The Influence of Filters on EEG-ERP Testing: Analysis of Motor Cortex in Healthy Subjects.

The raw EEG signal is always contaminated with many different artifacts, such as muscle movements (electromyographic artifacts), eye blinking (electrooculographic artifacts) or power line disturbances. All artifacts must be removed for correct data interpretation. However, various noise reduction methods significantly influence the final shape of the EEG signal and thus its characteristic values, latency and amplitude. There are several types of filters to eliminate noise early in the processing of EEG data. However, there is no gold standard for their use. This article aims to verify and compare the influence of four various filters (FIR, IIR, FFT, NOTCH) on the latency and amplitude of the EEG signal. By presenting a comparison of selected filters, the authors intend to raise awareness among researchers as regards the effects of known filters on latency and amplitude in a selected area-the sensorimotor area.

Experiencing fear during the pandemic: validation of the fear of COVID-19 scale in Polish.

BACKGROUND: The Fear of COVID-19 Scale (FCV-19S) is a brief self-report measure developed at the beginning of the coronavirus pandemic. The scale evaluates the emotional responses to COVID-19. To date, the FCV-19S has been translated and validated in about 20 languages and has been used in many published research. The current study aimed to validate the Polish version of the FCV-19S. METHOD: The FCV-19S was translated into Polish using forward- and back-translation. From May 15th to June 15th 2020, a total of 708 community members (Sample 1: 383 persons, 209 females, Sample 2: 325 persons, 198 females) participated in the online study. The participants were recruited using online advertisements in social media. Participation was anonymous, voluntary, and without compensation. A confirmatory factor analysis was performed to test the one-factor structure of the FCV-19S. Then, measurement invariance was analyzed across samples, gender and age groups. To assess the validity of the scale, correlations between the FCV-19S and the remaining scales were computed. RESULTS: Internal consistency of the FCV-19S was good in both samples (Cronbach's alpha 0.89 and 0.85). The CFA showed that the one-factor model fits the data well (RMSEA = 0.067, 90% CI [0.059-0.094], CFI = 0.977, TLI = 0.965, GFI = 0.986). The criteria for configural, metric, scalar and strict invariance were met for all models tested. The FCV-19S scores correlated significantly with age, subjective vulnerability to the disease, neuroticism, self-reported compliance with the pandemic measures, and three kinds of preventive behavior (i.e., social distancing, hand hygiene, and disinfecting things). CONCLUSION: The Polish version of the FCV-19S had a unidimensional structure, good reliability, and correlated as predicted with other variables. With the FCV-19S and the obtained results, healthcare professionals, researchers, and the government can gain more valuable information about people who may be at risk for negative psychological outcomes during the pandemic or who are not implementing protective behavior. The tool can be used in hospitals to quickly screen the level of fear in patients and minimize its severe adverse consequences.