MoBILAB: an open source toolbox for analysis and visualization of mobile brain/body imaging data

Ojeda, Alejandro; Bigdely-Shamlo, Nima; Makeig, Scott

doi:10.3389/fnhum.2014.00121

Cited by 78 publications

(73 citation statements)

References 20 publications

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“…The user can compute spatially averaged, integrated or maximal CSD for any subset of these ROIs. We make use of routines from our MoBILAB toolbox freely available online [8]. …”

Section: Methodsmentioning

confidence: 99%

Real-time modeling and 3D visualization of source dynamics and connectivity using wearable EEG

Mullen

Kothe²,

Mike

et al. 2013

2013 35th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC)

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309

242

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This report summarizes our recent efforts to deliver real-time data extraction, preprocessing, artifact rejection, source reconstruction, multivariate dynamical system analysis (including spectral Granger causality) and 3D visualization as well as classification within the open-source SIFT and BCILAB toolboxes. We report the application of such a pipeline to simulated data and real EEG data obtained from a novel wearable high-density (64-channel) dry EEG system.

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“…The user can compute spatially averaged, integrated or maximal CSD for any subset of these ROIs. We make use of routines from our MoBILAB toolbox freely available online [8]. …”

Section: Methodsmentioning

confidence: 99%

Real-time modeling and 3D visualization of source dynamics and connectivity using wearable EEG

Mullen

Kothe²,

Mike

et al. 2013

2013 35th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC)

Self Cite

309

242

View full text Add to dashboard Cite

show abstract

“…One important attempt at developing standardized frameworks for mobile technology comes from open-source initiatives designed to support the processing and analysis of mobile brain and body imaging data and facilitate their integration (e.g., MoBILAB; Ojeda et al, 2014 and Lab Streaming Layer; Kothe, 2014). While these frameworks allow for the recording and processing of multimodal data, the exact synchronization of data streams remains problematic due to current hardware limitations.…”

Section: Current Challengesmentioning

confidence: 99%

Understanding Minds in Real-World Environments: Toward a Mobile Cognition Approach

Ladouce

Donaldson

Dudchenko

et al. 2017

Front. Hum. Neurosci.

142

156

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There is a growing body of evidence that important aspects of human cognition have been marginalized, or overlooked, by traditional cognitive science. In particular, the use of laboratory-based experiments in which stimuli are artificial, and response options are fixed, inevitably results in findings that are less ecologically valid in relation to real-world behavior. In the present review we highlight the opportunities provided by a range of new mobile technologies that allow traditionally lab-bound measurements to now be collected during natural interactions with the world. We begin by outlining the theoretical support that mobile approaches receive from the development of embodied accounts of cognition, and we review the widening evidence that illustrates the importance of examining cognitive processes in their context. As we acknowledge, in practice, the development of mobile approaches brings with it fresh challenges, and will undoubtedly require innovation in paradigm design and analysis. If successful, however, the mobile cognition approach will offer novel insights in a range of areas, including understanding the cognitive processes underlying navigation through space and the role of attention during natural behavior. We argue that the development of real-world mobile cognition offers both increased ecological validity, and the opportunity to examine the interactions between perception, cognition and action—rather than examining each in isolation.

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“…2. Motion records of toddler's right hand were analyzed using EEGLAB's MoBILAB toolbox (Ojeda et al, 2014). All no-action or action-observation epochs in which hand velocity exceeded 0.45 mm/s were excluded from final analysis.…”

Section: Eliminating Movement Artifactmentioning

confidence: 99%

EEG imaging of toddlers during dyadic turn-taking: Mu-rhythm modulation while producing or observing social actions

Acar

Makeig

et al. 2015

NeuroImage

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Contemporary active-EEG and EEG-imaging methods show particular promise for studying the development of action planning and social-action representation in infancy and early childhood. Action-related mu suppression was measured in eleven 3-year-old children and their mothers during a 'live,' largely unscripted social interaction. High-density EEG was recorded from children and synchronized with motion-captured records of children's and mothers' hand actions, and with video recordings. Independent Component Analysis (ICA) was used to separate brain and non-brain source signals in toddlers' EEG records. EEG source dynamics were compared across three kinds of epochs: toddlers' own actions (execution), mothers' actions (observation), and between-turn intervals (no action). Mu (6-9 Hz) power was suppressed in left and right somatomotor cortex during both action execution and observation, as reflected by independent components of individual children's EEG data. These mu rhythm components were accompanied by beta-harmonic (~16 Hz) suppression, similar to findings from adults. The toddlers' power spectrum and scalp density projections provide converging evidence of adult-like musuppression features. Mu-suppression components' source locations were modeled using an age-specific 4-layer forward head model. Putative sources clustered around somatosensory cortex, near the hand/arm region. The results demonstrate that action-locked, event-related EEG dynamics can be measured, and sourceresolved, from toddlers during social interactions with relatively unrestricted social behaviors.

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MoBILAB: an open source toolbox for analysis and visualization of mobile brain/body imaging data

Cited by 78 publications

References 20 publications

Real-time modeling and 3D visualization of source dynamics and connectivity using wearable EEG

Real-time modeling and 3D visualization of source dynamics and connectivity using wearable EEG

Understanding Minds in Real-World Environments: Toward a Mobile Cognition Approach

EEG imaging of toddlers during dyadic turn-taking: Mu-rhythm modulation while producing or observing social actions

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