Electrocorticographic signal classification based on time-frequency decomposition and nonparametric statistical modeling.

In this paper, we propose a novel statistical framework based on time-frequency decomposition and nonparametric modelling of electrocortical (ECoG) signals in the context of a Brain Computer Interface. The proposed method decomposes the ECoG signals into subbands (with no down-sampling) using Gabor filters. The subband signals are then encoded using a nonparametric statistical modeling and the distance between the resulting empirical distributions is as used as the classification criterion. Cross-validation experiments were carried out to pre-select the channel (from the multi-channel sources) and subbands which can archive the best classification scores. The proposed framework has been evaluated using Data Set I from the BCI Competition III and results indicate a superiority over conventional vector quantization method particularly when the number of training samples is small. It was found that the proposed nonparametric distribution modeling based on empirical inverse cumulative distribution distance is fast, robust and applicable to the mobile systems.