Abstract
The effects of age and gender on EEG signal have been investigated in clinical psychophysiology. However extracting age and gender information from EEG data has not been addressed. This information is useful in building automatic systems that can classify a person in to gender or age groups based on EEG characteristics of that person, index EEG data for searching, identify or verify a person, and improve brain-computer interface systems. We propose in this paper a framework of automatic age and gender classification system using EEG data. We also propose a speech-based method to extract paralinguistic features in EEG signal that contain rich age and gender information and apply these features to improve performance of our age and gender classification system. Experimental results for system evaluation and comparison are also presented
Original language | English |
---|---|
Title of host publication | The 6th IEEE/EMBS Conference on Neural Engineering (NER) |
Editors | Kenji Sunagawa, Christian Roux, Toshiyo Tamura, Nigel Lovell, Masaaki Makikawa |
Place of Publication | USA |
Publisher | IEEE, Institute of Electrical and Electronics Engineers |
Pages | 1295-1298 |
Number of pages | 4 |
Volume | 1 |
ISBN (Electronic) | 9781467319690 |
DOIs | |
Publication status | Published - 2013 |
Event | 6th IEEE/EMBS Conference on Neural Engineering (NER) - San Diego, San Diego, United States Duration: 6 Nov 2013 → 8 Nov 2013 |
Conference
Conference | 6th IEEE/EMBS Conference on Neural Engineering (NER) |
---|---|
Country/Territory | United States |
City | San Diego |
Period | 6/11/13 → 8/11/13 |