2016 Volume 28 Issue 3 Pages 655-665
To elucidate higher order brain function, it is important to analyze spatio-temporal patterns of electrical activity of neuronal network. For this purpose, electrical activity in neuronal networks measured from dissociated neuronal culture system or acute slice of brain are widely studied. In such studies, accurate detection of neuronal electrical spikes from measured electrical-potential-data including noises is critically required. In addition, the on-line spike detection from the measured electrical signals is preferred to offline detection, especially for the application of the neuronal spike detection in brain-machine interface.
In this study, we developed the novel and simple threshold-based-algorithm to detect neuronal electrical spikes, determining adequate threshold for spike detection, even though the frequency of spontaneous spikes and noises drastically changes. Using this novel method, numbers of detected spikes were improved to 96.4\% of correct number, while number of detected spikes were 91.8\% of correct number with previous method, suggesting that number of lost neuronalelectrical spikes decreased. In addition, we developed software to simultaneously perform the on-line spike detection and the recording electrical signals from 64-electrodes, which is convenient to control electrical devices according to the electrical activity of the neuronal network.
