Feature Learning from Incomplete EEG with Denoising Autoencoder

TL;DR

This paper introduces a novel approach combining Lomb-Scargle periodogram and Denoising Autoencoder to effectively decode incomplete EEG signals contaminated by artefacts, improving brain-computer interface performance.

Contribution

It presents a new method for decoding incomplete EEG data using spectral estimation and autoencoders, enabling BCI operation despite artefacts.

Findings

Successfully decodes incomplete EEG signals

Outperforms traditional filtering methods

Maintains BCI functionality during artefacts

Abstract

An alternative pathway for the human brain to communicate with the outside world is by means of a brain computer interface (BCI). A BCI can decode electroencephalogram (EEG) signals of brain activities, and then send a command or an intent to an external interactive device, such as a wheelchair. The effectiveness of the BCI depends on the performance in decoding the EEG. Usually, the EEG is contaminated by different kinds of artefacts (e.g., electromyogram (EMG), background activity), which leads to a low decoding performance. A number of filtering methods can be utilized to remove or weaken the effects of artefacts, but they generally fail when the EEG contains extreme artefacts. In such cases, the most common approach is to discard the whole data segment containing extreme artefacts. This causes the fatal drawback that the BCI cannot output decoding results during that time. In order to solve this problem, we employ the Lomb-Scargle periodogram to estimate the spectral power from incomplete EEG (after removing only parts contaminated by artefacts), and Denoising Autoencoder (DAE) for learning. The proposed method is evaluated with motor imagery EEG data. The results show that our method can successfully decode incomplete EEG to good effect.