Open data: Visual load effects on the auditory steady-state responses to 20-, 40-, and 80-Hz amplitude-modulated tones - Stefan Wiens; Malina Szychowska (Stockholm University, 2020)
The main results file are saved separately:

- ASSR2.html: R output of the main analyses (N = 33)
- ASSR2_subset.html: R output of the main analyses for the smaller sample (N = 25)

FIGSHARE METADATA

Categories

- Biological psychology
- Neuroscience and physiological psychology
- Sensory processes, perception, and performance

Keywords

- crossmodal attention
- electroencephalography (EEG)
- early-filter theory
- task difficulty
- envelope following response

References

- https://doi.org/10.17605/OSF.IO/6FHR8
- https://github.com/stamnosslin/mn
- https://doi.org/10.17045/sthlmuni.4981154.v3
- https://biosemi.com/
- https://www.python.org/
- https://mne.tools/stable/index.html#- https://www.r-project.org/- https://rstudio.com/products/rstudio/

GENERAL INFORMATION

1. Title of Dataset:
Open data: Visual load effects on the auditory steady-state responses to 20-, 40-, and 80-Hz amplitude-modulated tones

2. Author Information
A. Principal Investigator Contact Information
Name: Stefan Wiens
Institution: Department of Psychology, Stockholm University, Sweden
Internet: https://www.su.se/profiles/swiens-1.184142
Email: sws@psychology.su.se

B. Associate or Co-investigator Contact Information
Name: Malina Szychowska
Institution: Department of Psychology, Stockholm University, Sweden
Internet: https://www.researchgate.net/profile/Malina_Szychowska
Email: malina.szychowska@psychology.su.se

3. Date of data collection:
Subjects (N = 33) were tested between 2019-11-15 and 2020-03-12.

4. Geographic location of data collection: Department of Psychology, Stockholm, Sweden

5. Information about funding sources that supported the collection of the data:
Swedish Research Council (Vetenskapsrådet) 2015-01181

SHARING/ACCESS INFORMATION

1. Licenses/restrictions placed on the data: CC BY 4.0

2. Links to publications that cite or use the data: Szychowska M., & Wiens S. (2020). Visual load effects on the auditory steady-state responses to 20-, 40-, and 80-Hz amplitude-modulated tones. Submitted manuscript.

The study was preregistered:
https://doi.org/10.17605/OSF.IO/6FHR8

3. Links to other publicly accessible locations of the data: N/A

4. Links/relationships to ancillary data sets: N/A

5. Was data derived from another source? No

6. Recommended citation for this dataset: Wiens, S., & Szychowska M. (2020). Open data: Visual load effects on the auditory steady-state responses to 20-, 40-, and 80-Hz amplitude-modulated tones. Stockholm: Stockholm University. https://doi.org/10.17045/sthlmuni.12582002

DATA & FILE OVERVIEW

File List:
The files contain the raw data, scripts, and results of main and supplementary analyses of an electroencephalography (EEG) study. Links to the hardware and software are provided under methodological information.

ASSR2_experiment_scripts.zip: contains the Python files to run the experiment.

ASSR2_rawdata.zip: contains raw datafiles for each subject

- data_EEG: EEG data in bdf format (generated by Biosemi)
- data_log: logfiles of the EEG session (generated by Python)

ASSR2_EEG_scripts.zip: Python-MNE scripts to process the EEG data

ASSR2_EEG_preprocessed_data.zip: EEG data in fif format after preprocessing with Python-MNE scripts

ASSR2_R_scripts.zip: R scripts to analyze the data together with the main datafiles. The main files in the folder are:

- ASSR2.html: R output of the main analyses
- ASSR2_subset.html: R output of the main analyses but after excluding eight subjects who were recorded as pilots before preregistering the study

ASSR2_results.zip: contains all figures and tables that are created by Python-MNE and R.

METHODOLOGICAL INFORMATION

1. Description of methods used for collection/generation of data:
The auditory stimuli were amplitude-modulated tones with a carrier frequency (fc) of 500 Hz and modulation frequencies (fm) of 20.48 Hz, 40.96 Hz, or 81.92 Hz.
The experiment was programmed in python: https://www.python.org/ and used extra functions from here: https://github.com/stamnosslin/mn

The EEG data were recorded with an Active Two BioSemi system (BioSemi, Amsterdam, Netherlands; www.biosemi.com) and saved in .bdf format.
For more information, see linked publication.

2. Methods for processing the data:
We conducted frequency analyses and computed event-related potentials. See linked publication

3. Instrument- or software-specific information needed to interpret the data:
MNE-Python (Gramfort A., et al., 2013): https://mne.tools/stable/index.html#
Rstudio used with R (R Core Team, 2020): https://rstudio.com/products/rstudio/
Wiens, S. (2017). Aladins Bayes Factor in R (Version 3). https://www.doi.org/10.17045/sthlmuni.4981154.v3

4. Standards and calibration information, if appropriate:
For information, see linked publication.

5. Environmental/experimental conditions:
For information, see linked publication.

6. Describe any quality-assurance procedures performed on the data:
For information, see linked publication.

7. People involved with sample collection, processing, analysis and/or submission:

- Data collection: Malina Szychowska with assistance from Jenny Arctaedius.
- Data processing, analysis, and submission: Malina Szychowska and Stefan Wiens

DATA-SPECIFIC INFORMATION:
All relevant information can be found in the MNE-Python and R scripts (in EEG_scripts and analysis_scripts folders) that process the raw data. For example, we added notes to explain what different variables mean.
Keywords: 170101 Biological Psychology (Neuropsychology, Psychopharmacology, Physiological Psychology); Neuroscience and Physiological Psychology; 170112 Sensory Processes, Perception and Performance; Neurosciences; Neurovetenskaper; Biological Sciences; Biologi