Unsupervised processing methods for motor imagery-based brain-computer interface

摘要

Brain-computer interface (BCI) research is speedily growing and numerous innovative techniques are proposed for implementing BCI. One of the major drawbacks in BCI's application is the problem of searching out a response from one trial. Hence, many trials are performed for every element so as to decrease the error in prediction. This led to a long time before accurately predicting the user intent and intensive user training is needed. The objective of this paper is to investigate a new technique to process the signal from brain in a real time without any prior training. The new approach is applied to experimental data for motor imagery-based BCI and is compared to the classification results of the same data using the conventional processing techniques requiring prior calibration. Different classification methodologies were used as in time and frequency domain. It is concluded that wavelet transform get best performance reaches 82.14%. Therefore, these promising results recommend that this approach can reach accuracies not extremely far from those got with training.