Increasing Performance of Professional Soccer Players and Elite Track and Field Athletes with Peak Performance Training and Biofeedback: A Pilot Study.

The aim of this pilot study was to investigate the effects of an intervention consisting of mental coaching combined with either electro encephalogram (EEG) alpha power feedback or heart rate variability (HRV) feedback on HRV, EEG outcomes and self-reported factors related to stress, performance, recovery and sleep quality in elite athletes. A prospective pilot study was performed with two distinct cohorts. Soccer players were provided with four sessions of mental coaching combined with daily HRV biofeedback (Group A); track and field athletes were provided with four sessions of mental coaching in combination with daily neurofeedback (Group B). Measurements were performed at baseline, post intervention and at 5 weeks follow-up. Objective measures: EEG and ECG. Subjective measures: Numeric Rating Scale for performance, Pittsburgh Sleep Quality Index, Rest and Stress Questionnaire and Sports Improvement-60. Group characteristics were too distinct to compare the interventions. Linear mixed models were used to analyze differences within groups over time. In Group A, significant changes over time were present in alpha power at 5 of 7 EEG locations (p < 0.01-0.03). LF/HF ratio significantly increased (p = 0.02) and the concentration (p = 0.02) and emotional scale (p = 0.03) of the SIM-60 increased significantly (p = 0.04). In Group B, the HRV low frequency power and recovery scale of the REST-Q significantly increased (p = 0.02 and <0.01 resp.). Other measures remained stable or improved non-significantly. A mental coaching program combined with either HRV or EEG alpha power feedback may increase HRV and alpha power and may lead to better performance-related outcomes and stress reduction. Further research is needed to elucidate the effects of either type of feedback and to compare effects with a control group.

Use of power-line interference for adaptive motion artifact removal in biopotential measurements.

Motion artifacts (MA) have long been a problem in biopotential measurements. Adaptive filtering is widely used for optimal noise removal in many biomedical applications. However, the existing adaptive filtering methods involve the use of additional sensors, limiting the applicability of adaptive filtering for MA reduction. In the present study, a novel adaptive filtering method without need for additional sensors is proposed. In biopotential measurements, movement of the electrodes and their leads may cause variations not only in the skin and half-cell potential (motion artifacts), but also in the electrode-skin impedance. Such impedance variations may also cause power-line interference modulation (PLIM), resulting in additional spectral components around the power-line interference (PLI) in the frequency domain. Demodulation of the PLI may reflect the movement-induced electrode-skin impedance variation, and can therefore represent a reference signal for the adaptive filter. Preliminary validation on ECG measurements with seven volunteers showed a high correlation coefficient (R = 0.97) between MA and PLIM, and excellent MA removal by the proposed adaptive filter, possibly leading to improved analysis of biopotential signals.

A multiscale model of the electrohysterogram the BioModUE_PTL project.

The electrohysterogram (EHG) is a promising means of monitoring pregnancy and of detecting a risk of preterm labor. To improve our understanding of the EHG as well as its relationship with the physiologic phenomena involved in uterine contractility, we plan to model these phenomena in terms of generation and propagation of uterine electrical activity. This activity can be realistically modeled by representing the principal ionic dynamics at the cell level, the propagation of electrical activity at the tissue level and then the way it is reflected on the skin surface through the intervening tissue. We present in this paper the different steps leading to the development and validation of a biophysics based multiscale model of the EHG, going from the cell to the electrical signal measured on the abdomen.

Brain-computer interface using water-based electrodes.

Current brain-computer interfaces (BCIs) that make use of EEG acquisition techniques require unpleasant electrode gel causing skin abrasion during the standard preparation procedure. Electrodes that require tap water instead of electrolytic electrode gel would make both daily setup and clean up much faster, easier and comfortable. This paper presents the results from ten subjects that controlled an SSVEP-based BCI speller system using two EEG sensor modalities: water-based and gel-based surface electrodes. Subjects performed in copy spelling mode using conventional gel-based electrodes and water-based electrodes with a mean information transfer rate (ITR) of 29.68 ± 14.088 bit min(-1) and of 26.56 ± 9.224 bit min(-1), respectively. A paired t-test failed to reveal significant differences in the information transfer rates and accuracies of using gel- or water-based electrodes for EEG acquisition. This promising result confirms the operational readiness of water-based electrodes for BCI applications.