Rebreikina, A. | bioRxiv Science

bioRxiv2023

Electrophysiological signatures of spelling sensitivity development from primary school age to adulthood

Recognizing spelling errors is important for correct writing and reading, and develops over an extended period. The neural bases of the development of orthographic sensitivity remain poorly understood. We investigated event-related potentials (ERPs) associated with spelling error recognition when performing the orthographic decision task with correctly spelled and misspelled words in children aged 8-10 years old, early adolescents aged 11-14 years old, and adults. Spelling processing in adults included an early stage associated with the initial recognition of conflict between orthography and phonology (reflected in the N400 time window) and a later stage (reflected in the P600 time window) related to re-checking the spelling. In children 8-10 years old, there were no differences in ERPs to correct and misspelled words; in addition, their behavioral scores were worse than those of early adolescents, implying that the ability to quickly recognize the correct spelling is just beginning to develop at this age. In early adolescents, spelling recognition was reflected only at the later stage, corresponding to the P600 component. At the behavioral level, they were worse than adults at recognizing misspelled words. Our data suggest that orthographic sensitivity can develop beyond 14 years.

neuroscience↗

bioRxiv2021

A Toolbox and Crowdsourcing Platform for Automatic Labeling of Independent Components in Electroencephalography (ALICE)

Independent Component Analysis (ICA) is a conventional approach to exclude non-brain signals such as eye-movements and muscle artifacts from electroencephalography (EEG). Due to other possible EEG contaminations, a rejection of independent components (ICs) is usually performed in semiautomatic mode and requires experts involvement. Noteworthy, as also revealed by our study, experts opinion about the nature of a component often disagrees highlighting the need to develop a robust and sustainable automatic system for EEG ICs classification. The current article presents a toolbox and crowdsourcing platform for Automatic Labeling of Independent Components in Electroencephalography (ALICE) available via link http://alice.adase.org/. The ALICE toolbox aims to build a sustainable algorithm not only to remove artifacts but also to find specific patterns in EEG signals using ICA decomposition based on accumulated experts knowledge. The difference from previous toolboxes is that the ALICE project will accumulate different benchmarks based on crowdsourced visual labeling of ICs collected from publicly available and in-house EEG recordings. The choice of labeling is based on estimation of IC time-series, IC amplitude topography and spectral power distribution. The platform allows supervised ML model training and re-training on available data subsamples for better performance in specific tasks (i.e. movement artifact detection in healthy or autistic children). Also, current research implements the novel strategy for consentient labeling of ICs by several experts. The provided baseline model shows that it can be used not only for detection of noisy IC but also for automatic identifications of components related to the functional brain oscillations such as alpha and mu-rhythm. The ALICE project implies the creation and constant replenishment of the IC database, which will be used for continuous improvement of ML algorithms for automatic labeling and extraction of non-brain signals from EEG. The toolbox and current dataset are open-source and freely available to the researcher community.

neuroscience↗