AJILE12: Long-term naturalistic human intracranial neural recordings and pose

Peterson, Steven M.; Singh, Satpreet H.; Dichter, Benjamin; Scheid, Michael R.; Rao, Rajesh P. N.; Brunton, Bingni W.

doi:10.1101/2021.07.26.453884

Cited by 4 publications

(2 citation statements)

References 59 publications

(67 reference statements)

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“…Visualizations of multiple streams of data is a common need across different types of neurophysiology data. Another NWBWidgets dashboard is described in Peterson et al, 2021 , which demonstrates a dashboard for viewing human body position tracking with simultaneously acquired ECoG data, as well as a panel for viewing the 3D position of electrodes on the participant’s brain. Dashboards for specific experiment types can be constructed using NWBWidgets, a library for interactive web-based visualizations of NWB data, that provides tools for navigating and combining data across modalities.…”

Section: Resultsmentioning

confidence: 99%

The Neurodata Without Borders ecosystem for neurophysiological data science

Rübel

Tritt

et al. 2022

eLife

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The neurophysiology of cells and tissues are monitored electrophysiologically and optically in diverse experiments and species, ranging from flies to humans. Understanding the brain requires integration of data across this diversity, and thus these data must be findable, accessible, interoperable, and reusable (FAIR). This requires a standard language for data and metadata that can coevolve with neuroscience. We describe design and implementation principles for a language for neurophysiology data. Our open-source software (Neurodata Without Borders, NWB) defines and modularizes the interdependent, yet separable, components of a data language. We demonstrate NWB’s impact through unified description of neurophysiology data across diverse modalities and species. NWB exists in an ecosystem, which includes data management, analysis, visualization, and archive tools. Thus, the NWB data language enables reproduction, interchange, and reuse of diverse neurophysiology data. More broadly, the design principles of NWB are generally applicable to enhance discovery across biology through data FAIRness.

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Section: Resultsmentioning

confidence: 99%

The Neurodata Without Borders ecosystem for neurophysiological data science

Rübel

Tritt

et al. 2022

eLife

View full text Add to dashboard Cite

show abstract

“…Visualizations of multiple streams of data is a common need across different types of neurophysiology data. Another NWBWidgets dashboard is described in 16 , which demonstrates a dashboard for viewing human body position tracking with simultaneously acquired ECoG data, as well as a panel for viewing the 3D position of electrodes on the participant’s brain. Dashboards for specific experiment types can be constructed using NWBWidgets, a library for interactive web-based visualizations of NWB data, that provides tools for navigating and combining data across modalities.…”

Section: Resultsmentioning

confidence: 99%

The Neurodata Without Borders ecosystem for neurophysiological data science

Rübel

Tritt

et al. 2021

Preprint

Self Cite

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The neurophysiology of cells and tissues are monitored electrophysiologically and optically in diverse experiments and species, ranging from flies to humans. Understanding the brain requires integration of data across this diversity, and thus these data must be findable, accessible, interoperable, and reusable (FAIR). This requires a standard language for data and metadata that can coevolve with neuroscience. We describe design and implementation principles for a language for neurophysiology data. Our software (Neurodata Without Borders, NWB) defines and modularizes the interdependent, yet separable, components of a data language. We demonstrate NWB's impact through unified description of neurophysiology data across diverse modalities and species. NWB exists in an ecosystem which includes data management, analysis, visualization, and archive tools. Thus, the NWB data language enables reproduction, interchange, and reuse of diverse neurophysiology data. More broadly, the design principles of NWB are generally applicable to enhance discovery across biology through data FAIRness.

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Learning neural decoders without labels using multiple data streams

Peterson

Rao

Brunton

2021

Preprint

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Recent advances in neural decoding have accelerated the development of brain-computer interfaces aimed at assisting users with everyday tasks such as speaking, walking, and manipulating objects. However, current approaches for training neural decoders commonly require large quantities of labeled data, which can be laborious or infeasible to obtain in real-world settings. One intriguing alternative uses self-supervised models that share self-generated pseudo-labels between two data streams; such models have shown exceptional performance on unlabeled audio and video data, but it remains unclear how well they extend to neural decoding. Here, we learn neural decoders without labels by leveraging multiple simultaneously recorded data streams, including neural, kinematic, and physiological signals. Specifically, we apply cross-modal, self-supervised deep clustering to decode movements from brain recordings; these decoders are compared to supervised and unimodal, self-supervised models. We find that sharing pseudo-labels between two data streams during training substantially increases decoding performance compared to unimodal, self-supervised models, with accuracies approaching those of supervised decoders trained on labeled data. Next, we develop decoders trained on three modalities that match or slightly exceed the performance of supervised models, achieving state-of-the-art neural decoding accuracy. Cross-modal decoding is a flexible, promising approach for robust, adaptive neural decoding in real-world applications without any labels.

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AJILE12: Long-term naturalistic human intracranial neural recordings and pose

Cited by 4 publications

References 59 publications

The Neurodata Without Borders ecosystem for neurophysiological data science

The Neurodata Without Borders ecosystem for neurophysiological data science

The Neurodata Without Borders ecosystem for neurophysiological data science

Learning neural decoders without labels using multiple data streams

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