Emotional state classification of brain signals using extreme learning machine (ELM) algorithm
Electroencephalogram (EEG) signals have been proven to have strong correlation with underlying human emotions. Numerous approaches have been reported for emotion recognition from EEG signals; however, not much effort has been performed in applying Wavelet Transform for emotion recognition from EEG s...
| Main Author: | |
|---|---|
| Other Authors: | |
| Format: | Final Year Project (FYP) |
| Language: | English |
| Published: |
2012
|
| Subjects: | |
| Online Access: | http://hdl.handle.net/10356/50056 |
| _version_ | 1811677205227372544 |
|---|---|
| author | Yohanes, Rendi Ein Janvier. |
| author2 | Huang Guangbin |
| author_facet | Huang Guangbin Yohanes, Rendi Ein Janvier. |
| author_sort | Yohanes, Rendi Ein Janvier. |
| collection | NTU |
| description | Electroencephalogram (EEG) signals have been proven to have strong correlation with underlying human emotions. Numerous approaches have been reported for emotion recognition from EEG signals; however, not much effort has been performed in applying Wavelet Transform for emotion recognition from EEG signals despite its proven effectiveness on non-stationary signal analysis. |
| first_indexed | 2024-10-01T02:33:40Z |
| format | Final Year Project (FYP) |
| id | ntu-10356/50056 |
| institution | Nanyang Technological University |
| language | English |
| last_indexed | 2024-10-01T02:33:40Z |
| publishDate | 2012 |
| record_format | dspace |
| spelling | ntu-10356/500562023-07-07T16:56:09Z Emotional state classification of brain signals using extreme learning machine (ELM) algorithm Yohanes, Rendi Ein Janvier. Huang Guangbin Ser Wee School of Electrical and Electronic Engineering Centre for Signal Processing DRNTU::Engineering::Electrical and electronic engineering::Electronic systems::Signal processing Electroencephalogram (EEG) signals have been proven to have strong correlation with underlying human emotions. Numerous approaches have been reported for emotion recognition from EEG signals; however, not much effort has been performed in applying Wavelet Transform for emotion recognition from EEG signals despite its proven effectiveness on non-stationary signal analysis. Bachelor of Engineering 2012-05-29T06:23:24Z 2012-05-29T06:23:24Z 2012 2012 Final Year Project (FYP) http://hdl.handle.net/10356/50056 en Nanyang Technological University 73 p. application/pdf |
| spellingShingle | DRNTU::Engineering::Electrical and electronic engineering::Electronic systems::Signal processing Yohanes, Rendi Ein Janvier. Emotional state classification of brain signals using extreme learning machine (ELM) algorithm |
| title | Emotional state classification of brain signals using extreme learning machine (ELM) algorithm |
| title_full | Emotional state classification of brain signals using extreme learning machine (ELM) algorithm |
| title_fullStr | Emotional state classification of brain signals using extreme learning machine (ELM) algorithm |
| title_full_unstemmed | Emotional state classification of brain signals using extreme learning machine (ELM) algorithm |
| title_short | Emotional state classification of brain signals using extreme learning machine (ELM) algorithm |
| title_sort | emotional state classification of brain signals using extreme learning machine elm algorithm |
| topic | DRNTU::Engineering::Electrical and electronic engineering::Electronic systems::Signal processing |
| url | http://hdl.handle.net/10356/50056 |
| work_keys_str_mv | AT yohanesrendieinjanvier emotionalstateclassificationofbrainsignalsusingextremelearningmachineelmalgorithm |