Power lab daq

The neural signals were continuously recorded from the electrodes inserted and sampled at 20 kHz (Power Lab DAQ; AD Instruments) amplified by a gain of 100 and band pass filtered at 0.1 Hz – 5 kHz (Model 1700, A-M systems). The neural activity from the hippocampus was analyzed using MATLAB (Mathworks) to extract the interictal spikes, seizure events, and theta waves. The seizure events were continuous spikes with frequency content more than 5 Hz for a duration of >5 s (Couturier and Durand, 2018 (link); Nissinen et al., 2000 (link)). The amplitude and duration of these seizure events were measured using the Root Mean Square (RMS) value with the help of an envelope method. The interictal spikes were paroxysmal events induced before and after the seizure events and were present constantly during the entire course of the experiment. The amplitude and duration of the spikes were calculated using the peak-to-peak value and full width at half height respectively. The theta periods were observed occasionally between these events; a band pass filter at 4–12 Hz was used to extract these theta waves. Cross-correlation and Phase Difference were used to calculate the speed of propagation of the acquired activity.

EEG recordings were sampled at 100kHz (PowerLab DAQ, AD Instruments Inc, Colorado Springs, CO USA) and amplified by 100. Recordings were taken continuously over the course of the experiment in order to monitor seizures in the cortex and secondarily in the hippocampus. Seizures were identified according to a metric established by Nissinen et al where in the EEG segment must be double the amplitude of the baseline amplitude, the majority of the frequency content must be at least 5Hz, and this activity must continue for longer than 5 seconds^{26 (link)}. Seizure onset was identified as the time at which the first spike from the first segment of EEG activity satisfying these criteria occurred. The duration of each of these seizures was recorded for purposes of comparison across experimental periods. Periods of time during stimulation were cleaned of stimulation artifacts by applying template subtraction and a median filter. The artifact removal did not affect seizure identification as can be seen in supplemental figure 2.