| 摘要 |
<p>Method for providing a brain-computer interface, and a brain-computer interface, having a classification model as part of a processing pipeline. The input signal comprises a neural signature to be detected by the classification model. Obtaining the classification model comprises training the classification model using the input signal and assigning one or more labels to the input signal at different time points. Each label indicates whether the input signal at the associated time point of the label should be classified as a target activity. Further processing steps are whitening the input signal using whitening parameters to reduce temporal and spatial correlations in the input signal to obtain a whitened time series, specifying a polynomial kernel using polynomial kernel parameters,that induces a mapping of the whitened time series to a linearly separable feature space,and classifying the feature space using the output of the polynomial kernel, classification parameters, and weights.</p> |
