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

Methods for motion artifact reduction in online brain-computer interface experiments: a systematic review

Brain-computer interfaces (BCIs) have emerged as a promising technology for enhancing communication between the human brain and external devices. Electroencephalography (EEG) is particularly promising in this regard because it has high temporal resolution and can be easily worn on the head in everyday life. However, motion artifacts caused by muscle activity, fasciculation, cable swings, or magnetic induction pose significant challenges in real-world BCI applications. In this paper, we present a systematic review of methods for motion artifact reduction in online BCI experiments. Using the PRISMA filter method, we conducted a comprehensive literature search on PubMed, focusing on open access publications from 1966 to 2022. We evaluated 2,333 publications based on predefined filtering rules to identify existing methods and pipelines for motion artifact reduction in EEG data. We present a lookup table of all papers that passed the defined filters, all used methods, and pipelines and compare their overall performance and suitability for online BCI experiments. We summarize suitable methods, algorithms, and concepts for motion artifact reduction in online BCI applications, highlight potential research gaps, and discuss existing community consensus. This review aims to provide a comprehensive overview of the current state of the field and guide researchers in selecting appropriate methods for motion artifact reduction in online BCI experiments.

The reliability of P300 and the influence of age, gender and education variables in a 50 years and older normative sample

OBJECTIVES

The present study aims to investigate the effects of age, gender, and level of education on P300 in a healthy population, aged 50 years and over; and determine the reliability metrics for different conditions and measurement methods.

METHOD

Auditory and visual oddball recordings of 171 healthy adults were investigated. A fully automated preprocessing was applied to elicit ERP P300. Maximum peak amplitude, latency and mean amplitudes were measured. Data were stratified by age, gender, and education to determine group-level differences by using repeat measures of ANOVA. The internal consistency of P300 was calculated by a split-half method using odd-even segments. Test-retest reliability was assessed by calculating the intraclass correlation coefficient (ICC).

RESULTS

Maximum peak P300 amplitudes were higher in the 50-64 years age group compared to the >65 years age group; and females showed increased P300 amplitudes compared to males. P300 measures showed fair to good internal consistency and poor to good test-retest reliability.

CONCLUSION

Age and gender should be taken into account when designing ERP studies with elderly individuals. P300 showed good internal consistency in general, between gender groups and age groups. Long-term test-retest reliability was lower but acceptable. These findings can be interpreted as the strength of P300 by being an objective and reliable method independent of cultural differences. Here we underline several factors that may affect P300 measures and discuss other possible factors that should be standardized for P300 to be used in clinical settings.

Validation of Continuous Monitoring System for Epileptic Users in Outpatient Settings

Epilepsy is a chronic disease with a significant social impact, given that the patients and their families often live conditioned by the possibility of an epileptic seizure and its possible consequences, such as accidents, injuries, or even sudden unexplained death. In this context, ambulatory monitoring allows the collection of biomedical data about the patients’ health, thus gaining more knowledge about the physiological state and daily activities of each patient in a more personalized manner. For this reason, this article proposes a novel monitoring system composed of different sensors capable of synchronously recording electrocardiogram (ECG), photoplethysmogram (PPG), and ear electroencephalogram (EEG) signals and storing them for further processing and analysis in a microSD card. This system can be used in a static and/or ambulatory way, providing information about the health state through features extracted from the ear EEG signal and the calculation of the heart rate variability (HRV) and pulse travel time (PTT). The different applied processing techniques to improve the quality of these signals are described in this work. A novel algorithm used to compute HRV and PTT robustly and accurately in ambulatory settings is also described. The developed device has also been validated and compared with other commercial systems obtaining similar results. In this way, based on the quality of the obtained signals and the low variability of the computed parameters, even in ambulatory conditions, the developed device can potentially serve as a support tool for clinical decision-taking stages.