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Until now, the canonical correlation analysis (CCA)-based method has been most widely applied to steady-state visual evoked potential (SSVEP). Artificial sine-cosine signals are used as the original references in the CCA method, which could hardly reflect the real SSVEP features buried in electroencephalogram (EEG). In this study, we use principal component analysis (PCA) to extract EEG features multivariate...
This paper evaluates and analyses the construction of “Resource-conserving and Environment-friendly” Society in China's Midwestern provinces scientifically based on Factor Analysis (FA) and Principal Component Analysis (PCA). The results demonstrate that now the construction of “two-oriented” Society in China's Midwestern provinces mainly rely on government investment, yet the effects of R&D is...
To Solve the problem of low accuracy and high false alarm, a construction method of Bagging ensemble based on random subspace PCA (Principle Component Analysis) was proposed. To create a training data for a base classifier, the feature set is randomly split into several subsets and PCA is applied to each subset. all principal components are retained to keep the variety information in the data; To...
An experiment was designed to record the electroencephalography (EEG) when people caught the vision of different directional moving (turning right, turning left, moving forward, moving backward). The EEG signals were obtained from 30 EEG electrode sites. The tagged permutations of EEG electrode sites were obtained and a discrete space was formed by the tags. DPSO algorithm was used to search the optimal...
In this paper, an experiment was designed to get the electroencephalography (EEG) when people caught the vision of moving to different direction (right, left, front, back). Through Fourier Transform., the feature of the EEG was obtained. Then, the algorithm of principal component analysis (PCA) was used to simplify the feature. Finally, in order to classify the direction perception EEG, it was distinguished...
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