Patent ID: 9538934
Date: 2017-01-10
CPC Classifications: A61B,G06F

Claim:
1. A method of training a classification algorithm for a Brain Computer Interface (BCI), the method comprising the steps of: dividing an Electroencephalography (EEG) signal into a plurality of time segments, for each time segment, dividing a corresponding EEG signal portion into a plurality of frequency bands; for each frequency band, computing a spatial filtering projection matrix based on a Common Spatial Pattern (CSP) algorithm and a corresponding feature, and computing mutual information of each corresponding feature with respect to one or more motor imagery classes; for each time segment, summing the mutual information of all the corresponding features with respect to the respective classes; selecting the corresponding features of each time segment with a maximum sum of mutual information for one class for training classifiers of the classification algorithm; and storing the selected corresponding features of the time segments in a computer system of the BCI, wherein said BCI is configured to determine motor imagery of a person by using said stored selected corresponding features in motor imagery detection.