/* ---- Google Analytics Code Below */

Monday, March 22, 2021

Data Augmentation for Brain-Computer Interface

Very good piece that also connects with some of our neural explorations.  The linking to GANs is fascinating.    (See tag links)

Data Augmentation for Brain-Computer Interface

New Business applications combined with Brain-computer interface and Generative Adversarial networks

Alexandre Gonfalonieri,  AI Consultant @Philips | I write about AI and BCI 

Despite significant progress in Brain-Computer Interface (BCI), many issues remain associated with collecting Electroencephalography (EEG) signals in real-world environments. This situation makes it difficult for BCIs to become a scalable device.

Brain-computer interface has always been facing severe data-related issues such as lack of sufficient data, lengthy calibration time and data corruption. In my latest project, we explored the idea of leveraging data augmentation methods such as generative adversarial networks to create synthetic EEG signals.

Indeed, data augmentation (DA) is a potential solution to address these issues. Among data augmentation techniques, the method of generative adversarial networks (GANs) with successful image processing applications has gained a lot of attention.

In this article, I’ll explain the issue of creating enough training data in the context of non-invasive BCIs and present a non-exhaustive list of data augmentation techniques for EEG datasets. I will also explain how GANs can be used to help BCIs in real-life applications.  .... " 

No comments: