Selection of cortical neurons for identifying movement transitions in stand and squat

Neural signals collected from motor cortex were quantified for identification of subject's specific movement intentions in a Brain Machine Interface (BMI). Neuron selection serves as an important procedure in this decoding process. In this study, we proposed a neuron selection method for identifying movement transitions in standing and squatting tasks by analyzing cortical neuron spike train patterns. A nonparametric analysis of variation, Kruskal-Wallis test, was introduced to evaluate whether the average discharging rate of each neuron changed significantly among different motion stages, and thereby categorize the neurons according to their active periods. Selection was performed based on neuron categorizing information. Finally, the average firing rates of selected neurons were assembled as feature vectors and a classifier based on support vector machines (SVM) was employed to discriminate different movement stages and identify transitions. The results indicate that our neuron selection method is accurate and efficient for finding neurons correlated with movement transitions in standing and squatting tasks.