Transfer Learning and SpecAugment applied to SSVEP Based BCI Classification

10/08/2020
by   Pedro R. A. S. Bassi, et al.
13

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.

READ FULL TEXT

Please sign up or login with your details

Forgot password? Click here to reset