG signals were converted to spectrograms and served as input to train a DCNN using the transfer learning technique. We applied a second technique, data augmentation, mostly SpecAugment, generally employed to speech recognition. The results, when excluding the evaluated user's data from the fine-tuning process, reached 99.3% mean test accuracy and 0.992 mean F1 score on 35 subjects from an open dataset.

Abstract (translated)

URL

https://arxiv.org/abs/2010.06503

PDF

https://arxiv.org/pdf/2010.06503.pdf

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