Intentions Recognition of EEG Signals with High Arousal Degree for Complex Task

AbstractThis paper presents a novel electroencephalography (EEG) evoked paradigm based on neurological rehabilitation. By implementing a conceptual model “cup-and-ball” system, EEG signals in manipulating the dynamic constrained objects are generated. Based on the operational EEG signals, a method is proposed to recognize different mental intentions. Under the manipulating task with a high arousal level, common spatial patterns (CSP) is used to e xtract and optimize features of the EEG signals from ten participants. Quadratic discriminant analysis (QDA) is implemented on EEG signals in different dimensions to identify different EEG patterns. The cross-validation is used to make classifier adaptive to a given data set. The receiver operating characteristic (ROC) curves are presented to illustrate recognition performance. The classification effect of QDA is verified by pairedt-test (P <  0.001). Based on the proposed method, the average accuracy of mental intentions is 0.9857 ± 0.0191 and the area under the ROC curve (AUC) is 0.9665 ± 0.0291. The performance of QDA is also compared with the other three classifiers such as the support vector machine (SVM), the decision t ree (DT) and thek-nearest neighborhood (k-NN) rule. The results suggest that the proposed method is very competitive with other methods.
Source: Journal of Medical Systems - Category: Information Technology Source Type: research