Exploring the Visual Guidance of Motor Imagery in Sustainable Brain–Computer Interfaces

Yang, Cheng; Kong, Lei; Zhang, Zhichao; Tao, Ye; Chen, Xiaoyu

doi:10.3390/su142113844

Cited by 2 publications

(1 citation statement)

References 51 publications

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“…They are different in timings, movements that are imagined differ (movements right and up for Dataset 1 and elbow extension and flexion for Dataset 2) and paradigm of Dataset 1 contains vibrotactile guidance on certain trials, which kept the participants more engaged with the task. Another reason could be the positive effect of visual guidance (Dataset 1) in comparison to visual cue only (Dataset 2) [ 49 ]. One more reason for different performance could be the different electrode positions availability described in “ Dataset 2 ” section.…”

Section: Resultsmentioning

confidence: 99%

Detection of motor imagery based on short-term entropy of time–frequency representations

2023

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Background Motor imagery is a cognitive process of imagining a performance of a motor task without employing the actual movement of muscles. It is often used in rehabilitation and utilized in assistive technologies to control a brain–computer interface (BCI). This paper provides a comparison of different time–frequency representations (TFR) and their Rényi and Shannon entropies for sensorimotor rhythm (SMR) based motor imagery control signals in electroencephalographic (EEG) data. The motor imagery task was guided by visual guidance, visual and vibrotactile (somatosensory) guidance or visual cue only. Results When using TFR-based entropy features as an input for classification of different interaction intentions, higher accuracies were achieved (up to 99.87%) in comparison to regular time-series amplitude features (for which accuracy was up to 85.91%), which is an increase when compared to existing methods. In particular, the highest accuracy was achieved for the classification of the motor imagery versus the baseline (rest state) when using Shannon entropy with Reassigned Pseudo Wigner–Ville time–frequency representation. Conclusions Our findings suggest that the quantity of useful classifiable motor imagery information (entropy output) changes during the period of motor imagery in comparison to baseline period; as a result, there is an increase in the accuracy and F1 score of classification when using entropy features in comparison to the accuracy and the F1 of classification when using amplitude features, hence, it is manifested as an improvement of the ability to detect motor imagery.

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Section: Resultsmentioning

confidence: 99%

Detection of motor imagery based on short-term entropy of time–frequency representations

2023

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Motor Imagery Classification Based on EEG Sensing with Visual and Vibrotactile Guidance

Batistić

Sušanj

Pinčić

et al. 2023

Sensors

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Motor imagery (MI) is a technique of imagining the performance of a motor task without actually using the muscles. When employed in a brain–computer interface (BCI) supported by electroencephalographic (EEG) sensors, it can be used as a successful method of human–computer interaction. In this paper, the performance of six different classifiers, namely linear discriminant analysis (LDA), support vector machine (SVM), random forest (RF), and three classifiers from the family of convolutional neural networks (CNN), is evaluated using EEG MI datasets. The study investigates the effectiveness of these classifiers on MI, guided by a static visual cue, dynamic visual guidance, and a combination of dynamic visual and vibrotactile (somatosensory) guidance. The effect of filtering passband during data preprocessing was also investigated. The results show that the ResNet-based CNN significantly outperforms the competing classifiers on both vibrotactile and visually guided data when detecting different directions of MI. Preprocessing the data using low-frequency signal features proves to be a better solution to achieve higher classification accuracy. It has also been shown that vibrotactile guidance has a significant impact on classification accuracy, with the associated improvement particularly evident for architecturally simpler classifiers. These findings have important implications for the development of EEG-based BCIs, as they provide valuable insight into the suitability of different classifiers for different contexts of use.

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Exploring the Visual Guidance of Motor Imagery in Sustainable Brain–Computer Interfaces

Cited by 2 publications

References 51 publications

Detection of motor imagery based on short-term entropy of time–frequency representations

Detection of motor imagery based on short-term entropy of time–frequency representations

Motor Imagery Classification Based on EEG Sensing with Visual and Vibrotactile Guidance

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