Age and Gender Classification Using EEG Paralinguistic Features

Research output: A Conference proceeding or a Chapter in BookConference contributionpeer-review

34 Citations (Scopus)


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 languageEnglish
Title of host publicationThe 6th IEEE/EMBS Conference on Neural Engineering (NER)
EditorsKenji Sunagawa, Christian Roux, Toshiyo Tamura, Nigel Lovell, Masaaki Makikawa
Place of PublicationUSA
PublisherIEEE, Institute of Electrical and Electronics Engineers
Number of pages4
ISBN (Electronic)9781467319690
Publication statusPublished - 2013
Event6th IEEE/EMBS Conference on Neural Engineering (NER) - San Diego, San Diego, United States
Duration: 6 Nov 20138 Nov 2013


Conference6th IEEE/EMBS Conference on Neural Engineering (NER)
Country/TerritoryUnited States
CitySan Diego


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