Implanted intra-cortical micro-electrode arrays record multi-unit extracellular spike activity that is used in deciphering the neural basis for adaptation, learning, plasticity and as command signal for brain-machine interfaces (BMI). Detection of spike activity is the first step in successful implementation of all the aforementioned applications. However, with awake and behaving animals, micro-electrode arrays typically also record non-neuronal signals induced by the animal's movement, feeding and grooming actions. The spectral and temporal nature of these artifacts is similar to neural spikes, which complicates accurate detection. The distal source and higher strength of non-neuronal signals result in their near simultaneous registration on most electrodes, while neural spiking event is rarely recorded on more than one electrode of an array. This difference is utilized in identifying non-neuronal content from acquired data by performing a correlation analysis. The efficacy of the method is evaluated by comparing outcomes from algorithms that use absolute threshold and Principal Component Analysis (PCA) as a means of identifying neural spikes with the same methods incorporating correlation analysis.
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