A fast and scalable framework for automated artifact recognition from EEG signals represented in scalp topographies of Independent Components. (May 2021)

• Record Type: Journal Article
• Authors: Placidi, Giuseppe; Cinque, Luigi; Polsinelli, Matteo
• Is Part Of: Computers in biology and medicine. Volume 132(2021)
• Publication Date: 2021-05
• Subjects: EEG -- EEG artifact recognition -- CNN EEG -- Pre-processing ICA -- IC scalp Topography -- Topoplots -- Online BCI; Medicine -- Data processing -- Periodicals; Biology -- Data processing -- Periodicals; 610.285
• DOI: 10.1016/j.compbiomed.2021.104347
• Languages: English
• ISSNs: 0010-4825

Abstract

Abstract: Background and objectives: Electroencephalography (EEG) measures the electrical brain activity in real-time by using sensors placed on the scalp. Artifacts due to eye movements and blinking, muscular/cardiac activity and generic electrical disturbances, have to be recognized and eliminated to allow a correct interpretation of the Useful Brain Signals (UBS). Independent Component Analysis (ICA) is effective to split the signal into Independent Components (IC) whose re-projection on 2D topographies of the scalp (images also called Topoplots) allows to recognize/separate artifacts and UBS. Topoplot analysis, a gold standard for EEG, is usually carried out offline either visually by human experts or through automated strategies, both unenforceable when a fast response is required as in online Brain-Computer Interfaces (BCI). We present a fully automatic, effective, fast, scalable framework for artifacts recognition from EEG signals represented in IC Topoplots to be used in online BCI. Methods: The proposed architecture, optimized to contain three 2D Convolutional Neural Networks (CNN), divides Topoplots in 4 classes: 3 types of artifacts and UBS. The framework architecture is described and the results are presented, discussed and indirectly compared with those obtained from state-of-the-art competitive strategies. Results: Experiments on public EEG datasets showed overall accuracy, sensitivity and specificity greater than 98%, taking 1.4 s on a standard PC for 32 Topoplots, i.e. for an EEG system with at least 32 sensors. Conclusions: The proposed framework is faster than other automatic methods based on IC analysis and fast enough to be used in EEG-based online BCI. In addition, its scalable architecture and ease of training are necessary conditions to apply it in BCI, where difficult operating conditions caused by uncontrolled muscle spasms, eye rotations or head movements, produce specific artifacts that need to be recognized and dealt with. Highlights: To define, train, validate and test a CNN-based system for EEG artifacts recognition by Independent Component Topoplots. To allow a fast enough artifact recognition to be used for online BCI. To provide a general and scalable tool for dealing with any kind of known artifacts and adaptable to future patterns. To provide a tool which is robust to sensors number/configuration and to run-time operating conditions.