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Electroencephalography-Based Emotion Recognition Using Auditory Stimulation for Affective Brain-Computer Interfaces.

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Title: Electroencephalography-Based Emotion Recognition Using Auditory Stimulation for Affective Brain-Computer Interfaces.
Authors: Bouyam C; School of Informatics, Walailak University, Nakhon Si Thammarat 80160, Thailand., Siribunyaphat N; School of Informatics, Walailak University, Nakhon Si Thammarat 80160, Thailand.; Informatics Innovative Center of Excellence, Walailak University, Nakhon Si Thammarat 80160, Thailand., Aung ST; Division of Mathematics and Science, Parami University, Washington, DC 20036, USA., Punsawad Y; School of Informatics, Walailak University, Nakhon Si Thammarat 80160, Thailand.; Informatics Innovative Center of Excellence, Walailak University, Nakhon Si Thammarat 80160, Thailand.
Source: Sensors (Basel, Switzerland) [Sensors (Basel)] 2026 Aug 05; Vol. 26 (15). Date of Electronic Publication: 2026 Aug 05.
Publication Type: Journal Article
Language: English
Journal Info: Publisher: MDPI Country of Publication: Switzerland NLM ID: 101204366 Publication Model: Electronic Cited Medium: Internet ISSN: 1424-8220 (Electronic) Linking ISSN: 14248220 NLM ISO Abbreviation: Sensors (Basel) Subsets: MEDLINE
Imprint Name(s): Original Publication: Basel, Switzerland : MDPI, c2000-
MeSH Terms: Electroencephalography*/methods , Emotions*/physiology , Acoustic Stimulation*/methods , Brain-Computer Interfaces*, Humans ; Female ; Male ; Adult ; Wavelet Analysis ; Machine Learning
Abstract: Although electroencephalography (EEG)-based emotion recognition is a promising approach for affective brain-computer interface (BCI) applications, substantial inter-subject variability continues to limit its generalizability. This study proposed an EEG-based framework to recognize emotions within a valence-arousal model using auditory stimulation. Predefined emotional states were established using validated affective video clips and subsequently evaluated through EEG responses elicited by instrumental melodies. Three EEG features, which include discrete wavelet transform (DWT), functional connectivity (FC), and effective connectivity (EC), together with their combined feature set, were systematically evaluated using five machine learning classifiers. Performance was assessed under subject-dependent, subject-independent (leave-one-subject-out, LOSO), and few-shot subject-adaptation protocols. The results showed that DWT achieved the highest subject-dependent classification accuracy (0.88), followed by the combined feature set (0.84). In contrast, the subject-independent LOSO evaluation yielded near-chance performance across all feature domains (0.23-0.30), highlighting substantial inter-subject variability. Few-shot subject adaptation using 25-75% subject-specific calibration data substantially improved subject-independent performance, with the highest accuracy of 0.77 achieved by FC at 75% calibration. In conclusion, these findings demonstrate the feasibility of EEG-based emotion recognition using auditory stimulation under predefined affective conditions and provide a foundation for the future development of personalized affective brain-computer interface systems.
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Grant Information: CGS-RF-2025/01 Walailak University
Contributed Indexing: Keywords: auditory stimulation; brain–computer interface; discrete wavelet transform; electroencephalography; emotion recognition; few-shot adaptation
Entry Date(s): Date Created: 20260813 Date Completed: 20260813 Latest Revision: 20260815
Update Code: 20260815
PubMed Central ID: PMC13468820
DOI: 10.3390/s26154971
PMID: 42590746
Database: MEDLINE
Description
ISSN:1424-8220
DOI:10.3390/s26154971