EEG-based Emotion Detection Using Unsupervised Transfer Learning
Research output: Contribution to book/Conference proceedings/Anthology/Report › Conference contribution › Contributed › peer-review
Contributors
Details
Original language | English |
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Title of host publication | 2019 41st Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC) |
Pages | 694-697 |
Number of pages | 4 |
Publication status | Published - 2019 |
Peer-reviewed | Yes |
External IDs
Scopus | 85077839374 |
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Keywords
Keywords
- convolutional neural nets, diseases, electroencephalography, emotion recognition, independent component analysis, medical signal processing, neurophysiology, signal classification, unsupervised learning, acute stages, Alzheimer's disease, high-fidelity emotion recognition systems, EEG data, Signal-to-noise ratio, subject-to-subject variability, integrated framework, semigeneric emotion detection, convolutional neural network, EEG-based emotion recognition, testing data, publicly available repositories, CNN classifier, transfer learning approach, subject-independent, unsupervised transfer learning, emotion classification, EEG signal processing, neurological disorders, Amyotrophic Lateral Sclerosis, ALS, standard international affective picture system, Electroencephalography, Training, Task analysis, Manuals, Unsupervised learning, Feature extraction