EEG-based emotion recognition using machine learning techniques
Electroencephalography (EEG)-based emotion recognition attempts to detect the affective states of humans directly via spontaneous EEG signals, bypassing the peripheral nervous system. In this thesis, we explore various machine learning techniques for EEG-based emotion recognition, and focus on the t...
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| Format: | Theses and Dissertations |
| Language: | English |
| Published: |
2018
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| Online Access: | https://hdl.handle.net/10356/89698 http://hdl.handle.net/10220/46340 |
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| Institution: | Nanyang Technological University |
| Language: | English |
| Summary: | Electroencephalography (EEG)-based emotion recognition attempts to detect the affective states of humans directly via spontaneous EEG signals, bypassing the peripheral nervous system. In this thesis, we explore various machine learning techniques for EEG-based emotion recognition, and focus on the three research gaps outlined as follows.
1. Stable feature selection for recalibration-less affective Brain-Computer Interfaces.
2. Cross-subject transfer learning for calibration-less affective Brain-Computer Interfaces.
3. Unsupervised feature learning for affective Brain-Computer Interfaces.
We propose several novel methods in this thesis to address the three research gaps and validate our proposed methods by experiments. Extensive comparisons between our methods and other existing methods justify the advantages of our methods. |
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