Fully automated unsupervised artefact removal in multichannel electroencephalogram using wavelet‐independent component analysis with density‐based spatial clustering of application with noise

Abstract

Abstract Electroencephalography (EEG) is a method for recording electrical activities arising from the cortical surface of the brain, which has found wide applications not just in clinical medicine, but also in neuroscience research and studies of Brain‐Computer Interface (BCI). However, EEG recordings often suffer from distortions due to artefactual components that degrade the true EEG signals. Artefactual components are any unwanted signals recorded in the EEG spectrum that originate from sources other than the neurophysiological activity of the human brain. Examples of the origin of artefactual components include eye blinking, facial or scalp muscles activities, and electrode slippage. Techniques for automated artefact removal such as Wavelet Transform and Independent Component Analysis (ICA) have been used to remove or reduce the effect of artefactual components on the EEG signals. However, detecting or identifying the signal artefacts to be removed presents a great challenge, as EEG signal properties vary between individuals and age groups. Techniques that rely on some arbitrarily defined threshold often fail to identify accurately the signal artefacts in a given dataset. In this study, a method is proposed using unsupervised machine learning coupled with Wavelet‐ICA to remove EEG artefacts. Using Density‐Based Spatial Clustering of Application with Noise (DBSCAN), a classification accuracy of 97.9% is achieved in identifying artefactual components. DBSCAN achieved excellent and robust performance in identifying artefactual components during the Wavelet‐ICA process, especially in consideration of the low‐density nature of typical artefactual signals. This new hybrid method provides a scalable and unsupervised solution for automated artefact removal that should be applicable for a wide range of EEG data types.

Keywords

• brain
• electroencephalography
• independent component analysis
• learning (artificial intelligence)
• medical signal detection
• medical signal processing