Evidence map›Paper›PMID 37457014›Full record

ArticleFrontiers in neuroscience2023

Design and implementation of high sampling rate and multichannel wireless recorder for EEG monitoring and SSVEP response detection.

Ruikai Li, Yixing Zhang, Guangwei Fan, Ziteng Li, Jun Li, Shiyong Fan, Cunguang Lou, Xiuling Liu

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In one paragraph

Article in Frontiers in neuroscience, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.

0numbers the graph read from it
0cells of the map it votes in
1citing 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

1 citing paper in PubMed.

  1. 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

8 authors.

Ruikai Li *The College of Electronic Information Engineering and the Hebei Key Laboratory of Digital Medical Engineering, Hebei University, Baoding, China.
Yixing Zhang *The College of Electronic Information Engineering and the Hebei Key Laboratory of Digital Medical Engineering, Hebei University, Baoding, China.
Guangwei FanThe College of Electronic Information Engineering and the Hebei Key Laboratory of Digital Medical Engineering, Hebei University, Baoding, China.
Ziteng LiThe College of Electronic Information Engineering and the Hebei Key Laboratory of Digital Medical Engineering, Hebei University, Baoding, China.
Jun LiThe College of Electronic Information Engineering and the Hebei Key Laboratory of Digital Medical Engineering, Hebei University, Baoding, China.
Shiyong FanThe College of Electronic Information Engineering and the Hebei Key Laboratory of Digital Medical Engineering, Hebei University, Baoding, China.
Cunguang LouThe College of Electronic Information Engineering and the Hebei Key Laboratory of Digital Medical Engineering, Hebei University, Baoding, China.
Xiuling LiuThe College of Electronic Information Engineering and the Hebei Key Laboratory of Digital Medical Engineering, Hebei University, Baoding, China.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Introduction: The collection and process of human brain activity signals play an essential role in developing brain-computer interface (BCI) systems. A portable electroencephalogram (EEG) device has become an important tool for monitoring brain activity and diagnosing mental diseases. However, the miniaturization, portability, and scalability of EEG recorder are the current bottleneck in the research and application of BCI. Methods: For scalp EEG and other applications, the current study designs a 32-channel EEG recorder with a sampling rate up to 30 kHz and 16-bit accuracy, which can meet both the demands of scalp and intracranial EEG signal recording. A fully integrated electrophysiology microchip RHS2116 controlled by FPGA is employed to build the EEG recorder, and the design meets the requirements of high sampling rate, high transmission rate and channel extensive. Results: The experimental results show that the developed EEG recorder provides a maximum 30 kHz sampling rate and 58 Mbps wireless transmission rate. The electrophysiological experiments were performed on scalp and intracranial EEG collection. An inflatable helmet with adjustable contact impedance was designed, and the pressurization can improve the SNR by approximately 4 times, the average accuracy of steady-state visual evoked potential (SSVEP) was 93.12%. Animal experiments were also performed on rats, and spike activity was captured successfully. Conclusion: The designed multichannel wireless EEG collection system is simple and comfort, the helmet-EEG recorder can capture the bioelectric signals without noticeable interference, and it has high measurement performance and great potential for practical application in BCI systems.

Indexed as

BCIEEGelectroencephalographyinflatable helmetRHS2116SSVEP

Identifiers

PMID37457014
PMCPMC10339741

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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.