scholarly journals Visual-Electrotactile Stimulation Feedback to Improve Immersive Brain-Computer Interface Based on Hand Motor Imagery

2021 ◽  
Vol 2021 ◽  
pp. 1-13
Author(s):  
David Achanccaray ◽  
Shin-Ichi Izumi ◽  
Mitsuhiro Hayashibe
Keyword(s):  
Motor Imagery ◽  
Visual Feedback ◽  
Neural Plasticity ◽  
Classification Accuracy ◽  
Premotor Cortex ◽  
Mean Power ◽  
Electrotactile Stimulation ◽  
Power Spectral ◽  
Flexion Extension ◽  
Brain Rhythm

In the aging society, the number of people suffering from vascular disorders is rapidly increasing and has become a social problem. The death rate due to stroke, which is the second leading cause of global mortality, has increased by 40% in the last two decades. Stroke can also cause paralysis. Of late, brain-computer interfaces (BCIs) have been garnering attention in the rehabilitation field as assistive technology. A BCI for the motor rehabilitation of patients with paralysis promotes neural plasticity, when subjects perform motor imagery (MI). Feedback, such as visual and proprioceptive, influences brain rhythm modulation to contribute to MI learning and motor function restoration. Also, virtual reality (VR) can provide powerful graphical options to enhance feedback visualization. This work aimed to improve immersive VR-BCI based on hand MI, using visual-electrotactile stimulation feedback instead of visual feedback. The MI tasks include grasping, flexion/extension, and their random combination. Moreover, the subjects answered a system perception questionnaire after the experiments. The proposed system was evaluated with twenty able-bodied subjects. Visual-electrotactile feedback improved the mean classification accuracy for the grasping (93.00%  ±  3.50%) and flexion/extension (95.00%  ±  5.27%) MI tasks. Additionally, the subjects achieved an acceptable mean classification accuracy (maximum of 86.5%  ±  5.80%) for the random MI task, which required more concentration. The proprioceptive feedback maintained lower mean power spectral density in all channels and higher attention levels than those of visual feedback during the test trials for the grasping and flexion/extension MI tasks. Also, this feedback generated greater relative power in the μ -band for the premotor cortex, which indicated better MI preparation. Thus, electrotactile stimulation along with visual feedback enhanced the immersive VR-BCI classification accuracy by 5.5% and 4.5% for the grasping and flexion/extension MI tasks, respectively, retained the subject’s attention, and eased MI better than visual feedback alone.

scholarly journals Insights into the combination of neuromuscular electrical stimulation and motor imagery in a training-based approach

2021 ◽  
Author(s):  
Amandine Bouguetoch ◽  
Alain Martin ◽  
Sidney Grosprêtre
Keyword(s):  
Electrical Stimulation ◽  
Motor Imagery ◽  
Neural Plasticity ◽  
Neuromuscular Electrical Stimulation ◽  
Voluntary Contraction ◽  
Muscle Architecture ◽  
Fascicle Length ◽  
Before And After ◽  
V Wave ◽  
And Control

Abstract Introduction Training stimuli that partially activate the neuromuscular system, such as motor imagery (MI) or neuromuscular electrical stimulation (NMES), have been previously shown as efficient tools to induce strength gains. Here the efficacy of MI, NMES or NMES + MI trainings has been compared. Methods Thirty-seven participants were enrolled in a training program of ten sessions in 2 weeks targeting plantar flexor muscles, distributed in four groups: MI, NMES, NMES + MI and control. Each group underwent forty contractions in each session, NMES + MI group doing 20 contractions of each modality. Before and after, the neuromuscular function was tested through the recording of maximal voluntary contraction (MVC), but also electrophysiological and mechanical responses associated with electrical nerve stimulation. Muscle architecture was assessed by ultrasonography. Results MVC increased by 11.3 ± 3.5% in NMES group, by 13.8 ± 5.6% in MI, while unchanged for NMES + MI and control. During MVC, a significant increase in V-wave without associated changes in superimposed H-reflex has been observed for NMES and MI, suggesting that neural adaptations occurred at supraspinal level. Rest spinal excitability was increased in the MI group while decreased in the NMES group. No change in muscle architecture (pennation angle, fascicle length) has been found in any group but muscular peak twitch and soleus maximal M-wave increased in the NMES group only. Conclusion Finally, MI and NMES seem to be efficient stimuli to improve strength, although both exhibited different and specific neural plasticity. On its side, NMES + MI combination did not provide the expected gains, suggesting that their effects are not simply cumulative, or even are competitive.

scholarly journals Intermittent Visuomotor Processing in the Human Cerebellum, Parietal Cortex, and Premotor Cortex

2006 ◽  
Vol 95 (2) ◽  
pp. 922-931 ◽  
Author(s):  
David E. Vaillancourt ◽  
Mary A. Mayka ◽  
Daniel M. Corcos
Keyword(s):  
Parietal Cortex ◽  
Visual Feedback ◽  
Visual Information ◽  
Grip Force ◽  
Premotor Cortex ◽  
Force Variability ◽  
Neural Activation ◽  
Control Force ◽  
Force Condition ◽  
Visuomotor Processing

The cerebellum, parietal cortex, and premotor cortex are integral to visuomotor processing. The parameters of visual information that modulate their role in visuomotor control are less clear. From motor psychophysics, the relation between the frequency of visual feedback and force variability has been identified as nonlinear. Thus we hypothesized that visual feedback frequency will differentially modulate the neural activation in the cerebellum, parietal cortex, and premotor cortex related to visuomotor processing. We used functional magnetic resonance imaging at 3 Tesla to examine visually guided grip force control under frequent and infrequent visual feedback conditions. Control conditions with intermittent visual feedback alone and a control force condition without visual feedback were examined. As expected, force variability was reduced in the frequent compared with the infrequent condition. Three novel findings were identified. First, infrequent (0.4 Hz) visual feedback did not result in visuomotor activation in lateral cerebellum (lobule VI/Crus I), whereas frequent (25 Hz) intermittent visual feedback did. This is in contrast to the anterior intermediate cerebellum (lobule V/VI), which was consistently active across all force conditions compared with rest. Second, confirming previous observations, the parietal and premotor cortices were active during grip force with frequent visual feedback. The novel finding was that the parietal and premotor cortex were also active during grip force with infrequent visual feedback. Third, right inferior parietal lobule, dorsal premotor cortex, and ventral premotor cortex had greater activation in the frequent compared with the infrequent grip force condition. These findings demonstrate that the frequency of visual information reduces motor error and differentially modulates the neural activation related to visuomotor processing in the cerebellum, parietal cortex, and premotor cortex.

scholarly journals CNN-based Framework using Spatial Dropping for Enhanced Interpretation of Neural Activity in Motor Imagery Classification

2020 ◽  
Author(s):  
Diego Fabian Collazos Huertas ◽  
Andres Marino Alvarez Meza ◽  
German Castellanos Dominguez
Keyword(s):  
Wavelet Transform ◽  
Motor Imagery ◽  
Continuous Wavelet Transform ◽  
Brain Activity ◽  
Machine Learning Techniques ◽  
Continuous Wavelet ◽  
Brain Response ◽  
Power Spectral ◽  
Learning Techniques ◽  
Eeg Data

Abstract Interpretation of brain activity responses using Motor Imagery (MI) paradigms is vital for medical diagnosis and monitoring. Assessed by machine learning techniques, identification of imagined actions is hindered by substantial intra and inter subject variability. Here, we develop an architecture of Convolutional Neural Networks (CNN) with enhanced interpretation of the spatial brain neural patterns that mainly contribute to the classification of MI tasks. Two methods of 2D-feature extraction from EEG data are contrasted: Power Spectral Density and Continuous Wavelet Transform. For preserving the spatial interpretation of extracting EEG patterns, we project the multi-channel data using a topographic interpolation. Besides, we include a spatial dropping algorithm to remove the learned weights that reflect the localities not engaged with the elicited brain response. Obtained results in a bi-task MI database show that the thresholding strategy in combination with Continuous Wavelet Transform improves the accuracy and enhances the interpretability of CNN architecture, showing that the highest contribution clusters over the sensorimotor cortex with differentiated behavior between μ and β rhythms.

Signal Processing and Classification for Electroencephalography Based Motor Imagery Brain Computer Interface

2021 ◽  
Vol 11 (12) ◽  
pp. 2918-2927
Author(s):  
A. Shankar ◽  
S. Muttan ◽  
D. Vaithiyanathan
Keyword(s):  
Neural Network ◽  
Signal Processing ◽  
Motor Imagery ◽  
Classification Accuracy ◽  
Brain Computer Interface ◽  
Computer Interface ◽  
Discrete Wavelet ◽  
Feature Sets ◽  
Evaluation Of Performance ◽  
The Brain

Brain Computer Interface (BCI) is a fast growing area of research to enable communication between our brains and computers. EEG based motor imagery BCI involves the user imagining movement, the subsequent recording and signal processing on the electroencephalogram signals from the brain, and the translation of those signals into specific commands. Ultimately, motor imagery BCI has the potential to be applied to helping those with special abilities recover motor control. This paper presents an evaluation of performance for EEG based motor imagery BCI with a classification accuracy of 80.2%, making use of features extracted using the Fast Fourier Transform and the Discrete Wavelet Transform, and classification is done using an Artificial Neural Network. It goes on to conclude how the performance is affected by the particular feature sets and neural network parameters.

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