Deep learning for electroencephalography emotion recognition.

Hesamoddin Pourrostami, Mohammad M AlyanNezhadi, Mousa Nazari, Shahab S Band, Amir Mosavi

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Article in AIMS public health, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

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0citing papers in PubMed

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1 · What the graph read from it

What it found

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2 · The registry

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4 · The record

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5 · Who and what money

Authors and funding

5 authors.

Hesamoddin PourrostamiDepartment of Computer Science, University of Science and Technology of Mazandaran, Behshahr, Iran.

Mohammad M AlyanNezhadiDepartment of Computer Science, University of Science and Technology of Mazandaran, Behshahr, Iran.

Mousa NazariDepartment of Computer Science, University of Science and Technology of Mazandaran, Behshahr, Iran.

Shahab S BandDepartment of Information Management, International Graduate School of Artificial Intelligence, National Yunlin University of Science and Technology, Douliu, Taiwan.

Amir MosaviJohn Von Neumann Faculty of Informatics, Obuda University, Budapest, Hungary.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

This study presents an Electroencephalography (EEG) emotion recognition using a long short-term memory (LSTM)-based method. Our proposed method selects window sizes and overlaps to divide the EEG data into segments, which optimally captures subtle signal changes. A Bidirectional LSTM (BiLSTM) layer is added to standard LSTM layers to better detect forward and backward patterns in the data. By using this dual-layer setup, we aim to improve both the feature extraction and the classification accuracy. The model was tested on the Database for Emotion Analysis using Physiological signals (DEAP) dataset and showed acceptable accuracy across emotional dimensions: arousal (94.0%), liking (98.9%), dominance (95.3%), and valence (99.6%). Our results suggest that the model better supports emotion recognition and has potential for mental health monitoring and adaptive therapy.

Indexed as

applied AIartificial intelligencebig datadata scienceelectroencephalographyemotion recognitionhuman-computer interactionmachine learning

Identifiers

PMID41127437

DOI10.3934/publichealth.2025041

PMCPMC12538247

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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.