癲癇是一種大腦異常放電引起的神經系統疾病，其中部分患者為雙側內側顳葉癲癇，其兩側顳葉區域皆可能產生異常放電，導致兩側腦區之間產生互相影響的繼發性反應。現有的藥物及手術切除治療仍難以完全根治，而神經電刺激提供了另一種可行的治療方式，其中閉迴路深腦電刺激能根據大腦的癲癇狀態調整最佳的電刺激強度並施加至目標區域，進而調節神經放電活動。為了抑制癲癇狀態，過去已有研究提出比例積分微分控制器、模糊控制以及模型預測控制等，相較於這些傳統的線性或單輸入單輸出的控制方法，非線性自我迴歸移動平均沃爾泰拉模型適用於構建神經系統或顱內腦電圖訊號之間的多變數非線性動態系統模型。與過去研究僅局限於特定腦區異常放電的局部性癲癇不同，透過結合兩個神經群模型模擬左右腦區的相互作用，可用以模擬雙側內側顳葉癲癇狀態並利用模型預測控制進行多變數的同步抑制與降低能量消耗。本研究旨在抑制由兩個神經群模型的相互作用模擬所產生的雙側內側顳葉癲癇，結合模型預測控制與非線性自我迴歸移動平均沃爾泰拉模型，擬合出多變數輸入輸出的非線性癲癇動態，並進行多變數狀態控制，以達到抑制雙側內側顳葉癲癇的效果，並使用平均功率評估控制表現。在模型訓練上，利用了Laguerre展開法來降低約93.64%參數計算數量，在不增加參數數量的情況下，將原本較長的記憶長度用更少的係數來描述或計算，且不影響其預測效果。隨後在抑制結果上，模型預測控制在兩腦區平均抑制時間分別為0.32s與0.37s，能量消耗上兩腦區平均功率分別為的1258mV^2以及1044mV^2，模型預測控制與開迴路電刺激相比，不僅能更快地將癲癇抑制，也大幅降低約92%與93%能量的消耗。雖然本研究仍需調整以符合臨床條件，但其在非線性動態建模與模型預測控制上的成果，為未來雙側癲癇電刺激治療的優化與臨床實作提供了可行的發展方向。

Epilepsy is a neurological disorder caused by abnormal neuronal discharges in the brain. Some patients suffer from bilateral mesial temporal lobe epilepsy (BMTLE), in which abnormal discharges may occur in both temporal lobes, leading to secondary responses due to interactions between the two brain regions. Currently, treatment options such as antiepileptic medications and surgical resection still fail to achieve complete remission in many patients, whereas neurostimulation has emerged as a feasible alternative therapeutic approach. In particular, closed-loop deep brain stimulation can adaptively adjust the optimal stimulation intensity according to the epileptic state of the brain, thereby modulating neuronal firing activity. To suppress epileptic states, nonlinear autoregressive moving-average Volterra model (NARMA Volterra model) are more suitable for constructing multivariate nonlinear dynamical models of neural systems or intracranial electroencephalography (iEEG) signals than conventional linear or single-input single-output control methods. Previous studies have primarily focused on focal seizures involving abnormal discharges in a single brain region. In contrast, this study employs the interaction of two neural mass model (NMM) to simulate BMTLE and applies model predictive control (MPC) to achieve multivariate synchronous suppression. The objective of this study is to suppress BMTLE simulated by the interaction of two NMMs. By integrating MPC with the NARMA Volterra model, we approximate multivariate input-output nonlinear epileptic dynamics and perform multivariate state control, thereby achieving suppression of BMTLE while reducing energy consumption. Control performance is evaluated using average power consumption as the performance metric.

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