TY - GEN
T1 - Investigating the effects of lossy compression on age, gender and alcoholic information in EEG signals
AU - Nguyen, Binh
AU - Ma, Wanli
AU - Tran, Dat
PY - 2019
Y1 - 2019
N2 - The age and gender information extracted from Electroencephalogram (EEG) has been used in various applications which are allocating a person to age and gender groups, identifying or authenticating a person and improving brain-computer interface systems. Besides this, the EEG-based automatic recognition of alcoholics greatly supports to the psychiatrists. However, one of the major challenges when using EEG is about storing and transmitting a huge amount of EEG data, leading to the need of using compression. Although lossy compression techniques give much higher compression ratio (CR) than lossless ones, they introduce the loss of information including the age, gender and alcoholic information in the reconstructed signals, which may reduce the performance of EEG-based age, gender and alcoholic recognition systems significantly. In this paper, the impact of lossy compression on the age, gender and alcoholic information extracted from EEG signals is examined in detail with different feature extraction and machine learning techniques. Our experimental results indicate that with an appropriate feature extraction technique, we could minimize the information loss in EEG compression and maintain the high performance of EEG-based age, gender and alcoholics recognition systems.
AB - The age and gender information extracted from Electroencephalogram (EEG) has been used in various applications which are allocating a person to age and gender groups, identifying or authenticating a person and improving brain-computer interface systems. Besides this, the EEG-based automatic recognition of alcoholics greatly supports to the psychiatrists. However, one of the major challenges when using EEG is about storing and transmitting a huge amount of EEG data, leading to the need of using compression. Although lossy compression techniques give much higher compression ratio (CR) than lossless ones, they introduce the loss of information including the age, gender and alcoholic information in the reconstructed signals, which may reduce the performance of EEG-based age, gender and alcoholic recognition systems significantly. In this paper, the impact of lossy compression on the age, gender and alcoholic information extracted from EEG signals is examined in detail with different feature extraction and machine learning techniques. Our experimental results indicate that with an appropriate feature extraction technique, we could minimize the information loss in EEG compression and maintain the high performance of EEG-based age, gender and alcoholics recognition systems.
KW - age
KW - EEG
KW - EEG lossy compression
KW - EEG-based age
KW - gender information
KW - gender recognition systems
UR - http://www.scopus.com/inward/record.url?scp=85076262877&partnerID=8YFLogxK
UR - http://kes2019.kesinternational.org/
U2 - 10.1016/j.procs.2019.09.178
DO - 10.1016/j.procs.2019.09.178
M3 - Conference contribution
AN - SCOPUS:85076262877
VL - 159
T3 - Procedia Computer Science
SP - 231
EP - 240
BT - 23rd KES International Conference on Knowledge-Based and Intelligent Information & Engineering Systems KES2019
A2 - Rudas, I. J.
A2 - Janos, C.
A2 - Toro, C.
A2 - Botzheim, J.
A2 - Howlett, R. J.
A2 - Jain, L. C.
PB - Elsevier
CY - Netherlands
T2 - 23rd International Conference on Knowledge-Based and Intelligent Information & Engineering Systems, KES 2019
Y2 - 4 September 2019 through 6 September 2019
ER -