Evidence map›Paper›PMID 31731489›Full record

ArticleMicromachines2019

Design and Verification of a Dry Sensor-Based Multi-Channel Digital Active Circuit for Human Brain Electroencephalography Signal Acquisition Systems.

Chin-Teng Lin, Chi-Hsien Liu, Po-Sheng Wang, Jung-Tai King, Lun-De Liao

Open access · goldAbstract read
In one paragraph

Article in Micromachines, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.

0numbers the graph read from it
0cells of the map it votes in
4citing papers in PubMed
0.6field-weighted citation impact, top 32% 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

4 citing papers in PubMed, 8 citations in OpenAlex.

  1. Review
  2. Review
  3. Article
  4. 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 at 3 institutions in 2 countries.

Chin-Teng LinCentre for AI, Faculty of Engineering and Information Technology, University of Technology Sydney, Sydney NSW 2007, Australia.ORCID 0000-0001-8371-8197
Chi-Hsien LiuBrain Research Center, National Chiao Tung University, Hsinchu 300, Taiwan.
Po-Sheng WangBrain Research Center, National Chiao Tung University, Hsinchu 300, Taiwan.
Jung-Tai KingBrain Research Center, National Chiao Tung University, Hsinchu 300, Taiwan.
Lun-De LiaoInstitute of Biomedical Engineering and Nanomedicine, National Health Research Institutes, Miaoli 35053, Taiwan.ORCID 0000-0002-2601-3673
National Yang Ming Chiao Tung University · TWNational Health Research Institutes · TWUniversity of Technology Sydney · AU

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

A brain-computer interface (BCI) is a type of interface/communication system that can help users interact with their environments. Electroencephalography (EEG) has become the most common application of BCIs and provides a way for disabled individuals to communicate. While wet sensors are the most commonly used sensors for traditional EEG measurements, they require considerable preparation time, including the time needed to prepare the skin and to use the conductive gel. Additionally, the conductive gel dries over time, leading to degraded performance. Furthermore, requiring patients to wear wet sensors to record EEG signals is considered highly inconvenient. Here, we report a wireless 8-channel digital active-circuit EEG signal acquisition system that uses dry sensors. Active-circuit systems for EEG measurement allow people to engage in daily life while using these systems, and the advantages of these systems can be further improved by utilizing dry sensors. Moreover, the use of dry sensors can help both disabled and healthy people enjoy the convenience of BCIs in daily life. To verify the reliability of the proposed system, we designed three experiments in which we evaluated eye blinking and teeth gritting, measured alpha waves, and recorded event-related potentials (ERPs) to compare our developed system with a standard Neuroscan EEG system.

Indexed as

brain–computer interface (BCI)dry sensorelectroencephalography (EEG)event-related potential (ERP)

Identifiers

PMID31731489
PMCPMC6915493
OpenAlexW2981607827

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.