EEG-based emotion recognition in music listening
Lin YP, Wang CH, Jung TP, Wu TL, Jeng SK, Duann JR, Chen JH
IEEE transactions on bio-medical engineering · 227 citations
Review labels
Neutral facts our review recorded about how this study was done. They describe method, never whether we like the result.
How it was studied
- Design
- In vitro/mechanistic study (classified by our AI screen)
- Studied in
- People
- Main outcome
- Health markers and function
Who paid for it
- Funding
- Funding not disclosed
Publication
- Published
- 2010-05-12 · IEEE Trans Biomed Eng · vol. 57 · issue 7 · pp. 1798–1806
- Publisher
- Institute of Electrical and Electronics Engineers
- Cited
- 1038 citations · more than 98% of similar papers · 8.2× the field average
- Impact
- Top 10% most cited in its field
- References
- 40 works
- Access
- Paywalled
- Research areas
- EEG and Brain-Computer Interfaces · Emotion and Mood Recognition · Neural dynamics and brain function
- Keywords
- Active listening, Speech recognition, Electroencephalography, Emotion recognition, Computer science, Psychology, Artificial intelligence, Pattern recognition (psychology), Communication, Neuroscience
- MeSH
- humans, electroencephalography, bayes theorem, electrodes, emotions, evoked potentials, auditory, algorithms, music, artificial intelligence, signal processing, computer-assisted, pattern recognition, automated, adult, female, male
7 authors
From TW, US
- Yuan‐Pin LinNational Taiwan University; National Taipei University
- Chi‐Hong WangCardinal Tien Hospital
- Tzyy‐Ping JungUniversity of California San Diego
- Tien-Lin WuNational Taiwan University
- Shyh‐Kang JengNational Taiwan University
- Jeng‐Ren DuannChina Medical University; University of California San Diego; China Medical University Hospital
Abstract
Ongoing brain activity can be recorded as electroencephalograph (EEG) to discover the links between emotional states and brain activity. This study applied machine-learning algorithms to categorize EEG dynamics according to subject self-reported emotional states during music listening. A framework was proposed to optimize EEG-based emotion recognition by systematically 1) seeking emotion-specific EEG features and 2) exploring the efficacy of the classifiers. Support vector machine was employed to classify four emotional states (joy, anger, sadness, and pleasure) and obtained an averaged classification accuracy of 82.29% +/- 3.06% across 26 subjects. Further, this study identified 30 subject-independent features that were most relevant to emotional processing across subjects and explored the feasibility of using fewer electrodes to characterize the EEG dynamics during music listening. The identified features were primarily derived from electrodes placed near the frontal and the parietal lobes, consistent with many of the findings in the literature. This study might lead to a practical system for noninvasive assessment of the emotional states in practical or clinical applications.
Abstract via Europe PMC. Copyright remains with the authors or publisher.
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