Recognition of various tactile stimuli using independent component analysis and k-means

A self-developed integrated system is employed to record and analyse intracortical evoked potentials from the primary somatosensory cortex of rats. Four different neural signals are recorded under no stimulation and stimulation using a toothbrush, pen shaft and toothpick separately. These evoked signals undergo preprocessing and post-processing, in that order. In order to improve the shortcoming of independent component analysis (ICA), which the magnitude and sequence of estimated independent components are ambiguous. The authors propose the dynamic dimension increasing method to form a feature vector by correlation coefficient matrix and mitigate the drawback of ICA. Then, k-means is employed to group the feature vector into different clusters. The authors use the information of monitoring subsystem to check the experimental results by using a video recording device. Finally, the presented methods are utilised to extract the features from various evoked potentials and distinguish the stimulants from different sensory signals.