Exploration of reusing the pre-recorded training data set to improve the supervised classifier for EEG-based motor-imagery brain computer interfaces
Brain computer interface based on Electroencephalogram can be used to control the external devices through the motor imagery, and may be the next-generation user computer interface. However, this system requires a significant amount of data for the supervised algorithm training. The collection of training data is time-consuming, which may impede the usage in the daily life. In this paper, the trade-off between the training data size and algorithm accuracy is first analyzed. Then the reusing of the generalized pre-recorded training data set is explored to further improve this trade off. According to the simulation results, 63.8% training data collection time can first be saved with only 3% accuracy degradation.