Earlier studies showed that external focusing enhances motor performance and reduces muscular activity compare to internal one. However, low activity is not always desired especially in case of Human-Machine Interface applications. This study is based on investigating the effects of attentional focusing preferences on EMG based control systems. For the EMG measurements via biceps brachii muscles, 35 subjects were asked to perform weight-lifting under control, external and internal focus conditions. The difference between external and internal focusing was found to be significant and internal focus enabled higher EMG activity. Besides, six statistical features, namely, RMS, maximum, minimum, mean, standard deviation, and variance were extracted from both time and frequency domains to be used as inputs for Artificial Neural Network classifiers. The results found to be 87.54% for ANN1 and 82.69% for ANN2, respectively. These findings showed that one’s focus of attention would be predicted during the performance and unlike the literature, internal focusing could be also useful when it is used as an input for HMI studies. Therefore, attentional focusing might be an important strategy not only for performance improvement to human movement but also for advancing the study of EMG-based control mechanisms.

中文翻译：

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注意聚焦策略对基于 EMG 的分类的影响

早期的研究表明，与内部聚焦相比，外部聚焦可增强运动表现并减少肌肉活动。然而，低活性并不总是需要的，尤其是在人机界面应用的情况下。本研究基于调查注意力集中偏好对基于 EMG 的控制系统的影响。对于通过肱二头肌进行 EMG 测量，35 名受试者被要求在控制、外部和内部聚焦条件下进行举重。发现外部聚焦和内部聚焦之间的差异是显着的，并且内部聚焦能够实现更高的 EMG 活动。此外，从时域和频域中提取了六个统计特征，即RMS、最大值、最小值、平均值、标准偏差和方差，用作人工神经网络分类器的输入。结果发现，ANN1 的结果分别为 87.54% 和 ANN2 的 82.69%。这些发现表明，在表演期间可以预测一个人的注意力焦点，与文献不同的是，内部聚焦在用作 HMI 研究的输入时也可能有用。因此，注意力集中可能是一种重要的策略，不仅可以提高人体运动的性能，而且可以促进基于 EMG 的控制机制的研究。