The phase-locking relationship between the firings of neuronal action potentials (i.e., spikes) and the oscillations of local field potentials (LFP) reflects important neural coding information. However, the present analysis methods can only determine whether there has phase-locking, but not the different strengths among various types of phase-locking. In the present paper, we used spike-triggered average (STA) signals and the percentage ratio (named φ) of the STA power to the power of original LFP as an index to evaluate the strengths of phase-locking. Experimental recordings obtained from rat hippocampal CA1 region as well as simulation data were used to evaluate the method. The results showed that the index φ changed monotonically as a function of the strength of phase-locking, and it could provide an effective critical value to divide phase-locking from non-phase-locking. Because the calculation of the index does not need pre-filtering, it can avoid the unwanted influences caused by intentionally limiting the frequencies of LFP oscillations such as in the traditional bin statistical method. Therefore, the index φ provides a novel method to investigate the mechanisms underlying neuronal coding in brain.
In order to investigate the effect of deep brain stimulation on diseases such as epilepsy, we developed a closed-loop electrical stimulation system using LabVIEW virtual instrument environment and NI data acquisition card. The system was used to detect electrical signals of epileptic seizures automatically and to generate electrical stimuli. We designed a novel automatic detection algorithm of epileptic seizures by combining three features of field potentials: the amplitude, slope and coastline index. Experimental results of rat epileptic model in the hippocampal region showed that the system was able to detect epileptic seizures with an accuracy rate 91.3% and false rate 8.0%. Furthermore, the on-line high frequency electrical stimuli showed a suppression effect on seizures. In addition, the system was adaptive and flexible with multiple work modes, such as automatic and manual modes. Moreover, the simple time-domain algorithm of seizure detection guaranteed the real-time feature of the system and provided an easy-to-use equipment for the experiment researches of epilepsy control by electrical stimulation.