MNE-BIDS: Organizing electrophysiological data into the BIDS format and facilitating their analysis

Appelhoff, Stefan; Sanderson, Matthew R.; Brooks, Teon L; Vliet, Marijn van; Quentin, Romain; Holdgraf, Chris; Chaumon, Maximilien; Mikulan, Ezequiel; Tavabi, Kambiz; Höchenberger, Richard; Welke, Dominik; Brunner, Clemens; Rockhill, Alexander P; Larson, Eric; Gramfort, Alexandre; Jas, Mainak

doi:10.21105/joss.01896

Cited by 76 publications

(54 citation statements)

References 6 publications

(6 reference statements)

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The epochs were downsampled to 250 Hz and baseline corrected relative to the period from −0.2 to 0 s before stimulus onset. All EEG analyses were performed in Python using MNE-Python (Gramfort et al 2013), MNE-BIDS (Appelhoff et al 2019), and custom code. All analysis code is available at https://github.com/sappelhoff/sp_code.…”

Section: Methodsmentioning

confidence: 99%

Control over sampling boosts numerical evidence processing in human decisions from experience

Appelhoff

Hertwig

Spitzer

2021

Preprint

Self Cite

View full text Add to dashboard Cite

When acquiring information about choice alternatives, decision makers may have varying levels of control over which and how much information they sample before making a choice. How does control over sampling affect the quality of experience-based decisions? Here, combining variants of a numerical sampling task with neural recordings, we show that control over when to stop sampling can enhance (i) behavioral choice accuracy, (ii) the build-up of parietal decision signals, and (iii) the encoding of numerical sample information in multivariate electroencephalogram (EEG) patterns. None of these effects were observed when participants could only control which alternatives to sample, but not when to stop sampling. Furthermore, levels of control had no effect on early sensory signals or on the extent to which sample information leaked from memory. The results indicate that freedom to stop sampling can amplify decisional evidence processing from the outset of information acquisition and lead to more accurate choices.

show abstract

Section: Methodsmentioning

confidence: 99%

Control over sampling boosts numerical evidence processing in human decisions from experience

Appelhoff

Hertwig

Spitzer

2021

Preprint

Self Cite

View full text Add to dashboard Cite

show abstract

“…Therefore, the preprocessing pipeline used in the present study was optimized for real-time performance and inspired by the Berlin Brain Computer Interface (Blankertz et al, 2006). Processing was performed in Python using custom code based on MNE-python (Gramfort et al, 2013), mne_bids (Appelhoff et al, 2019), fooof (Haller et al, 2018) and pybv (https://pybv.readthedocs.io/en/stable/). All raw data files were saved in the iEEG-BIDS structure (Holdgraf et al, 2019).…”

Section: Ecog and Lfp Preprocessing And Feature Extractionmentioning

confidence: 99%

Electrocorticography is superior to subthalamic local field potentials for movement decoding in Parkinson’s disease

Merk

Peterson

Lipski

et al. 2021

Preprint

View full text Add to dashboard Cite

SummarySmart brain implants will revolutionize neurotechnology for improving the quality of life in patients with brain disorders. The treatment of Parkinson’s disease (PD) with neural implants for deep brain stimulation (DBS) presents an avenue for developing machine-learning based individualized treatments to refine human motor control. We developed an optimized movement decoding approach to predict grip-force based on sensorimotor electrocorticography (ECoG) and subthalamic local field potentials in PD patients undergoing DBS neurosurgery. ECoG combined with Bayesian optimized extreme gradient boosted decision trees outperformed multiple state of the art machine learning approaches. We further developed a whole brain connectomics approach to predict decoding performance in invasive neurophysiology, relevant for connectomic targeting of distributed brain networks for neural decoding. PD motor impairment deteriorated decoding performance, suggestive of a role for dopamine in human movement coding capacity. Our study provides an advanced neurophysiological and computational framework to aid development of intelligent adaptive DBS.

show abstract

“…Furthermore, it implements a comprehensive list of methods for source imaging ranging from dipole fitting, linear methods (dSPM, sLORETA), beamformers, (RAP-)MUSIC and more recent nonlinear approaches. GSP is enforced in this environment in particular through the object-oriented interface that ensures that specific processes can only be applied to designated appropriate data objects, as well as with the development of child projects such as MNE-BIDS (Appelhoff et al, 2019), to facilitate BIDS integration. The rich and open-source Python ecosystem and the strong bonds of the original developers of the Freesurfer (Dale & Sereno, 1993;Dale et al, 1999;Fischl, 2012) MRI segmentation toolbox make it easy to interface with those widely used tools.…”

Section: Box 2: a Brief Historical Perspective On Gsp Of Meeg Analysis Softwarementioning

confidence: 99%

Good Scientific Practice in MEEG Research: Progress and Perspectives

Niso¹,

Krol²,

Combrisson³

et al. 2021

Preprint

Self Cite

View full text Add to dashboard Cite

Good Scientific Practice (GSP) refers to both explicit and implicit rules or guidelines that help scientists to produce work that is of the highest quality at any given time, and to efficiently share that work with the community for further scrutiny or utilization. For experimental research using magneto- and electroencephalography (MEEG), GSP includes specific standards and guidelines for technical competence, which are periodically updated whenever new findings come to light. However, GSP also needs to be periodically revisited in a broader light. At the LiveMEEG 2020 conference, a reflection on GSP was fostered that included explicitly documented guidelines and technical advances, but also emphasised intangible GSP: a general awareness of personal, organisational, and societal realities and how they can influence MEEG research. This article provides an extensive report on most of the LiveMEEG contributions and new literature, with the additional aim to synthesize ongoing cultural changes in GSP. It first covers GSP with respect to cognitive biases and logical fallacies, pre-registration as a tool to avoid those and other early pitfalls, and a number of resources to enable collaborative and reproducible research as a general approach to minimize misconceptions. Second, GSP with respect to data acquisition, analysis, reporting, and sharing is discussed, including new tools and frameworks to support collaborative work. Finally, GSP is considered in light of ethical implications of MEEG research and the resulting responsibility that scientists have to engage with societal challenges. Considering among other things the benefits of peer review and open access at all stages, the need to coordinate larger international projects, the complexity of MEEG subject matter, and today's prioritization of fairness, privacy, and the environment, we find that current GSP tends to favour collective and cooperative work, for both scientific and for societal reasons.

show abstract

MNE-BIDS: Organizing electrophysiological data into the BIDS format and facilitating their analysis

Cited by 76 publications

References 6 publications

Control over sampling boosts numerical evidence processing in human decisions from experience

Control over sampling boosts numerical evidence processing in human decisions from experience

Electrocorticography is superior to subthalamic local field potentials for movement decoding in Parkinson’s disease

Good Scientific Practice in MEEG Research: Progress and Perspectives

Contact Info

Product

Resources

About