Evidence map›Paper›PMID 29874804›Full record

ArticleSensors (Basel, Switzerland)2018

Improvement of Information Transfer Rates Using a Hybrid EEG-NIRS Brain-Computer Interface with a Short Trial Length: Offline and Pseudo-Online Analyses.

Jaeyoung Shin, Do-Won Kim, Klaus-Robert Müller, Han-Jeong Hwang

Open access · goldAbstract read
In one paragraph

Article in Sensors (Basel, Switzerland), 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 7 papers.

0numbers the graph read from it
0cells of the map it votes in
7citing papers in PubMed
2.3field-weighted citation impact, top 12% of its field
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

7 citing papers in PubMed, 32 citations in OpenAlex.

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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

4 authors at 4 institutions in 2 countries.

Jaeyoung ShinDepartment of Biomedical Engineering, Hanyang University, Seoul 04763, Korea. naraeshigo@gmail.com.ORCID 0000-0003-2899-6893
Do-Won KimDepartment of Biomedical Engineering, Chonnam National University, Yeosu 59626, Korea. dowon.kim@jnu.ac.kr.
Klaus-Robert MüllerMachine Learning Group, Berlin Institute of Technology (TU Berlin), 10623 Berlin, Germany. klaus-robert.mueller@tu-berlin.de.
Han-Jeong HwangDepartment of Medical IT Convergence Engineering, Kumoh National Institute of Technology, Gumi 39177, Korea. h2j@kumoh.ac.kr.ORCID 0000-0002-1183-1219
Chonnam National University · KRHanyang University · KRKorea University · KRKumoh National Institute of Technology · KR

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Electroencephalography (EEG) and near-infrared spectroscopy (NIRS) are non-invasive neuroimaging methods that record the electrical and metabolic activity of the brain, respectively. Hybrid EEG-NIRS brain-computer interfaces (hBCIs) that use complementary EEG and NIRS information to enhance BCI performance have recently emerged to overcome the limitations of existing unimodal BCIs, such as vulnerability to motion artifacts for EEG-BCI or low temporal resolution for NIRS-BCI. However, with respect to NIRS-BCI, in order to fully induce a task-related brain activation, a relatively long trial length (≥10 s) is selected owing to the inherent hemodynamic delay that lowers the information transfer rate (ITR; bits/min). To alleviate the ITR degradation, we propose a more practical hBCI operated by intuitive mental tasks, such as mental arithmetic (MA) and word chain (WC) tasks, performed within a short trial length (5 s). In addition, the suitability of the WC as a BCI task was assessed, which has so far rarely been used in the BCI field. In this experiment, EEG and NIRS data were simultaneously recorded while participants performed MA and WC tasks without preliminary training and remained relaxed (baseline; BL). Each task was performed for 5 s, which was a shorter time than previous hBCI studies. Subsequently, a classification was performed to discriminate MA-related or WC-related brain activations from BL-related activations. By using hBCI in the offline/pseudo-online analyses, average classification accuracies of 90.0 ± 7.1/85.5 ± 8.1% and 85.8 ± 8.6/79.5 ± 13.4% for MA vs. BL and WC vs. BL, respectively, were achieved. These were significantly higher than those of the unimodal EEG- or NIRS-BCI in most cases. Given the short trial length and improved classification accuracy, the average ITRs were improved by more than 96.6% for MA vs. BL and 87.1% for WC vs. BL, respectively, compared to those reported in previous studies. The suitability of implementing a more practical hBCI based on intuitive mental tasks without preliminary training and with a shorter trial length was validated when compared to previous studies.

Indexed as

Brain-Computer InterfacesElectroencephalographySpectroscopy, Near-InfraredAdultBrainDiscriminant AnalysisFemaleHumansMalePhotic StimulationSignal Processing, Computer-AssistedYoung Adultbrain-computer interfaceEEGinformation transfer rateNIRSpseudo-online

Identifiers

PMID29874804
PMCPMC6021956
OpenAlexW2806319130

What OpenQuestion holds

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LicenceCC BY
Read underepoch 390

Registered trials

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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.