A Correlation-Driven Mapping For Deep Learning application in detecting artifacts within the EEG

Abstract

Objective: When developing approaches for automatic preprocessing of electroencephalogram (EEG) signals in non-isolated demanding environment such as intensive care unit (ICU) or even outdoor environment, one of the major concerns is varying nature of characteristics of different artifacts in time, frequency and spatial domains, which in turn causes a simple approach to be not enough for reliable artifact removal. Considering this, current study aims to use correlation-driven mapping to improve artifact detection performance.Approach: A framework is proposed here for mapping signals from multichannel space (regardless of the number of EEG channels) into two-dimensional RGB space, in which the correlation of all EEG channels is simultaneously taken into account, and a deep convolutional neural network (CNN) model can then learn specific patterns in generated 2D representation related to specific artifact.Main results: The method with a classification accuracy of 92.30% (AUC=0.96) in a leave-three-subjects-out cross-validation procedure was evaluated using data including 2310 EEG sequences contaminated by artifacts and 2285 artifact-free EEG sequences collected with BrainStatus self-adhesive electrode and wireless amplifier from 15 intensive care patients. For further assessment, several scenarios were also tested including performance variation of proposed method under different segment lengths, different numbers of isoline and different numbers of channel. The results showed outperformance of CNN fed by correlation coefficients data over both spectrogram-based CNN and EEGNet on the same dataset.Significance: This study showed the feasibility of utilizing correlation image of EEG channels coupled with deep learning as a promising tool for dimensionality reduction, channels fusion and capturing various artifacts patterns in temporal-spatial domains. A simplified version of proposed approach was also shown to be feasible in real-time application with latency of 0.0181 s for making real-time decision.

Reconstruction of missing channel in electroencephalogram using spatiotemporal correlation-based averaging

“…HEN it comes to electroencephalography (EEG) recordings as one of the major modalities, widely used for neural systems and rehabilitation applications, there are many sources of variabilities including impedance change, shifts in electrode position, electrode popping and electrode shortcuts [1][2][3]. These faulty recordings lead to missing channels.…”

Real-time noise cancellation with deep learning

“…Traditionally, deep learning is a classifier and has been used to detect EEG artefacts with high accuracy of up to 90% [ 36 – 38 ] but not to remove the artefacts from the EEG. Deep learning can also assist ICA-based algorithms [ 6 , 9 ] to identify the principal components which contain the EMG noise [ 39 ].…”

Real-Time Noise Cancellation With Deep Learning

“…Thus, in terms of computational cost not only the standard encoder architecture is beneficial because of its wide availability but also makes it possible to directly use deep learning optimised hardware such as GPUs to perform the computations. Traditionally, deep learning is a classifier and has been used to detect EEG artefacts with high accuracy of up to 90% [36][37][38] but not to remove the artefacts from the EEG. Deep learning can also assist ICA-based algorithms [6,9] to identify the principal components which contain the EMG noise [39].…”