中文摘要：

表面阵列电极在改进刺激选择性和控制能力方面具有优越性能，其中电极设计和刺激波形对于神经肌肉电刺激效果具有重要影响。针对手功能康复需求，采用大小电极触点交叉排列的表面阵列电极，对人体前臂组织实施直流阴极刺激；基于人体前臂的简化层次模型，通过有限元法仿真人体前臂组织的电场分布，使用神经纤维激励函数表征外加电场对神经轴突电活动的影响。在此基础上，选择前臂深层神经纤维激励函数的峰值和半宽度乘积之比作为靶向性能评价函数，利用粒子群算法对表面阵列电极的触点尺寸和间距进行优化设计。结果发现，当大、小触点尺寸分别为9．80和10．72mm时，阵列电极的靶向性能最优，靶向性能评价函数最大值为11252．68V／m4。对比不同刺激波形作用下随时间变化的人体前臂深层神经纤维激励函数最大值，发现矩形波刺激下神经纤维激励函数最大值可达3．448V／m2，稍高于其他刺激波形，有利于神经纤维的激活，从而为表面阵列电极设计和制定刺激处方提供理论指导。

英文摘要：

Surface array electrode （SAE） provides high performance, which improves the stimulation selectivity and controllability. The electrode design and stimulating waveform have a marked effect on the performance of functional neuromuscular stimulation （FNS）. For motor rehabilitation of hand, the simplified forearm model with different layers was established in this paper, and finite element method （FEM） was used to simulate the distribution of electric fields within the forearm while a SAE with all large contacts was separated by small ones outputs direct current signals to achieve cathodic stimulation. Moreover, the activation function （AF） indicates the effect of applied electric field on the activity of the nerve axon, and then the ratio of the maximum to the multiplication of half-widths of AF for the deep axon in the forearm was used to evaluate the stimulation selectivity. In order to achieve a perfect performance, the particle swarm optimization （PSO） algorithm was used to optimize the design of SAE. Results show that the optimal performance is achieved for SAE when the sizes of large square contacts and small ones are 9. 80 mmx 9.80 mm and 10. 72 mmx 10.72 ram, respeetirely, while the maximum of performance index for simulation selectivity is 11 252. 68 V/m4. Inaddition, different stimulation waveforms are investigated based on the time-varying maximum of AF for the deep axon in the forearm. Compared with other stimulation waveforms, the biphasic charge-balanced rectangular pulse produced a slightly larger maximum of AF, i.e. 3. 448 V/m2, which has a positive effect on the activation of axons. The FEM simulations provide foundation for the design of SAE and clinical application.