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EEG Classification with a Sequential Decision-Making Method in Motor Imagery BCI.
Liu, Rong; Wang, Yongxuan; Newman, Geoffrey I; Thakor, Nitish V; Ying, Sarah.
Afiliación
  • Liu R; 1 Biomedical Engineering Department, Dalian University of Technology, Dalian, Liaoning 116024, P. R. China.
  • Wang Y; 2 Affiliated Zhongshan Hospital of Dalian University, Dalian University of Technology, Dalian, Liaoning 116001, P. R. China.
  • Newman GI; 3 Department of Biomedical Engineering, School of Medicine, Johns Hopkins University, Baltimore, MD 21205, USA.
  • Thakor NV; 3 Department of Biomedical Engineering, School of Medicine, Johns Hopkins University, Baltimore, MD 21205, USA.
  • Ying S; 4 Departments of Radiology, Neurology, and Ophthalmology, School of Medicine, Johns Hopkins University, Baltimore, MD 21205, USA.
Int J Neural Syst ; 27(8): 1750046, 2017 Dec.
Article en En | MEDLINE | ID: mdl-29046111
To develop subject-specific classifier to recognize mental states fast and reliably is an important issue in brain-computer interfaces (BCI), particularly in practical real-time applications such as wheelchair or neuroprosthetic control. In this paper, a sequential decision-making strategy is explored in conjunction with an optimal wavelet analysis for EEG classification. The subject-specific wavelet parameters based on a grid-search method were first developed to determine evidence accumulative curve for the sequential classifier. Then we proposed a new method to set the two constrained thresholds in the sequential probability ratio test (SPRT) based on the cumulative curve and a desired expected stopping time. As a result, it balanced the decision time of each class, and we term it balanced threshold SPRT (BTSPRT). The properties of the method were illustrated on 14 subjects' recordings from offline and online tests. Results showed the average maximum accuracy of the proposed method to be 83.4% and the average decision time of 2.77[Formula: see text]s, when compared with 79.2% accuracy and a decision time of 3.01[Formula: see text]s for the sequential Bayesian (SB) method. The BTSPRT method not only improves the classification accuracy and decision speed comparing with the other nonsequential or SB methods, but also provides an explicit relationship between stopping time, thresholds and error, which is important for balancing the speed-accuracy tradeoff. These results suggest that BTSPRT would be useful in explicitly adjusting the tradeoff between rapid decision-making and error-free device control.
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Texto completo: 1 Colección: 01-internacional Base de datos: MEDLINE Asunto principal: Encéfalo / Toma de Decisiones / Electroencefalografía / Interfaces Cerebro-Computador / Imaginación / Actividad Motora Tipo de estudio: Prognostic_studies Límite: Humans Idioma: En Revista: Int J Neural Syst Asunto de la revista: ENGENHARIA BIOMEDICA / INFORMATICA MEDICA Año: 2017 Tipo del documento: Article Pais de publicación: Singapur
Texto completo
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Texto completo: 1 Colección: 01-internacional Base de datos: MEDLINE Asunto principal: Encéfalo / Toma de Decisiones / Electroencefalografía / Interfaces Cerebro-Computador / Imaginación / Actividad Motora Tipo de estudio: Prognostic_studies Límite: Humans Idioma: En Revista: Int J Neural Syst Asunto de la revista: ENGENHARIA BIOMEDICA / INFORMATICA MEDICA Año: 2017 Tipo del documento: Article Pais de publicación: Singapur