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Yuan-Pin Lin
Researcher at National Sun Yat-sen University
Publications - 52
Citations - 2242
Yuan-Pin Lin is an academic researcher from National Sun Yat-sen University. The author has contributed to research in topics: Electroencephalography & Emotion classification. The author has an hindex of 19, co-authored 48 publications receiving 1796 citations. Previous affiliations of Yuan-Pin Lin include National Taiwan University & University of California, San Diego.
Papers
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Journal ArticleDOI
EEG-Based Emotion Recognition in Music Listening
Yuan-Pin Lin,Chi-Hong Wang,Tzyy-Ping Jung,Tien-Lin Wu,Shyh-Kang Jeng,Jeng Ren Duann,Jyh-Horng Chen +6 more
TL;DR: This study applied machine-learning algorithms to categorize EEG dynamics according to subject self-reported emotional states during music listening to identify 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 duringMusic listening.
Proceedings ArticleDOI
EEG-based emotion recognition in music listening: A comparison of schemes for multiclass support vector machine
TL;DR: It is found that using one-against-one scheme of hierarchical binary classifier results in an improvement to performance, but also established an alternative solution for emotion recognition by proposed model-based scheme depending on 2D emotion model.
Journal ArticleDOI
Improving EEG-Based Emotion Classification Using Conditional Transfer Learning
TL;DR: The proposed conditional TL (cTL) framework may shed light on the development of a robust emotion-classification model using fewer labeled subject-specific data toward a real-life affective brain-computer interface (ABCI).
Journal ArticleDOI
Fusion of electroencephalographic dynamics and musical contents for estimating emotional responses in music listening.
TL;DR: The present study not only provided principles for constructing an EEG-based multimodal approach, but also revealed the fundamental insights into the interplay of the brain activity and musical contents in emotion modeling.
Journal ArticleDOI
A subject-transfer framework for obviating inter- and intra-subject variability in EEG-based drowsiness detection.
TL;DR: This study proposes applying hierarchical clustering to assess the inter‐ and intra‐subject variability within a large‐scale dataset of EEG collected in a simulated driving task, and validates the feasibility of transferring EEG‐based drowsiness‐detection models across subjects.