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Regularization has been one of the most popular approaches to prevent overfitting in electroencephalogram (EEG) classification of brain–computer interfaces (BCIs). The effectiveness of regularization is often highly dependent on the selection of regularization parameters that are typically determined by cross-validation (CV). However, the CV imposes two main limitations on BCIs: 1) a large amount...
Brain-computer interface (BCI) plays an important role in helping the people with severe motor disability. In event-related potential (ERP) based BCIs, subjects were asked to count the target stimulus in the offline experiment, the recorded electroencephalogram (EEG) data was used to train the classification mode. However, subjects may make mistakes in counting the target stimulus or be affected by...
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...
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