Evidence map›Paper›PMID 34064847›Full record

ArticleBrain sciences2021

Design and Implementation of an EEG-Based Learning-Style Recognition Mechanism.

Bingxue Zhang, Chengliang Chai, Zhong Yin, Yang Shi

Abstract read
PubMed Full text at PMC Publisher
In one paragraph

Article in Brain sciences, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.

0numbers the graph read from it
0cells of the map it votes in
3citing 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

3 citing papers in PubMed.

  1. Article
  2. Article
  3. 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

4 authors.

Bingxue ZhangDepartment of Optical-Electrical & Computer Engineering, University of Shanghai for Science and Technology, Shanghai 200093, China.
Chengliang ChaiDepartment of Optical-Electrical & Computer Engineering, University of Shanghai for Science and Technology, Shanghai 200093, China.
Zhong YinDepartment of Optical-Electrical & Computer Engineering, University of Shanghai for Science and Technology, Shanghai 200093, China.
Yang ShiDepartment of Optical-Electrical & Computer Engineering, University of Shanghai for Science and Technology, Shanghai 200093, China.

Funding

National Natural Science Foundation of China 62007024Shanghai Sailing Program 17YF1428400
6 · The paper itself

Abstract

Existing methods for learning-style recognition are highly subjective and difficult to implement. Therefore, the present study aimed to develop a learning-style recognition mechanism based on EEG features. The process for the mechanism included labeling learners' actual learning styles, designing a method to effectively stimulate different learners' internal state differences regarding learning styles, designing the data-collection method, designing the preprocessing procedure, and constructing the recognition model. In this way, we designed and verified an experimental method that can effectively stimulate learning-style differences in the information-processing dimension. In addition, we verified the effectiveness of using EEG signals to recognize learning style. The recognition accuracy of the learning-style processing dimension was 71.2%. This result is highly significant for the further exploration of using EEG signals for effective learning-style recognition.

Indexed as

brain-computer interfaceEEG featuresFelder–Silverman learning-stylelearning-style recognitionprocessing dimension

Identifiers

PMID34064847
PMCPMC8150355

What OpenQuestion holds

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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.