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In this paper we introduced a new method to optimally select the time window for a single-trial classification problem in BCI system. As a hybrid-BCI, we combine EEG and NIRS signals to improve the performance of BCI system. Since there's a coupled relationship between EEG and NIRS, we try to define the activation state of subject's brain according to the changes of hemoglobin. We therefore defined...
In this work, Accumulated Continuous Classification (ACC) method was proposed to facilitate single-trial decoding in Motor Imagery based Brain-computer Interface. Exploring the temporal information, the time interval of the single-trial was divided into several sub-segments, then the classified results were combined. Theoretical model was proposed, and the proposed method was evaluated in BCI competition...
The proliferation of wireless localization technologies provides a promising future for serving human beings in indoor scenarios. Their applications include real-time tracking, activity recognition, health care, navigation, emergence detection, and target-of-interest monitoring, among others. Additionally, indoor localization technologies address the inefficiency of GPS (Global Positioning System)...
Classification based on predictive association rules (CPAR) is a kind of association classification methods which combines the advantages of both associative classification and traditional rule-based classification. For rule generation, CPAR is more efficient than traditional rule-based classification because much repeated calculation is avoided and multiple literals can be selected to generate multiple...
Because spatial data are usually high-dimensional, complex and mass, we categorize the attributes of each spatial data object as spatial attributes and non-spatial attributes. We use spatial attributes to construct spatial index and determine spatial neighborhood, and use non-spatial attributes to compute outlying degree and spatial outlying degree factor, so as to solve the problem of index and the...
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