Evidence map›Paper›PMID 27148018›Full record

ArticleFrontiers in human neuroscience2016

Unsupervised Decoding of Long-Term, Naturalistic Human Neural Recordings with Automated Video and Audio Annotations.

Nancy X R Wang, Jared D Olson, Jeffrey G Ojemann, Rajesh P N Rao, Bingni W Brunton

Abstract read
PubMed Full text at PMC Publisher
In one paragraph

Article in Frontiers in human neuroscience, 2016. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 14 papers.

0numbers the graph read from it
0cells of the map it votes in
14citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

14 citing papers in PubMed.

  1. Trial
  2. Article
  3. Article
  4. Article
  5. Article
  6. Article
  7. Article
  8. Multi-scale neural decoding and analysis.Journal of neural engineering · 2021
    Review
  9. Article
  10. Article
  11. Article
  12. Review
  13. Closed-loop neuromodulation systems: next-generation treatments for psychiatric illness.International review of psychiatry (Abingdon, England) · 2017
    Review
  14. Article
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

5 authors.

Nancy X R WangDepartment of Computer Science and Engineering, University of WashingtonSeattle, WA, USA; Institute for Neuroengineering, University of WashingtonSeattle, WA, USA; eScience Institute, University of WashingtonSeattle, WA, USA; Center for Sensorimotor Neural Engineering, University of WashingtonSeattle, WA, USA.
Jared D OlsonCenter for Sensorimotor Neural Engineering, University of WashingtonSeattle, WA, USA; Department of Rehabilitation Medicine, University of WashingtonSeattle, WA, USA.
Jeffrey G OjemannInstitute for Neuroengineering, University of WashingtonSeattle, WA, USA; Center for Sensorimotor Neural Engineering, University of WashingtonSeattle, WA, USA; Department of Neurological Surgery, University of WashingtonSeattle, WA, USA.
Rajesh P N RaoDepartment of Computer Science and Engineering, University of WashingtonSeattle, WA, USA; Institute for Neuroengineering, University of WashingtonSeattle, WA, USA; Center for Sensorimotor Neural Engineering, University of WashingtonSeattle, WA, USA.
Bingni W BruntonInstitute for Neuroengineering, University of WashingtonSeattle, WA, USA; eScience Institute, University of WashingtonSeattle, WA, USA; Department of Biology, University of WashingtonSeattle, WA, USA.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Fully automated decoding of human activities and intentions from direct neural recordings is a tantalizing challenge in brain-computer interfacing. Implementing Brain Computer Interfaces (BCIs) outside carefully controlled experiments in laboratory settings requires adaptive and scalable strategies with minimal supervision. Here we describe an unsupervised approach to decoding neural states from naturalistic human brain recordings. We analyzed continuous, long-term electrocorticography (ECoG) data recorded over many days from the brain of subjects in a hospital room, with simultaneous audio and video recordings. We discovered coherent clusters in high-dimensional ECoG recordings using hierarchical clustering and automatically annotated them using speech and movement labels extracted from audio and video. To our knowledge, this represents the first time techniques from computer vision and speech processing have been used for natural ECoG decoding. Interpretable behaviors were decoded from ECoG data, including moving, speaking and resting; the results were assessed by comparison with manual annotation. Discovered clusters were projected back onto the brain revealing features consistent with known functional areas, opening the door to automated functional brain mapping in natural settings.

Indexed as

automationcomputer visionelectrocorticography (ECoG)functional brain mappinglong-term recordingneural decodingspeech processingunsupervised machine learning

Identifiers

PMID27148018
PMCPMC4838634

What OpenQuestion holds

Textmetadata
LicenceCC BY
Read underepoch 390

Registered trials

None linked

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.