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# README ## Details related to access to the data Please contact the following authors for further information: - Tong Shan (email: tshan@ur.rochester.edu) - Ross K. Maddox (email: rmaddox@ur.rochester.edu) ## Overview The goal of this study is to derive Auditory Brainstem Response (ABR) from continuous music and speech stimuli using deconvolution method. Data collected from Jun to Aug, 2021. The details of the experiment can be found at Shan et al. (2024). There were two phases in this exp

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22

Size

213 GB

## Executive Functioning Tasks The data of this dataset was collected as part of an executive functioning battery consisting of three separate tasks: 1) N-Back (NB) 2) Sustained Attention to Response Task (SART) 3) Local Global (LG) The original experiment details in which these tasks were conducted in addition to can be read about here (https://doi.org/10.3389/fnhum.2020.00246). **Experiment Design:** Two sessions of each task were conducted on the first and last day of the neurofeedback

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24

Size

9.44 GB

Ear-EEG Sleep Monitoring 2017 (EESM17) data set **Overview** This dataset was collected as part of a research project on ear-EEG sleep monitoring which took place in 2017. The data set contains nightly EEG recordings from 9 healthy participants ('subjects'). The recordings consist of 'partial polysomnography' (PSG) measurements, including EEG, EOG and chin EMG combined with 14 ear-EEG electrodes. **Format** The dataset is formatted according to the Brain Imaging Data Structure. Se

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9

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8.19 GB

References ---------- Ferrante, O., Liu, L., Minarik, T., Gorska, U., Ghafari, T., Luo, H., & Jensen, O. (2022). FLUX: A pipeline for MEG analysis. NeuroImage, 253, 119047. https://doi.org/10.1016/j.neuroimage.2022.119047 Appelhoff, S., Sanderson, M., Brooks, T., Vliet, M., Quentin, R., Holdgraf, C., Chaumon, M., Mikulan, E., Tavabi, K., Höchenberger, R., Welke, D., Brunner, C., Rockhill, A., Larson, E., Gramfort, A. and Jas, M. (2019). MNE-BIDS: Organizing electrophysiological data into the

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1

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3.59 GB

SPN1 Experiment 1 Project 1. After stimulus offset, participants reported whether patterns were regular or random. For full catalogue, see https://osf.io/2sncj/

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24

Size

2.40 GB

This dataset contains the raw MEG data accompanying the paper "The spatiotemporal neural dynamics of object recognition for natural images and line drawings" (Link to preprint: https://biorxiv.org/cgi/content/short/2022.08.12.503484v1). Please cite the above paper if you use this data. The dataset includes: MEG data for 9 runs for each subjects. Events files that contain the onsets, durations and trial types for each trial in the experiment (excluding catch trials). For a full description of

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30

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154 GB

Reinforcement learning task with 50 healthy controls (25 after a sad mood manipulation, 25 after a happy mood manipulation) Task with a training section and testing section. Task adapted from here: https://doi.org/10.1126/science.1102941. Mood Manipulation occurs during task before each training block. Task included in Matlab programming language. Data collected from 2019-2021 in Cognitive Rhythms and Computation Lab at University of New Mexico. Check the .xls sheet under code folder f

Participants

50

Size

18.3 GB

Reinforcement learning task with 50 healthy controls (25 after a sad mood manipulation, 25 after a neutral mood manipulation) Task with a training section and testing section. Task adapted from here: https://doi.org/10.1126/science.1102941. Separate mood manipulation occurred earlier. Task included in Matlab programming language. Data collected from 2019-2021 in Cognitive Rhythms and Computation Lab at University of New Mexico. Check the .xls sheet under code folder for more meta data.

Participants

50

Size

9.81 GB

Two reversal learning tasks with different reinforcer (monetary reward vs. primary threat reinforcer). Positive feedback in the reward task indicated monetary reward (+10 Cent) and negative feedback monetary non-reward (+0 Cent). In the punishment task, positive feedback signaled successful avoidance of a loud white noise burst and negative feedback the application of the noise burst. The white noise burst intensity was titrated to match monetary reward (+10 Cent) for every participant (81 dB, 8

Participants

26

Size

31.5 GB

# Auditory single word recognition in MEG This dataset is described in Gaston et al. (2022). Stimuli and TextGrids are available from the Massive Auditory Lexical Decision database (Tucker et al., 2019). Converted to BIDS using MNE-BIDS (Appelhoff et al., 2019; Niso et al., 2018). # References Appelhoff, S., Sanderson, M., Brooks, T., Vliet, M., Quentin, R., Holdgraf, C., Chaumon, M., Mikulan, E., Tavabi, K., Höchenberger, R., Welke, D., Brunner, C., Rockhill, A., Larson, E., Gramfort, A.

Participants

18

Size

11.6 GB