Evidence map›Paper›PMID 29681792›Full record

ArticleFrontiers in neuroscience2018

A Study of the Effects of Electrode Number and Decoding Algorithm on Online EEG-Based BCI Behavioral Performance.

Jianjun Meng, Bradley J Edelman, Jaron Olsoe, Gabriel Jacobs, Shuying Zhang, Angeliki Beyko, Bin He

Abstract read
In one paragraph

Article in Frontiers in neuroscience, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 11 papers.

0numbers the graph read from it
0cells of the map it votes in
11citing 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

11 citing papers in PubMed.

  1. Article
  2. Article
  3. Pediatric Spinal Cord Injury: A Review.Children (Basel, Switzerland) · 2023
    Review
  4. Applying correlation analysis to electrode optimization in source domain.Medical & biological engineering & computing · 2023
    Article
  5. Article
  6. Article
  7. Article
  8. [Recognition method of single trial motor imagery electroencephalogram signal based on sparse common spatial pattern and Fisher discriminant analysis].Sheng wu yi xue gong cheng xue za zhi = Journal of biomedical engineering = Shengwu yixue gongchengxue zazhi · 2019
    Article
  9. Article
  10. Article
  11. 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

7 authors.

Jianjun MengDepartment of Biomedical Engineering, Carnegie Mellon University, Pittsburgh, PA, United States.
Bradley J EdelmanDepartment of Biomedical Engineering, University of Minnesota, Minneapolis, MN, United States.
Jaron OlsoeDepartment of Biomedical Engineering, University of Minnesota, Minneapolis, MN, United States.
Gabriel JacobsDepartment of Biomedical Engineering, University of Minnesota, Minneapolis, MN, United States.
Shuying ZhangDepartment of Biomedical Engineering, University of Minnesota, Minneapolis, MN, United States.
Angeliki BeykoInstitute for Engineering in Medicine, University of Minnesota, Minneapolis, MN, United States.
Bin HeDepartment of Biomedical Engineering, Carnegie Mellon University, Pittsburgh, PA, United States.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Motor imagery-based brain-computer interface (BCI) using electroencephalography (EEG) has demonstrated promising applications by directly decoding users' movement related mental intention. The selection of control signals, e.g., the channel configuration and decoding algorithm, plays a vital role in the online performance and progressing of BCI control. While several offline analyses report the effect of these factors on BCI accuracy for a single session-performance increases asymptotically by increasing the number of channels, saturates, and then decreases-no online study, to the best of our knowledge, has yet been performed to compare for a single session or across training. The purpose of the current study is to assess, in a group of forty-five subjects, the effect of channel number and decoding method on the progression of BCI performance across multiple training sessions and the corresponding neurophysiological changes. The 45 subjects were divided into three groups using Laplacian Filtering (LAP/S) with nine channels, Common Spatial Pattern (CSP/L) with 40 channels and CSP (CSP/S) with nine channels for online decoding. At the first training session, subjects using CSP/L displayed no significant difference compared to CSP/S but a higher average BCI performance over those using LAP/S. Despite the average performance when using the LAP/S method was initially lower, but LAP/S displayed improvement over first three sessions, whereas the other two groups did not. Additionally, analysis of the recorded EEG during BCI control indicates that the LAP/S produces control signals that are more strongly correlated with the target location and a higher R-square value was shown at the fifth session. In the present study, we found that subjects' average online BCI performance using a large EEG montage does not show significantly better performance after the first session than a smaller montage comprised of a common subset of these electrodes. The LAP/S method with a small EEG montage allowed the subjects to improve their skills across sessions, but no improvement was shown for the CSP method.

Indexed as

BCIchannel configurationCSPEEGelectrode number

Identifiers

PMID29681792
PMCPMC5897442

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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.