Low Quality Samples Detection in Motor Imagery EEG Data by Combining Independent Component Analysis and Confident Learning

Abstract

Electroencephalogram (EEG), a non-invasive method of brain signal acquisition, is an important part of the research of motor-imagery brain-computer interface (MI-BCI). However, the collected EEG dataset are often contaminated by various kinds of noise and artifacts. Furthermore, noisy labeled samples are often generated due to fatigue and distraction of subject in data acquisition. These low-quality samples will deteriorate the performance of MI - BCI. Therefore, the data cleaning technique is needed in EEG-based BCI research. In this paper, the feasibility and performance of confident learning (CL) for detecting low-quality samples in motor imagery EEG (MI-EEG) data was studied. We found that the CL method, while very effective in image data cleaning, is not suitable for EEG processing due to the impact of artifacts in MI-EEG data. So, we proposed to use the simplified infomax (slnfomax) independent component analysis (ICA) as the preprocessing step to improve the signal to noise ratio (SNR) of MI-EEG. The experimental results on benchmark MI-EEG datasets via convolutional neural network (CNN) demonstrated that, compared with CL only, the combination of sInfomax and CL can achieve more reliable results in low-quality MI-EEG data selection.