From zero to lab: Guidelines and practical implementation for building a multimodal experimental psychology and cognitive neuroscience laboratory.

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Paper

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The authors' code

Python · 55 lines · 1.8 KB · no license

import os
import xml.etree.ElementTree as ET
import matplotlib.pyplot as plt
# Lista de archivos dentro de la carpeta 'Files'
folder_path = "Files"
file_paths = [
os.path.join(folder_path, "PUNTO2 BFF1E.mdr"),
os.path.join(folder_path, "PUNTO2 BFF2E.mdr"),
os.path.join(folder_path, "PUNTO2 BFF3E.mdr"),
os.path.join(folder_path, "PUNTO2 BFF4E.mdr"),
os.path.join(folder_path, "PUNTO2 BFF5E.mdr"),
os.path.join(folder_path, "PUNTO2 BFF6E.mdr"),
os.path.join(folder_path, "PUNTO2 BFF7E.mdr"),
os.path.join(folder_path, "PUNTO2 BFF8E.mdr")
]
# Crear la figura
plt.figure(figsize=(10, 5))
# Recorrer todos los archivos y extraer los datos
for file_path in file_paths:
if os.path.exists(file_path):
# Parsear el archivo XML
tree = ET.parse(file_path)
root = tree.getroot()
# Extraer datos del nodo Sweep
sweep_data = root.find(".//Sweep")
if sweep_data is not None:
# Extraer frecuencias y valores
freq_text = sweep_data.find("Frequencies").text.strip()
values_text = sweep_data.find("Values").text.strip()
# Convertir a listas numéricas
frequencies = [float(f) for f in freq_text.split(";") if f]
values = [float(v) for v in values_text.split(";") if v]
# Convertir frecuencias a kHz
frequencies_kHz = [f / 1000 for f in frequencies]
# Graficar
plt.plot(frequencies_kHz, values, linestyle="-", color="black")
# Etiquetas de los ejes
# plt.grid(which='both', linestyle='--', linewidth=0.1)
plt.xlabel("Frequency [in kHz]", fontsize=12)
plt.ylabel("E [in V/m]", fontsize=12)
# Grid
#plt.grid()
# Mostrar la gráfica
plt.savefig("point2.png") # Saves the plot locally
plt.show()

figureS1.py, no license · at the source

Overview

Authors: David del Rosario-Gilabert¹, A García-Miquel¹, I Vigué-Guix¹

ORCID iDs: David del Rosario-Gilabert

Instituto de Neurociencia Avanzada de Barcelona (INAB), Passeig del Mare Nostrum, 15, 08039 Barcelona, Spain

Institutions: Instituto de Neurociencia Avanzada de Barcelona (Spain)

Journal: Behavior research methods, volume 58, issue 4, article 98

Dates: received 8 August 2025; accepted 24 February 2026; published online 30 March 2026; in print 2026

Type: Research article · Language: English

License: CC BY

Identifiers: DOI 10.3758/s13428-026-02988-0 · PMID 41912972 · PMCID PMC13035650 · OpenAlex W7143298774

Open access: hybrid, a free copy (OpenAlex)

Status: code verified (how the code is found and verified)

Categories: EEG (modality), human (organism), cognitive (subfield)

Methods: Evoked potentials, Graphs, Complexity, fMRI & imaging, Physiology & signal measures, Smoothing, state filtering, decompositions, Machine learning

Keywords: Cognitive neuroscience, Neuroimaging, Multimodal integration, Laboratory design, Experimental reproducibility

MeSH: Cognitive Neuroscience*, Laboratories*, Electroencephalography, Eye-Tracking Technology, Humans, Reproducibility of Results, Research Design, Spectroscopy, Near-Infrared (* major topic)

Topic: EEG and Brain-Computer Interfaces (Cognitive Neuroscience, Neuroscience), according to OpenAlex

Citations: not cited yet (Europe PMC); 81 references in the paper

Abstract

As multimodal approaches become increasingly common in cognitive neuroscience, there is a growing need for clear guidelines for designing and building laboratories. Here, we present a structured framework for the development of a multimodal neuroscience laboratory, covering key aspects such as space selection, acoustic treatment, electromagnetic shielding, environmental control, data synchronization, and data management. We also describe its practical implementation at the Instituto de Neurociencia Avanzada de Barcelona (INAB), where the facility was configured to support both individual and multi-participant studies using electroencephalography (EEG), functional near-infrared spectroscopy (fNIRS), eye-tracking, and peripheral biosensors. The laboratory was designed to preserve signal quality, minimize artifacts, and promote experimental reproducibility through controlled environmental conditions and rigorous synchronization procedures. Validation tests confirmed that the recording space achieved reverberation times below 0.6 s and reduced electromagnetic interference (EMI) by more than 30 × . Taken together, these technical and practical considerations provide a replicable model that other research groups can adopt to improve standardization and data reliability in multimodal neuroscience settings.

Supplementary Information: The online version contains supplementary material available at 10.3758/s13428-026-02988-0.

Reproduced under the paper's license (CC BY), from the paper cited above.

Repository

Its files are read in the Code ↔ Paper reader above.

OSF vcs8u

License: none: the authors keep all their rights

State: the link answers, verified on 30 September 2026

Evidence: files inventoried

Languages: Python (2)

Size: 14 files, 2 scripts

Software Heritage: not checked

Found in: “Code availability”

Not found: README, license file, CITATION.cff, environment file, tests, continuous integration, documentation

Tools: Matplotlib (2 files)

Availability: 1 check, the latest on 30 September 2026: the link answers (HTTP 200)

30 September 2026: the link answers (HTTP 200)

2 files

2d93s/Aaronia - Before lab construction/figureS1.py, Python, 55 lines
r3q92/figure3_RT.py, Python, 80 lines

At the source: osf.io/vcs8u/

Code availability

The Python scripts used in this study for data processing and figure generation in the acoustic treatment and electromagnetic interference (EMI) analyses are available via the same OSF repository (https://osf.io/vcs8u/).

Reproduced under the paper's license (CC BY), from the paper cited above.

Tracing map

Proposed by the machine: these links were found in the paper and verified at the source, without human review. The map will receive a Zenodo DOI once one of the paper's authors has validated it with their ORCID.

What the map holds:

1 repository of the authors' code, each at its verified commit, with its license and how the link was found in the paper;
2 scripts, each with its path and the digest of its content;
no match between paragraphs and code yet;
neither the text of the paper nor the code itself.

Its JSON (tracing-map.json) is deposited on Zenodo with its DOI once the map is validated.

Data

No dataset and no data link were found in the paper.

Data Availability Statement

All data and materials supporting this study are available on the Open Science Framework (OSF) at https://osf.io/vcs8u/. The repository includes datasets and scripts related to the laboratory’s acoustic treatment measurements, electromagnetic interference (EMI) analyses, and lighting system simulations and measurements, as well as documentation describing the procedures and results.

Reproduced under the paper's license (CC BY), from the paper cited above.

Versions

The history of this record: each version stored by the harvester or made by a correction of its authors or of the maintainers of its code, and what changed in its facts. The texts of the paper (its abstract, its availability statements) are not part of it; versions that changed only those are not listed.

Version 1, 30 September 2026: the first record

Recorded: type, language, journal, volume, issue, pages, dates, 3 authors, 5 keywords, 8 MeSH terms, 69 references.

Cite

This paper

del Rosario-Gilabert, D., García-Miquel, A., & Vigué-Guix, I. (2026). From zero to lab: Guidelines and practical implementation for building a multimodal experimental psychology and cognitive neuroscience laboratory. Behavior research methods, 58(4), 98. https://doi.org/10.3758/s13428-026-02988-0

BibTeX

@article{delrosariogilabert2026zero, author = {del Rosario-Gilabert, David and García-Miquel, A and Vigué-Guix, I}, title = {{From zero to lab: Guidelines and practical implementation for building a multimodal experimental psychology and cognitive neuroscience laboratory}}, journal = {Behavior research methods}, year = {2026}, month = mar, volume = {58}, number = {4}, pages = {98}, publisher = {Springer Science+Business Media}, issn = {1554-351X}, doi = {10.3758/s13428-026-02988-0}, url = {https://doi.org/10.3758/s13428-026-02988-0}, pmid = {41912972}, pmcid = {PMC13035650} }

RIS

TY - JOUR AU - del Rosario-Gilabert, David AU - García-Miquel, A AU - Vigué-Guix, I TI - From zero to lab: Guidelines and practical implementation for building a multimodal experimental psychology and cognitive neuroscience laboratory T2 - Behavior research methods J2 - Behav Res Methods PY - 2026 DA - 2026/03/30 VL - 58 IS - 4 SP - 98 SN - 1554-351X PB - Springer Science+Business Media DO - 10.3758/s13428-026-02988-0 UR - https://doi.org/10.3758/s13428-026-02988-0 LA - en ER -

CSL-JSON

{ "id": "10.3758/s13428-026-02988-0", "type": "article-journal", "title": "From zero to lab: Guidelines and practical implementation for building a multimodal experimental psychology and cognitive neuroscience laboratory", "container-title": "Behavior research methods", "author": [ { "family": "del Rosario-Gilabert", "given": "David" }, { "family": "García-Miquel", "given": "A" }, { "family": "Vigué-Guix", "given": "I" } ], "container-title-short": "Behav Res Methods", "volume": "58", "issue": "4", "page": "98", "DOI": "10.3758/s13428-026-02988-0", "PMID": "41912972", "PMCID": "PMC13035650", "ISSN": "1554-351X", "publisher": "Springer Science+Business Media", "URL": "https://doi.org/10.3758/s13428-026-02988-0", "language": "en", "issued": { "date-parts": [ [ 2026, 3, 30 ] ] } }

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From zero to lab: Guidelines and practical implementation for building a multimodal experimental psychology and cognitive neuroscience laboratory.

Code ↔ Paper

Paper

The authors' code

Overview

Abstract

Repository

OSF vcs8u

Code availability

Tracing map

Data

Data Availability Statement

Versions

Version 1, 30 September 2026: the first record

Cite

This paper

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