Abstract
The importance of learning important features in an automatic manner is growing exponentially as the volume of data and number of systems using pattern recognition techniques continue to increase. In this paper, arousal recognition from multi channels EEG signals was conducted using human crafted statistical features and learned features from 32 different EEG source channels. We have obtained 98.99% accuracy rate with unsupervised feature learning approach for Arousal classification. Unsupervised feature learning worked better compared to handcrafted feature approach.
Original language | English |
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Title of host publication | 2017 25th Signal Processing and Communications Applications Conference, SIU 2017 |
Publisher | Institute of Electrical and Electronics Engineers Inc. |
ISBN (Electronic) | 9781509064946 |
DOIs | |
Publication status | Published - 27 Jun 2017 |
Event | 25th Signal Processing and Communications Applications Conference, SIU 2017 - Antalya, Turkey Duration: 15 May 2017 → 18 May 2017 |
Publication series
Name | 2017 25th Signal Processing and Communications Applications Conference, SIU 2017 |
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Conference
Conference | 25th Signal Processing and Communications Applications Conference, SIU 2017 |
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Country/Territory | Turkey |
City | Antalya |
Period | 15/05/17 → 18/05/17 |
Bibliographical note
Publisher Copyright:© 2017 IEEE.
Keywords
- Arousal Recognition
- Autoencoders
- Brain Computer Interface
- Brain EEG Analysis
- Feature Learning
- kNN
- Multi-channel sensor processing
- Random Forests
- Signal Processing