The parameter setting of functional electrical stimulation (FES) is important for active recovery training since it affects muscle health. Among the FES parameters, current amplitude is the most influential factor. To explore the FES effect on the maximum stimulation time, this study establishes a curve between FES current amplitude and the maximum stimulation time based on muscle fatigue. We collect 10 subjects’ surface electromyography under dumbbell weightlifting training and analyze the muscle fatigue state by calculating the root mean square (RMS) of power. By analyzing signal RMS, the fatigue characteristic curves under different fatigue levels are obtained. According to the muscle response under FES, the relationship curve between the current amplitude and the maximum stimulation time is established and FES parameters’ effect on the maximum stimulation time is obtained. The linear curve provides a reference for FES parameter setting, which can help to set stimulation time safely, thus preventing the muscles from entering an excessive fatigue state and becoming more active to muscle recovery training.

Invasive techniques are becoming increasingly important in the presurgical evaluation of epilepsy. Adopting the electrophysiological source imaging (ESI) of interictal scalp electroencephalography (EEG) to localize the epileptogenic zone remains a challenge. The accuracy of the preoperative localization of the epileptogenic zone is key to curing epilepsy. The T1 MRI and the boundary element method were used to build the realistic head model. To solve the inverse problem, the distributed inverse solution and equivalent current dipole (ECD) methods were employed to locate the epileptogenic zone. Furthermore, a combination of inverse solution algorithms and Granger causality connectivity measures was evaluated. The ECD method exhibited excellent focalization in lateralization and localization, achieving a coincidence rate of 99.02% (p<0.05) with the stereo electroencephalogram. The combination of ECD and the directed transfer function led to excellent matching between the information flow obtained from intracranial and scalp EEG recordings. The ECD inverse solution method showed the highest performance and could extract the discharge information at the cortex level from noninvasive low-density EEG data. Thus, the accurate preoperative localization of the epileptogenic zone could reduce the number of intracranial electrode implantations required.