Progressive fusion of multi rate motor imagery classification for brain computer interfaces
Motivated by limited availability of training data for practical implementation of a synchronous Brain computer interface (BCI) the paper proposes a novel EEG based framework consisting of two separate (partially coupled) filters running in parallel: (i) The Progressive Filter: An efficient but computationally extensive combination of feature extraction and classification that uses new arriving epochs to train in an offline fashion, and; (ii) The Active Filter: A simplified feature extraction approach running online based on pre trained classifiers. The Active Filter produces MI classification results at the end of each epoch while the Progressive Filter takes several epochs to perform processing, adaptation, and re training tasks. Once the computation of the Progressive Filter is complete, the two filters are coupled to improve the real time performance of the overall system.
2017 IEEE 60th International Midwest Symposium on Circuits and Systems (MWSCAS)