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

This 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 propos...

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Published in:Journal of Medical Systems Vol. 44; no. 6; pp. 1 - 13
Main Authors: Fu, Rongrong, Han, Mengmeng, Wang, Fuwang, Shi, Peiming
Format: equations & formulas pictorial research tables/charts tracings Journal Article
Published: Springer Nature Jun2020
Online Access:View this record in EBSCOhost
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        01485598
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      dt: Jun2020
      vid: 44
      iid: 6
      pid: 237
      pub: Springer Nature
      place: New York, New York
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        143571478
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        10.1007/s10916-020-01571-0
        143571478
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        atl: Intentions Recognition of EEG Signals with High Arousal Degree for Complex Task.
      aug:
        au:
          Fu, Rongrong
          Han, Mengmeng
          Wang, Fuwang
          Shi, Peiming
        affil: Measurement Technology and Instrumentation Key Lab of Hebei Province, Yanshan University, 066004, Qinhuangdao, China
      sug:
        subj:
          Electroencephalography
          Evoked Potentials
          Intention Evaluation
          Task Performance and Analysis
          Paradigms Methods
          Virtual Reality
          Feedback
          Rehabilitation Methods
          Human
          Spatial Perception
          ROC Curve
          Paired T-Tests
          Conceptual Framework
          Models, Statistical
          Descriptive Statistics
      ab: This 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 extract 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 paired t-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 tree (DT) and the k-nearest neighborhood (k-NN) rule. The results suggest that the proposed method is very competitive with other methods.
      pubtype: Academic Journal
      doctype:
        equations & formulas
        pictorial
        research
        tables/charts
        tracings
        Journal Article
      ougenre: Article
    language: English
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