TY - GEN
T1 - Target detection using incremental learning on single-trial evoked response
AU - Huang, Yonghong
AU - Erdogmus, Deniz
AU - Pavel, Misha
AU - Hild, Kenneth E.
AU - Mathan, Santosh
PY - 2009
Y1 - 2009
N2 - The human neural responses associated with cognitive events, referred as event related potentials (ERPs), can provide reliable inference for target image detection. Incremental learning has been widely investigated to deal with large datasets. To solve the problem of data growing over time in cross session studies, we apply an incremental learning support vector machines (SVM) method on single-trial ERP detection for identifying targets in satellite images. We implement the incremental learning SVM by keeping only the support vectors, instead of all the data, from the previous sessions and incorporating them with the data of the current session. Thus the incremental learning dramatically reduces the computational load. The results demonstrate that the incremental learning ERP detection system performs as well as the naive method, which uses only the current training session, and the batch mode, which uses all training data. Furthermore, it is more computationally efficient, which allows it to better cope with a continuous stream of EEG data.
AB - The human neural responses associated with cognitive events, referred as event related potentials (ERPs), can provide reliable inference for target image detection. Incremental learning has been widely investigated to deal with large datasets. To solve the problem of data growing over time in cross session studies, we apply an incremental learning support vector machines (SVM) method on single-trial ERP detection for identifying targets in satellite images. We implement the incremental learning SVM by keeping only the support vectors, instead of all the data, from the previous sessions and incorporating them with the data of the current session. Thus the incremental learning dramatically reduces the computational load. The results demonstrate that the incremental learning ERP detection system performs as well as the naive method, which uses only the current training session, and the batch mode, which uses all training data. Furthermore, it is more computationally efficient, which allows it to better cope with a continuous stream of EEG data.
KW - Brain computer interface
KW - Event-related potential
KW - Incremental learning
KW - Support vector machine
KW - Target detection
UR - http://www.scopus.com/inward/record.url?scp=70349454107&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=70349454107&partnerID=8YFLogxK
U2 - 10.1109/icassp.2009.4959625
DO - 10.1109/icassp.2009.4959625
M3 - Conference contribution
AN - SCOPUS:70349454107
SN - 9781424423545
T3 - ICASSP, IEEE International Conference on Acoustics, Speech and Signal Processing - Proceedings
SP - 481
EP - 484
BT - 2009 IEEE International Conference on Acoustics, Speech, and Signal Processing - Proceedings, ICASSP 2009
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 2009 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2009
Y2 - 19 April 2009 through 24 April 2009
ER -