Author(s)

Liu Shaobo, Sun Fuchun, Zhang Wenchang, Tan Chuanqi

Corresponding Author

Shaobo Liu

ABSTRACT

Feature Extraction is one of the most important steps in brain-computer interface (BCI) systems. In particular, the common spatial patterns (CSP) is one of the most successful solutions which has been widely used in MI-BCIs. However, studies have reported that the performance of CSP heavily depends on its channels configuration. To the best of our current knowledge, it is not available to obtain the active channels related to brain activities of stroke patients in advance. Hence, we usually set a relatively broad channels or try to select a subject-specific channels when applying CSP to stroke patients. In this paper, we present a novel approach which employs wavelet transform and boosting algorithm to improve accuracy and robustness of the conventional CSP. In our proposed approach, the channel configurations are initially divided into multiple preconditions. Then, the informative features of the predefined channels are obtained using the Wavelet Common Spatial Pattern (W-CSP) algorithm that provided high-temporal-spectral resolution. Eventually, we train weak classifiers on the obtained features and combine these weak classifiers to a weighted combinational model using boosting strategy. Extensive experiments have been performed on datasets from the famous BCI competition III and IV. The results demonstrate its superior performance.

KEYWORDS

Common Spatial Pattern, Wavelet Transform, Brain-Computer Interface (BCI)