Objective . Hand movement is a crucial function for humans’ daily life. Developing brain-machine interface (BMI) to control a robotic hand by brain signals would help the severely paralyzed people partially regain the functional independence. Previous intracranial electroencephalography (iEEG)-based BMIs towards gesture decoding mostly used neural signals from the primary sensorimotor cortex while ignoring the hand movement related signals from posterior parietal cortex (PPC). Here, we propose combining iEEG recordings from PPC with that from primary sensorimotor cortex to enhance the gesture decoding performance of iEEG-based BMI. Approach . Stereoelectroencephalography (SEEG) signals from 25 epilepsy subjects were recorded when they performed a three-class hand gesture task. Across all 25 subjects, we identified 524, 114 and 221 electrodes from three regions of interest (ROIs), including PPC, postcentral cortex (POC) and precentral cortex (PRC), respectively. Base...

中文翻译：

---

使用来自后顶叶皮层的信号增强手势解码性能：立体脑电图 (SEEG) 研究。

客观的 。手部运动是人类日常生活的重要功能。开发脑机接口 (BMI) 以通过大脑信号控制机械手将帮助严重瘫痪的人部分恢复功能独立。以前基于颅内脑电图 (iEEG) 的 BMI 用于手势解码主要使用来自初级感觉运动皮层的神经信号，而忽略了来自后顶叶皮层 (PPC) 的手部运动相关信号。在这里，我们建议将来自 PPC 的 iEEG 记录与来自初级感觉运动皮层的 iEEG 记录相结合，以增强基于 iEEG 的 BMI 的手势解码性能。方法 。25 名癫痫受试者在执行三类手势任务时记录了立体脑电图 (SEEG) 信号。在所有 25 个受试者中，我们确定了 524 个，来自三个感兴趣区域 (ROI) 的 114 和 221 个电极，分别包括 PPC、中央后皮质 (POC) 和中央前皮质 (PRC)。根据...