Dry EEG in Sports Sciences: A Fast and Reliable Tool to Assess Individual Alpha Peak Frequency Changes Induced by Physical Effort

Abstract

Novel state-of-the-art amplifier and cap systems enable Electroencephalography (EEG) recording outside of stationary lab systems during physical exercise and body motion. However, extensive preparation time, cleaning, and limited long-term stability of conventional gel-based electrode systems pose significant limitations in out-of-the-lab conditions. Dry electrode systems may contribute to rapid and repetitive mobile EEG acquisition with significantly reduced preparation time, reduced cleaning requirements, and possible self-application by the volunteer but are known for higher channel failure probability and increased sensitivity to movement artifacts. We performed a counterbalanced repeated measure endurance cycling study to objectively validate the performance and applicability of a novel commercially available 64-channel dry electrode cap for sport science. A total of 17 healthy volunteers participated in the study, performing an endurance cycling paradigm comprising five phases: (I) baseline EEG, (II) pre-cycling EEG, (III) endurance cycling, (IV) active recovery, and (V) passive recovery. We compared the performance of the 64-channel dry electrode cap with a commercial gel-based cap system in terms of usability metrics, reliability, and signal characteristics. Furthermore, we validated the performance of the dry cap during a realistic sport science investigation, verifying the hypothesis of a systematic, reproducible shift of the individual alpha peak frequency (iAPF) induced by physical effort. The average preparation time of the dry cap was one-third of the gel-based electrode caps. The average channel reliability of the dry cap varied between 80 ± 15% (Phase I), 66 ± 19% (Phase III), and 91 ± 10% (Phase V). In comparison, the channel reliability of the gel-based cap varied between 95 ± 3, 85 ± 9, and 82 ± 9%, respectively. No considerable differences were evident for the comfort evaluations nor the signal characteristics of both caps. A within-volunteers repeated measure analysis of variance (RM-ANOVA) did not show significant effects of the electrode type on the iAPF [F(1,12) = 1.670, p = 0.221, ηp2 = 0.122, Power = 0.222]. However, a significant increase of the iAPF exists from Phase II to Phases IV and V due to exhaustive physical task. In conclusion, we demonstrated that dry electrode cap is equivalent to the gel-based electrode cap based on signal characteristics, comfort, and signal information content, thereby confirming the usefulness of dry electrodes in sports science and other mobile applications involving ample movement.

Modulation of Brain Functional Connectivity and Efficiency During an Endurance Cycling Task: A Source-Level EEG and Graph Theory Approach

“…Rated perceived exertion was collected every minute during cycling to detect the participants’ exhaustion using the CR-10 Borg scale ( Borg and Borg, 2010 ; Staiano et al, 2018 ). Given that reaching exhaustion is an individual process that depends on the participants’ fitness and expertise ( di Fronso et al, 2018 , 2019 ), the duration of the endurance task ranged from approximately 10 to 28 min (18 ± 5 min). Exhaustion was immediately followed by an active recovery period of 2 min, during which the participants continued cycling with eyes closed and the ergometer power was set back to 50 W. The task ended with a passive recovery period of 2 min, during which the participants were asked to stop cycling and maintain their eyes closed while sitting on the cycle-ergometer.…”

Mobile brain/body imaging of landmark-based navigation with high-density EEG

“…Recent technical developments have unlocked the possibility of using EEG brain imaging in a variety of ecological conditions (indoor walking: Luu et al ., 2017; Ladouce et al ., 2019; Park & Donaldson, 2019; outdoor walking: Debener et al ., 2012; Reiser et al ., 2019; cycling: Zink et al ., 2016; di Fronso et al ., 2019; dual-tasking: Marcar et al ., 2014; Bohle et al ., 2019). By coupling EEG recordings with other biometric measures (e.g., body and eye movements), the Mobile Brain/Body Imaging (MoBI) approach gives access to unprecedented behavioral and neural data analysis (Makeig et al ., 2009; Gramann et al ., 2011, 2014; Ladouce et al ., 2017).…”

Is Brain Dynamics Preserved in the EEG After Automated Artifact Removal? A Validation of the Fingerprint Method and the Automatic Removal of Cardiac Interference Approach Based on Microstate Analysis

“…The investigation of the human brain function largely relies on electroencephalography (EEG), a technique characterized by an excellent temporal resolution ( Niedermeyer and da Silva, 2005 ) that has recently undergone several technological advances in the electronics and sensor components to enable continuous, out-of-the-lab, and mobile EEG acquisitions ( Thompson et al, 2008 ; Del Percio et al, 2011 ; De Vos et al, 2011 ; Lance et al, 2012 ; Askamp and van Putten, 2014 ; Liao et al, 2014 ; Lopez-Gordo et al, 2014 ; Comani et al, 2015 ; Fiedler et al, 2015 ; Michel et al, 2015 ; di Fronso et al, 2016 , 2019 ; Filho et al, 2016 ). A drawback of these latest advancements is that they have also enhanced the EEG sensitivity to severe contamination by electrical activity generated outside of cerebral sources, which can seriously affect the analysis and interpretation of EEG signals.…”