Automatic Removal of Physiological Artifacts in EEG: The Optimized Fingerprint Method for Sports Science Applications

Abstract

Data contamination due to physiological artifacts such as those generated by eyeblinks, eye movements, and muscle activity continues to be a central concern in the acquisition and analysis of electroencephalographic (EEG) data. This issue is further compounded in EEG sports science applications where the presence of artifacts is notoriously difficult to control because behaviors that generate these interferences are often the behaviors under investigation. Therefore, there is a need to develop effective and efficient methods to identify physiological artifacts in EEG recordings during sports applications so that they can be isolated from cerebral activity related to the activities of interest. We have developed an EEG artifact detection model, the Fingerprint Method, which identifies different spatial, temporal, spectral, and statistical features indicative of physiological artifacts and uses these features to automatically classify artifactual independent components in EEG based on a machine leaning approach. Here, we optimized our method using artifact-rich training data and a procedure to determine which features were best suited to identify eyeblinks, eye movements, and muscle artifacts. We then applied our model to an experimental dataset collected during endurance cycling. Results reveal that unique sets of features are suitable for the detection of distinct types of artifacts and that the Optimized Fingerprint Method was able to correctly identify over 90% of the artifactual components with physiological origin present in the experimental data. These results represent a significant advancement in the search for effective means to address artifact contamination in EEG sports science applications.

Focusing Attention on Muscle Exertion Increases EEG Coherence in an Endurance Cycling Task

“…We conducted this study with a stationary EEG system and wet electrodes, which did not totally allow to avoid the artifacts in low frequency ranges. More advanced EEG equipment (e.g., mobile EEG systems, dry-electrode technology) and new algorithms for EEG data pre-processing (Stone et al, 2018 ; Tamburro et al, 2018 ) could allow to consider also the theta band, hence paving the way to a better interpretation of EEG data in the light of the neural efficiency hypothesis. From a theoretical point of view, it might be important to investigate also cortico-muscolar coherence during voluntary movements (Marsden et al, 2000 ), as it could provide useful information on specific functional connections between the cortex and the engaged muscles (Travis et al, 2002 ), and a better understanding of the brain-body interaction and integration (Tang and Bruya, 2017 ).…”

The Relevance of a Conductor Competition for the Study of Emotional Synchronization Within and Between Groups in a Natural Musical Setting

“…Some studies using EEG hyperscanning have investigated the synchronization between musicians playing in groups (Babiloni et al, 2006;Lindenberger et al, 2009;Müller et al, 2012Müller et al, , 2013. The technological progress provided by recently developed portable EEG equipment combined with increasingly powerful artifact detection methods (Stone et al, 2018) facilitates realistic experiments, especially those studying several musicians simultaneously (Babiloni et al, 2011(Babiloni et al, , 2012Müller et al, 2012Müller et al, , 2013Müller et al, , 2018. As previously emphasized, classical musicians performing a musical score in a relatively stationary position allows researchers to record EEG data with a high signal-to-noise ratio (Babiloni et al, 2011;D'Ausilio et al, 2015), as well as a good physiological recording.…”

Exploring the Limitations of Event-Related Potential Measures in Moving Subjects: Pilot Studies of Four Different Technical Modifications in Ergometer Rowing

“…Conventional EEG systems are very sensitive to mechanical (cable and electrode movements) and physiological (electromyogram (EMG) of head and neck muscles, and sweating) movement artefacts [ 14 ]. If cognitive processes in a moving subject rather than the movement itself are the focus of interest, data preprocessing algorithms informed by the behavioural movement data can be used to clean the EEG data from movement-related (neuronal and artefactual) activity [ 15 , 16 ]. If the motor-related activity is of interest, advanced data preprocessing algorithms such as independent component analysis (ICA) [ 17 ] can be used to correct such artefacts.…”