A BCI based visual-haptic neurofeedback training improves cortical activations and classification performance during motor imagery

Wang, Zhongpeng; Zhou, Yijie; Chen, Long; Gu, Bin; Liu, Shuang; Xu, Minpeng; Qi, Hongzhi; He, Feng; Ming, Dong

doi:10.1088/1741-2552/ab377d

Cited by 55 publications

(34 citation statements)

References 43 publications

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“…There are multiple improvements in the field that suggest motor imagery learning among novice BCI users; for example, it has been shown that realistic feedback from humanlike bodies and the feeling of embodiment improves the modulation of brain activity needed for motor imagery (Alimardani et al, 2018;Penaloza et al, 2018;Choi et al, 2020). Furthermore, visual guidance in virtual reality (Liang et al, 2016;Coogan andHe, 2018), gamification (de Castro-Cros et al, 2020), and multimodal visual-haptic feedback (Wang et al, 2019) can improve learning of MI-BCI. Improving the training conditions might reveal a more robust difference between (in-)efficient learners and thereby provide more valid evidence for the impacting variables on MI-BCI.…”

Section: Future Researchmentioning

confidence: 99%

Vividness of Visual Imagery and Personality Impact Motor-Imagery Brain Computer Interfaces

Leeuwis

Paas

Alimardani

2021

Front. Hum. Neurosci.

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Brain-computer interfaces (BCIs) are communication bridges between a human brain and external world, enabling humans to interact with their environment without muscle intervention. Their functionality, therefore, depends on both the BCI system and the cognitive capacities of the user. Motor-imagery BCIs (MI-BCI) rely on the users’ mental imagination of body movements. However, not all users have the ability to sufficiently modulate their brain activity for control of a MI-BCI; a problem known as BCI illiteracy or inefficiency. The underlying mechanism of this phenomenon and the cause of such difference among users is yet not fully understood. In this study, we investigated the impact of several cognitive and psychological measures on MI-BCI performance. Fifty-five novice BCI-users participated in a left- versus right-hand motor imagery task. In addition to their BCI classification error rate and demographics, psychological measures including personality factors, affinity for technology, and motivation during the experiment, as well as cognitive measures including visuospatial memory and spatial ability and Vividness of Visual Imagery were collected. Factors that were found to have a significant impact on MI-BCI performance were Vividness of Visual Imagery, and the personality factors of orderliness and autonomy. These findings shed light on individual traits that lead to difficulty in BCI operation and hence can help with early prediction of inefficiency among users to optimize training for them.

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Section: Future Researchmentioning

confidence: 99%

Vividness of Visual Imagery and Personality Impact Motor-Imagery Brain Computer Interfaces

Leeuwis

Paas

Alimardani

2021

Front. Hum. Neurosci.

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“…The ES tried to improve the AO effect; however, there were no statistically significant differences between the accuracies of the VIR and VER groups for most of the cases. The protocol proposed could be modified to improve BCI classification performance taking the advantages of the ES [28] to enhance the MI performance and attention [5], [15], [16], [47] during BCI calibration sessions.…”

Section: Discussionmentioning

confidence: 99%

“…It was evaluated for the two-class and three-class classification; the MI tasks were flexion, extension, and grasping, which are difficult to classify owing to the overlapped corresponded area on the motor cortex. In addition, we included VR animations as BCI training reinforcement after the MI task to contribute to the decoding enhancement, similar to feedback sessions [28]; visual reinforcement (VIR) is based on that action observation (AO) increased the ERD power, visual stimulation displaying body movements stimulates the motor-related cortical areas that correspond to the observed movement through the mirror neuron system [29]- [31]. Then, ES was added to visual reinforcement (VER) to provide proprioceptive stimulation without provoking muscle fatigue and contribute to MI learning, as mentioned previously; both types of reinforcements were compared.…”

Section: Introductionmentioning

confidence: 99%

Decoding Hand Motor Imagery Tasks Within the Same Limb From EEG Signals Using Deep Learning

Achanccaray

Hayashibe

2020

IEEE Trans. Med. Robot. Bionics

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Motor imagery (MI) tasks of different body parts have been successfully decoded by conventional classifiers, such as LDA and SVM. On the other hand, decoding MI tasks within the same limb is a challenging problem with these classifiers; however, it would provide more options to control robotic devices. This work proposes to improve the hand MI tasks decoding within the same limb in a brain-computer interface using convolutional neural networks (CNNs); the CNN EEGNet, LDA, and SVM classifiers were evaluated for two (flexion/extension) and three (flexion/extension/grasping) MI tasks. Our approach is the first attempt to apply CNNs for solving this problem to our best knowledge. In addition, visual and electrotactile stimulation were included as BCI training reinforcement after the MI task similar to feedback sessions; then, they were compared. The EEGNet achieved maximum mean accuracies of 78.46% (±12.50%) and 76.72% (±11.67%) for two and three classes, respectively. Outperforming conventional classifiers with results around 60% and 48%, and similar works with results lower than 67% and 75%, respectively. Moreover, the electrical stimulation over the visual stimulus was not significant during the calibration session. The deep learning scheme enhanced the decoding of MI tasks within the same limb against the conventional framework.

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“…In another study, Wilson et al [27] proposed a lingual electrotactile stimulation feedback as a vision substitution system in a BCI based on MI to move a cursor, where subjects with or without visual disability obtained similar results. Also, a BCI used visual-haptic feedback [28], which comprised a visual scene and electrical stimulation simultaneously. is feedback combination improved sensorimotor cortical activity and BCI performance during MI in able-bodied subjects.…”

Section: Introductionmentioning

confidence: 99%

Visual‐Electrotactile Stimulation Feedback to Improve Immersive Brain‐Computer Interface Based on Hand Motor Imagery

Achanccaray

Izumi

Hayashibe

2021

Computational Intelligence and Neuroscience

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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.

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A BCI based visual-haptic neurofeedback training improves cortical activations and classification performance during motor imagery

Cited by 55 publications

References 43 publications

Vividness of Visual Imagery and Personality Impact Motor-Imagery Brain Computer Interfaces

Vividness of Visual Imagery and Personality Impact Motor-Imagery Brain Computer Interfaces

Decoding Hand Motor Imagery Tasks Within the Same Limb From EEG Signals Using Deep Learning

Visual‐Electrotactile Stimulation Feedback to Improve Immersive Brain‐Computer Interface Based on Hand Motor Imagery

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