OBJECTIVE In this study, we proposed a state-based probabilistic method for decoding hand positions during unilateral and bilateral movements using the ECoG signals recorded from the brain of Rhesus monkey. APPROACH A customized electrode array was implanted subdurally in the right hemisphere of the brain covering from the primary motor cortex to the frontal cortex. Three different experimental paradigms were considered: ipsilateral, contralateral, and bilateral movements. During unilateral movement, the monkey was trained to get food with one hand, while during bilateral movement, the monkey used its left and right hands alternately to get food. To estimate the hand positions, a state-based probabilistic method was introduced which was based on the conditional probability of the hand movement state (i.e. idle, right hand movement, and left hand movement) and the conditional expectation of the hand position for each state. Moreover, a hybrid feature extraction method based on linear discriminant analysis and partial least squares (PLS) was introduced. MAIN RESULTS The proposed method could successfully decode the hand positions during ipsilateral, contralateral, and bilateral movements and significantly improved the decoding performance compared to the conventional Kalman and PLS regression methods [Formula: see text]. The proposed hybrid feature extraction method was found to outperform both the PLS and PCA methods [Formula: see text]. Investigating the kinematic information of each frequency band shows that more informative frequency bands were [Formula: see text] (15-30 Hz) and [Formula: see text](50-100 Hz) for ipsilateral and [Formula: see text] and [Formula: see text] (100-200 Hz) for contralateral movements. It is observed that ipsilateral movement was decoded better than contralateral movement for [Formula: see text] (5-15 Hz) and [Formula: see text] bands, while contralateral movements was decoded better for [Formula: see text] (30-200 Hz) and hfECoG (200-400 Hz) bands. SIGNIFICANCE Accurate decoding the bilateral movement using the ECoG recorded from one brain hemisphere is an important issue toward real-life applications of the brain-machine interface technologies.

中文翻译：

---

一种基于状态的概率方法，用于从非人类灵长类动物的ECoG信号进行运动期间解码手的位置。

目的在这项研究中，我们提出了一种基于状态的概率方法，该方法使用恒河猴大脑记录的ECoG信号对单侧和双侧运动过程中的手部位置进行解码。方法将定制的电极阵列硬膜下植入大脑右半球，覆盖从原发性运动皮层到额叶皮层。考虑了三种不同的实验范式：同侧，对侧和双侧运动。在单边运动期间，训练猴子用一只手来获取食物，而在双边运动期间，猴子则交替使用其左手和右手来获取食物。为了估算手的位置，引入了一种基于状态的概率方法，该方法基于手运动状态（即，空转，右手运动，和左手移动）以及每种状态下的手位置的条件期望。此外，介绍了一种基于线性判别分析和偏最小二乘（PLS）的混合特征提取方法。主要结果与传统的Kalman和PLS回归方法相比，该方法可以成功地解码同侧，对侧和双侧运动过程中的手部位置，并显着提高了解码性能。发现提出的混合特征提取方法优于PLS和PCA方法[公式：请参见文本]。调查每个频带的运动学信息表明，同侧和[公式：参见文本]的更多信息频带为[公式：参见文本]（15-30 Hz）和[公式：参见文本]（50-100 Hz）。 [式：参见文本]（100-200 Hz）以了解对侧运动。观察到，对于[公式：参见文本]（5-15 Hz）和[公式：参见文本]频段，同侧运动的解码效果优于对侧运动，而对[公式：参见文本]的对侧运动解码效果更好（30- 200 Hz）和hfECoG（200-400 Hz）频段。意义使用从一个大脑半球记录的ECoG准确解码双边运动是脑机接口技术在现实生活中的重要应用。