Skip to content
DraconiticResearch
Search Sources API
189,280 studies31 sourcesUpdated 7 min ago
PreprintCC0PsyArXiv

OpenDvM: Decoding the Visual Mind — A Unified Toolbox for EEG Attention Research

  • Dirk van Moorselaar
  • Sebastiaan Mathot
  • Stefan van der Stigchel
Open at sourcePDFDOICite

Abstract

Multivariate pattern analysis has become a standard complement to univariate event-related potentials (ERPs) in EEG research, yet integrated toolboxes that combine ERP, time-frequency, decoding, and forward-encoding analyses within a single, consistent framework remain largely confined to MATLAB. We introduce OpenDvM (Decoding the Visual Mind), an open-source Python toolbox built on MNE-Python that unifies these four complementary analysis types behind a shared two-step, initialize-then-execute workflow. Developed with the needs of attention researchers specifically in mind, but readily applicable to other domains, OpenDvM computes stimulus- and response-locked ERPs and time-frequency representations, characterizes when and how strongly experimental conditions are represented in multivariate EEG activity via backward decoding models (BDM), and reconstructs the continuous, graded representational geometry underlying spatial attention via forward encoding models using channel tuning functions (CTF). The toolbox standardizes data organization, batch processing, and automated quality-control reporting across all four modules, supporting reproducible analysis pipelines from raw data through group-level statistics. We demonstrate OpenDvM's functionality on an openly shared EEG dataset probing multiple facets of attentional selection, including feature-based selection, spatial attention, and distractor suppression, and provide ten accompanying Jupyter notebook tutorials covering both core and advanced functionality. By lowering the barrier to combining these complementary analytical perspectives, OpenDvM aims to make integrated univariate and multivariate EEG analysis more accessible to researchers without extensive programming backgrounds.

Related

  1. A validated antibody toolbox for ALS research
    Riham Ayoubi, Emma J MacDougall, Ian McDowell +34
    eLifePeer-reviewedCC BY 4.0Full textCell biologyNeuroscienceeLifeEurope PMC
  2. Decoding spatial attention in the cocktail party problem using whole-head high-density fNIRS
    Sudan Duwadi, De’Ja Rogers, Alex D. Boyd +12
    NeuroImagePeer-reviewedCC BYFull textNeuroscienceDOAJ
  3. Decoding motor imagery EEG signals by a deep learning network integrating multiscale filtering and temporal-channel attention mechanisms
    Yong Zhu, Yongge Shi, Jianhua Wu +1
    PLOS OnePeer-reviewedCC BYFull textNeurosciencePLOSEurope PMC
  4. Speech Restoration & Mental Surveillance as Conflicting Ethical Concerns in Neural Speech-Decoding Research.
    Narayan Sankaran, Winston Chiong
    The Hastings Center reportPeer-reviewedCC BYFull textNeuroscienceEurope PMCPMC
  5. Uneven research attention to biodiversity loss revealed by large language models
    H. Mohiuddin, K. Forster, S. Feuerriegel
    bioRxivPreprintCC BYFull textEcologybioRxiv

Cite

BibTeXRIS
BibTeX
@misc{moorselaar2026opendvm,
  title = {{OpenDvM: Decoding the Visual Mind — A Unified Toolbox for EEG Attention Research}},
  author = {Dirk van Moorselaar and Sebastiaan Mathot and Stefan van der Stigchel},
  howpublished = {PsyArXiv},
  year = {2026},
  month = sep,
  doi = {10.31234/osf.io/p6swx_v1},
  url = {https://osf.io/preprints/psyarxiv/p6swx_v1/},
  note = {CC0},
}
RIS
TY  - UNPB
TI  - OpenDvM: Decoding the Visual Mind — A Unified Toolbox for EEG Attention Research
AU  - Dirk van Moorselaar
AU  - Sebastiaan Mathot
AU  - Stefan van der Stigchel
PY  - 2026
DA  - 2026/09/10
PB  - PsyArXiv
DO  - 10.31234/osf.io/p6swx_v1
UR  - https://osf.io/preprints/psyarxiv/p6swx_v1/
AB  - Multivariate pattern analysis has become a standard complement to univariate event-related potentials (ERPs) in EEG research, yet integrated toolboxes that combine ERP, time-frequency, decoding, and forward-encoding analyses within a single, consistent framework remain largely confined to MATLAB. We introduce OpenDvM (Decoding the Visual Mind), an open-source Python toolbox built on MNE-Python that unifies these four complementary analysis types behind a shared two-step, initialize-then-execute workflow. Developed with the needs of attention researchers specifically in mind, but readily applicable to other domains, OpenDvM computes stimulus- and response-locked ERPs and time-frequency representations, characterizes when and how strongly experimental conditions are represented in multivariate EEG activity via backward decoding models (BDM), and reconstructs the continuous, graded representational geometry underlying spatial attention via forward encoding models using channel tuning functions (CTF). The toolbox standardizes data organization, batch processing, and automated quality-control reporting across all four modules, supporting reproducible analysis pipelines from raw data through group-level statistics. We demonstrate OpenDvM's functionality on an openly shared EEG dataset probing multiple facets of attentional selection, including feature-based selection, spatial attention, and distractor suppression, and provide ten accompanying Jupyter notebook tutorials covering both core and advanced functionality. By lowering the barrier to combining these complementary analytical perspectives, OpenDvM aims to make integrated univariate and multivariate EEG analysis more accessible to researchers without extensive programming backgrounds.
ER  -