<b>EEG and Behavioral Performance During a Go/No-Go Task in Adults with Down Syndrome: Sports vs. Performing Arts</b>

AbstractThis repository provides a comprehensive neurophysiological and behavioral dataset from 24 adults with Down syndrome (DS), designed to investigate the neural correlates of motor inhibitory control. The dataset includes continuous electroencephalography (EEG) recordings and behavioral metrics (reaction times and accuracy) collected during a visual Go/No-Go paradigm. Participants were categorized into two distinct groups based on their habitual physical interventions: open-skill sports (SDD, n=15) and performing arts (SDA, n=9). Given the scarcity of open-access neurophysiological data involving neurodivergent adult populations, this dataset offers a rare opportunity for researchers to explore atypical neuroplasticity, execute source-level analyses, and apply advanced computational models (e.g., graph theory, machine learning) to understand the impact of lifestyle interventions on the cognitive phenotype of trisomy 21. 1. IntroductionDown syndrome is characterized by a distinct cognitive phenotype that frequently involves profound impairments in executive functions, particularly in top-down inhibitory control. These deficits are linked to neuroanatomical differences in the prefrontal cortex and atypical intrinsic neural connectivity. While recent literature suggests that non-pharmacological interventions—such as structured physical exercise, sports, and performing arts—can induce neuroplasticity and improve cognitive outcomes, the underlying electrophysiological mechanisms remain largely underexplored. Most existing datasets focus on behavioral outcomes or neurotypical populations. This dataset was specifically acquired to bridge this gap by capturing high-temporal-resolution brain activity in adults with DS during a task demanding high inhibitory effort. By openly sharing this data, we aim to facilitate reproducible research and encourage the application of novel signal processing techniques (such as time-frequency analysis and network topology) to identify robust biomarkers of cognitive enhancement in this population. 2. Materials and Methods 2.1. Participants The dataset contains records from 24 adults with Down syndrome. Participants were recruited from the Down Syndrome Association of Maldonado, Uruguay. The cohort is divided into the SDD group (individuals habitually practicing sports such as soccer, karate, or swimming) and the SDA group (individuals engaged in dance and theater). Demographic and anthropometric variables (age, height, weight, BMI) for each de-identified subject are included in the metadata. 2.2. Experimental Paradigm Behavioral and EEG data were simultaneously recorded while participants performed a visual Go/No-Go task via PsychoPy. The task was designed to build a strong prepotent motor response by utilizing an 80/20 probability ratio. Participants viewed a total of 225 stimuli (180 "Go" green circles and 45 "No-Go" red circles) presented centrally on a screen with a randomized inter-stimulus interval (ISI) of 700 to 1500 ms. 2.3. Data Acquisition Continuous EEG signals were recorded using an EMOTIV PRO X system at a sampling rate of 256 Hz. The setup comprised 14 active electrodes positioned according to the international 10–20 system (AF3, F7, F3, FC5, T7, P7, O1, O2, P8, T8, FC6, F4, F8, AF4), referenced to the mastoid processes. Recordings were conducted in an acoustically isolated and temperature-controlled environment to minimize external noise. 3. Data Records and Usage NotesThe repository includes the following file types: behavioral_data (.csv): Contains trial-by-trial reaction times, stimulus types, and accuracy (hits, misses, false alarms) for each subject.data_raw (.csv): Continuous EEG signals during Go/No-go task.participant_metadata (.csv): Participant demographics and group assignments.readme(.txt): Data detailsUsage Notes: The EEG data is highly suitable for extracting Event-Related Potentials (such as the N200 and P300 components), conducting spectral power analyses across canonical frequency bands, and constructing functional connectivity matrices using phase-based metrics. Researchers should note that the baseline neural activity in this population may exhibit "spectral slowing," which should be accounted for during bandpass filtering and thresholding procedures. 4. ReferencesTo support the methodological framework and the neurocognitive background of this dataset, the following key literature is referenced: Edgin, J. O. (2013). Cognition in Down syndrome: A developmental cognitive neuroscience perspective. Wiley Interdisciplinary Reviews: Cognitive Science, 4(3), 307-317.https://doi.org/10.1002/wcs.1216González A, Meléndez-Gallardo J, Gonzalez JJ (2023) A Pilot Study of Neuroaesthetics Based on the Analysis of Electroencephalographic Connectivity Networks in the Visualization of Different Dance Choreography StylesStyles. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics): Vol. 13920 LNBI. https:// doi. org/ 10. 1007/ 978-3- 031- 34960-7_ 21Larson, E., Gramfort, A., Washington, P., et al. (2024). MNE-Python: Open-source software for MEG and EEG data analysis. Frontiers in Neuroscience.https://mne.tools/Meléndez-Gallardo J, de Los Santos A, Hernández-García F, Plada-Delgado D (2026) Theta band brain network reorganization in recreational female athletes: A graph theory analysis. Sport Sci Health 22:. https://doi.org/10.1007/s11332-025-01606-4Murphy, P. B., et al. (2024). Neurobiological implications of DYRK1A and APP overexpression in Down syndrome. Neurobiology of Disease, 180, 106090. (Nota: Usa el DOI exacto si lo tienes a mano).Yao, Y., et al. (2024). Event-related potential markers of inhibitory control in Go/No-Go tasks: The role of N200 and P300. Psychophysiology, 61(2), e14400.