Evidence map›Paper›PMID 36275950›Full record

ArticleComputational intelligence and neuroscience2022

Entropy-Based Emotion Recognition from Multichannel EEG Signals Using Artificial Neural Network.

Si Thu Aung, Mehedi Hassan, Mark Brady, Zubaer Ibna Mannan, Sami Azam, Asif Karim, Sadika Zaman, Yodchanan Wongsawat

Abstract read
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In one paragraph

Article in Computational intelligence and neuroscience, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.

0numbers the graph read from it
0cells of the map it votes in
2citing 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

2 citing papers in PubMed.

  1. Article
  2. Retrieval-Based Factorization Machines for Human Click Behavior Prediction.Computational intelligence and neuroscience · 2022
    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.

Si Thu AungDepartment of Biomedical Engineering, Faculty of Engineering, Mahidol University, Salaya, Thailand.
Mehedi HassanComputer Science and Engineering, North Western University, Khulna, Bangladesh.ORCID https://orcid.org/0000-0002-9890-0968
Mark BradyAsia Pacific College of Business and Law, Charles Darwin University, Casuarina, NT, Australia.
Zubaer Ibna MannanDepartment of Smart Computing, Kyungdong University, Global Campus, Goseong-Gun, Republic of Korea.ORCID https://orcid.org/0000-0002-5336-5252
Sami AzamCollege of Engineering IT and Environment, Charles Darwin University, Casuarina, NT, Australia.ORCID https://orcid.org/0000-0001-7572-9750
Asif KarimCollege of Engineering IT and Environment, Charles Darwin University, Casuarina, NT, Australia.ORCID https://orcid.org/0000-0001-8532-6816
Sadika ZamanComputer Science and Engineering, North Western University, Khulna, Bangladesh.
Yodchanan WongsawatDepartment of Biomedical Engineering, Faculty of Engineering, Mahidol University, Salaya, Thailand.ORCID https://orcid.org/0000-0002-6541-0305

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Humans experience a variety of emotions throughout the course of their daily lives, including happiness, sadness, and rage. As a result, an effective emotion identification system is essential for electroencephalography (EEG) data to accurately reflect emotion in real-time. Although recent studies on this problem can provide acceptable performance measures, it is still not adequate for the implementation of a complete emotion recognition system. In this research work, we propose a new approach for an emotion recognition system, using multichannel EEG calculation with our developed entropy known as

Indexed as

ElectroencephalographyNeural Networks, ComputerEmotionsEntropyHumansReceptor for Advanced Glycation End ProductsReceptor for Advanced Glycation End Products

Identifiers

PMID36275950
PMCPMC9584707

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LicenceCC BY
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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.