The effect of attentional focusing strategies on EMG-based classification
-
Ayse Nur Ay
and
Mustafa Zahid Yildiz
Abstract
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.
Acknowledgments
The authors thank to the students of Sakarya University of Applied Sciences for their participation to the experiments.
Research funding: Authors state no funding involved.
Author contributions: All authors have accepted responsibility for the entire content of this manuscript and approved its submission.
Conflict of interest: Authors state no conflict of interest.
Informed consent: Informed consent was obtained from all individuals included in this study.
References
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© 2020 Walter de Gruyter GmbH, Berlin/Boston
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Articles in the same Issue
- Frontmatter
- Research Articles
- Nonlinear analysis of scalp EEGs from normal and brain tumour subjects
- Dimensionality reduction for EEG-based sleep stage detection: comparison of autoencoders, principal component analysis and factor analysis
- EEG signal classification based on SVM with improved squirrel search algorithm
- The effect of attentional focusing strategies on EMG-based classification
- Identification of dental pain sensation based on cardiorespiratory signals
- ScatT-LOOP: scattering tetrolet-LOOP descriptor and optimized NN for iris recognition at-a-distance
- A detailed and comparative work for retinal vessel segmentation based on the most effective heuristic approaches
- Visual enhancement of brain cancer MRI using multiscale dyadic filter and Hilbert transformation
- Raspberry Pi implemented with MATLAB simulation and communication of physiological signal-based fast chaff point (RPSC) generation algorithm for WBAN systems
- Short Communication
- How yarn orientation limits fibrotic tissue ingrowth in a woven polyester heart valve scaffold: a case report
