Individuals with severe neuromuscular ailments can benefit from restoring their grasp activities with a brain-controlled upper-limb neuroprosthesis. EEG signals can be utilized as the driving source, and to implement natural human-like grasping abilities. Although good accuracy has already been achieved in classifying the various grasp patterns for specific sets of objects, unseen objects are still a hurdle in real-life implementation. Generalization of grasp patterns should be explored without any prior knowledge of the objects. In this regard, the similarity of motor imagery for different objects requiring similar grasp pattern can be utilized. It is also necessary to identify the brain regions that exhibit prominent distinguishability during different grasp patterns. In this study, we propose a chaos-based method to decode the motor imagery of two quite similar Power grasp patterns-cylindrical and spherical-for holding various objects. Three distinct suitable objects were chosen for each of the two patterns, and a 29-channel EEG was taken of 18 healthy participants to explore motor imagery for grasping the objects. Nonlinear correlation dimension was employed on the EEG data, at sub-band levels $\alpha$, upper $\beta$, and $\gamma$, to analyse the distinguishability, as well as the similarity of grasp patterns for the objects. ANOVA was subsequently performed on the obtained CD parameters to identify the contribution of each electrode channel. Furthermore, using an SVM classifier, more than 80% accuracy was obtained in classifying the grasping patterns at the upper $\beta$ sub-band. The outcome may lead to identification of optimum feature sets of motor imagery from specific brain regions for random objects grasps.

患有严重神经肌肉疾病的个体可以通过使用大脑控制的上肢神经假体来恢复抓地力。脑电信号可以用作驱动源，并实现类似人的自然抓握能力。尽管在对特定对象集的各种抓取模式进行分类方面已经实现了良好的准确性，但是看不见的对象仍然是现实生活中的障碍。在没有对象的任何先验知识的情况下，应该探索掌握模式的一般性。在这方面，可以利用针对需要相似抓握模式的不同对象的运动图像的相似性。还需要确定在不同抓握模式下表现出显着可分辨性的大脑区域。在这个研究中，我们提出了一种基于混沌的方法来解码两个非常相似的Power抓取模式（圆柱形和球形）的运动图像，以容纳各种物体。为这两种模式中的每一个选择了三个不同的合适对象，并从18位健康参与者那里获取了29通道的脑电图，以探索运动图像以掌握这些对象。在脑电图数据上采用子带级的非线性相关维$\alpha$，上 $\beta$和 $\gamma$，以分析对象的抓取模式的可区分性和相似性。随后对获得的CD参数执行方差分析，以识别每个电极通道的贡献。此外，使用SVM分类器，在对上方抓握模式进行分类时获得了80％以上的准确性$\beta$子带。结果可能会导致从特定的大脑区域识别出针对随机物体抓握的最佳运动图像特征集。