Target detection using incremental learning on single-trial evoked response

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.