Common spatial pattern (CSP) is one of the most popular and effective feature extraction methods for motor imagery-based brain-computer interface (BCI), but the inherent drawback of CSP is that the estimation of the covariance matrices is sensitive to noise. In this work, local temporal correlation (LTC) information was introduced to further improve the covariance matrices estimation (LTCCSP). Compared to the Euclidean distance used in a previous CSP variant named local temporal CSP (LTCSP), the correlation may be a more reasonable metric to measure the similarity of activated spatial patterns existing in motor imagery period. Numerical comparisons among CSP, LTCSP, and LTCCSP were quantitatively conducted on the simulated datasets by adding outliers to Dataset IVa of BCI Competition III and Dataset IIa of BCI Competition IV, respectively. Results showed that LTCCSP achieves the highest average classification accuracies in all the outliers occurrence frequencies. The application of the three methods to the EEG dataset recorded in our laboratory also demonstrated that LTCCSP achieves the highest average accuracy. The above results consistently indicate that LTCCSP would be a promising method for practical motor imagery BCI application. 1. Introduction Brain-computer interfaces (BCI) use electroencephalographic signals or other electrophysiological measures of brain activity to provide a new nonmuscular channel for sending messages and commands to the external world. According to the different electrophysiological signals which they use, BCI can fall into 6 groups [1]: visual evoked potentials (VEP) based BCI [2]; slow cortical potentials (SCP) based BCI [3]; evoked potentials P300 based BCI [4]; mu and beta rhythms (ERD/ERS) based BCI [5], cortical neuronal action potentials based BCI [6] and hybrid BCI [7]. Among them, ERD/ERS based BCI has received a lot of attentions in recent years due to its potential application in motor rehabilitation and its assisting for the motor function impaired patients [8–10]. Feature extraction and classification algorithms play important roles for the performance of ERD/ERS based BCI, and there are various methods have been proposed to extract ERD/ERS related features [11, 12], such as the laplacian method [13], autoregressive spectral analysis [14], common spatial pattern (CSP) [15], discriminative spatial patterns [16], bispectrum analysis [17], and multivariate empirical mode decomposition [18]. Currently, CSP is one of the most popular feature extraction methods for ERD/ERS based BCI, its efficiencies have been proved
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