DocumentCode :
3512558
Title :
Target detection using incremental learning on single-trial evoked response
Author :
Huang, Yonghong ; Erdogmus, Deniz ; Pavel, Misha ; Hild, Kenneth E. ; Mathan, Santosh
fYear :
2009
fDate :
19-24 April 2009
Firstpage :
481
Lastpage :
484
Abstract :
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.
Keywords :
brain-computer interfaces; learning (artificial intelligence); object detection; support vector machines; EEG data; ERP detection system; brain computer interface; cognitive events; event related potentials; human neural responses; incremental learning; satellite images; single-trial evoked response; support vector machines; target detection; target image detection; Electroencephalography; Enterprise resource planning; Humans; Machine learning; Object detection; Reliability engineering; Satellites; Support vector machine classification; Support vector machines; Training data; Brain computer interface; Event-related potential; Incremental learning; Support vector machine; Target detection;
fLanguage :
English
Publisher :
ieee
Conference_Titel :
Acoustics, Speech and Signal Processing, 2009. ICASSP 2009. IEEE International Conference on
Conference_Location :
Taipei
ISSN :
1520-6149
Print_ISBN :
978-1-4244-2353-8
Electronic_ISBN :
1520-6149
Type :
conf
DOI :
10.1109/ICASSP.2009.4959625
Filename :
4959625
Link To Document :
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