EEG-based Emotion Detection Using Unsupervised Transfer Learning

Research output: Contribution to book/conference proceedings/anthology/reportConference contributionContributedpeer-review

Contributors

Details

Original languageEnglish
Title of host publication2019 41st Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC)
Pages694-697
Number of pages4
Publication statusPublished - 2019
Peer-reviewedYes

External IDs

Scopus 85077839374

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