High frequency electrical stimulation of brain is commonly used in research experiments and clinical trials as a modern tool for control of epileptic seizures. However, the mechanistic basis by which periodic external stimuli alter the brain state is not well understood. This study provides a computational insight into the mechanism of seizure suppression by high frequency stimulation (HFS). In particular, a modified version of the Jansen-Rit neural mass model is employed, in which EEG signals can be considered as the input. The proposed model reproduces seizure-like activity in the output during the ictal period of the input signal. By applying a control signal to the model, a wide range of stimulation amplitudes and frequencies are systematically explored. Simulation results reveal that HFS can effectively suppress the seizure-like activity. Our results suggest that HFS has the ability of shifting the operating state of neural populations away from a critical condition. Furthermore, a closed-loop control strategy is proposed in this paper. The main objective has been to considerably reduce the control effort needed for blocking abnormal activity of the brain. Such an energy reduction could be of practical importance, to reduce possible side effects and increase battery life for implanted neurostimulators.

大脑的高频电刺激通常用于研究实验和临床试验，作为控制癫痫发作的现代工具。然而，周期性外部刺激改变大脑状态的机制基础尚不清楚。本研究提供了对高频刺激 (HFS) 抑制癫痫发作机制的计算洞察力。特别是，采用了 Jansen-Rit 神经质量模型的修改版本，其中 EEG 信号可以被视为输入。所提出的模型在输入信号的发作期再现了输出中的癫痫样活动。通过将控制信号应用于模型，系统地探索了广泛的刺激幅度和频率。模拟结果表明，HFS 可以有效地抑制癫痫样活动。我们的结果表明，HFS 具有将神经群体的运行状态从临界状态转移的能力。此外，本文提出了一种闭环控制策略。主要目标是大大减少阻止大脑异常活动所需的控制工作。这种能量降低可能具有实际意义，以减少可能的副作用并延长植入神经刺激器的电池寿命。