An amplitudes-perturbation data augmentation method in convolutional neural networks for EEG decoding
This addresses the challenge of limited EEG data for deep learning models, though it is incremental as it builds on existing data augmentation techniques.
The paper tackled the problem of data insufficiency in EEG decoding for Brain-Computer Interfaces by proposing a novel data augmentation method that adds perturbations to amplitudes in the frequency domain, which improved recognition accuracy on benchmark and local datasets.
Brain-Computer Interface (BCI) system provides a pathway between humans and the outside world by analyzing brain signals which contain potential neural information. Electroencephalography (EEG) is one of most commonly used brain signals and EEG recognition is an important part of BCI system. Recently, convolutional neural networks (ConvNet) in deep learning are becoming the new cutting edge tools to tackle the problem of EEG recognition. However, training an effective deep learning model requires a big number of data, which limits the application of EEG datasets with a small number of samples. In order to solve the issue of data insufficiency in deep learning for EEG decoding, we propose a novel data augmentation method that add perturbations to amplitudes of EEG signals after transform them to frequency domain. In experiments, we explore the performance of signal recognition with the state-of-the-art models before and after data augmentation on BCI Competition IV dataset 2a and our local dataset. The results show that our data augmentation technique can improve the accuracy of EEG recognition effectively.