A hybrid BCI-controlled FES system for hand-wrist motor function

Choi, Inchul; Bond, K.; Nam, Chang S.

doi:10.1109/smc.2016.7844585

Cited by 8 publications

(6 citation statements)

References 27 publications

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“…ereby, the ERD over the sensory perception region of the VES feedback group was weaker, verifying the results of [61]. Also, both feedback groups achieved relative powers higher than those of similar approaches (approximately 40%) [34,35] in the μand central β-bands.…”

Section: Relative Powersupporting

confidence: 71%

“…e relative power can normalize the PSD, eliminating offsets and reducing the power variability. en, the relative power was computed using [33][34][35] RP x � P x − P baseline P baseline × 100.…”

Section: Discussionmentioning

confidence: 99%

“…FES-BCI [30,31] and robot-BMI [32] showed virtual hands as realistic visual feedback. Both systems improved upper limb motor functions and increased the subject's motivation with SCI by achieving higher relative power (RP) [33][34][35] than those of conventional BCI systems. Other studies used a head-mounted display (HMD) to increase the immersive experience, which is considered as the subject's propensity to respond to the VR environment as it was real [36].…”

Section: Introductionmentioning

confidence: 99%

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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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Section: Relative Powersupporting

confidence: 71%

Section: Discussionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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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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“…They are (1) Orthosis ( Ortner et al, 2011 ; Duvinage et al, 2012 ; Stan et al, 2015 ; Delijorge et al, 2020 ) and exoskeleton ( Gui et al, 2015 ; Kwak et al, 2015 ; Bhagat et al, 2016 ), used to perform sequences of movements to activate the hand, wrist, arm, leg or foot. (2) FES, which has been reported to be of help to regain coordination and improve performance in functional tasks ( Do et al, 2011 ; Ding-Guo and Ying, 2012 ; Yao et al, 2012 ; McCabe et al, 2015 ; van Dokkum et al, 2015 ; Choi et al, 2016 ; Osuagwu et al, 2016 ; Zhao et al, 2016 ; Son et al, 2020 ). (3) Prosthesis ( Li et al, 2018 ), and (4) VR (VEP-based BCI systems immersed in virtual environment) ( Su et al, 2011 ; Koo et al, 2015 ; Tidoni et al, 2017 ; Touyama and Sakuda, 2017 ; Choi et al, 2019 ; Huang et al, 2019 ; Yao et al, 2019 ).…”

Section: Discussionmentioning

confidence: 99%

Artificial Intelligence Algorithms in Visual Evoked Potential-Based Brain-Computer Interfaces for Motor Rehabilitation Applications: Systematic Review and Future Directions

Gutiérrez-Martínez

Mercado-Gutierrez

Carvajal-Gámez

et al. 2021

Front. Hum. Neurosci.

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Brain-Computer Interface (BCI) is a technology that uses electroencephalographic (EEG) signals to control external devices, such as Functional Electrical Stimulation (FES). Visual BCI paradigms based on P300 and Steady State Visually Evoked potentials (SSVEP) have shown high potential for clinical purposes. Numerous studies have been published on P300- and SSVEP-based non-invasive BCIs, but many of them present two shortcomings: (1) they are not aimed for motor rehabilitation applications, and (2) they do not report in detail the artificial intelligence (AI) methods used for classification, or their performance metrics. To address this gap, in this paper the PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) methodology was applied to prepare a systematic literature review (SLR). Papers older than 10 years, repeated or not related to a motor rehabilitation application, were excluded. Of all the studies, 51.02% referred to theoretical analysis of classification algorithms. Of the remaining, 28.48% were for spelling, 12.73% for diverse applications (control of wheelchair or home appliances), and only 7.77% were focused on motor rehabilitation. After the inclusion and exclusion criteria were applied and quality screening was performed, 34 articles were selected. Of them, 26.47% used the P300 and 55.8% the SSVEP signal. Five applications categories were established: Rehabilitation Systems (17.64%), Virtual Reality environments (23.52%), FES (17.64%), Orthosis (29.41%), and Prosthesis (11.76%). Of all the works, only four performed tests with patients. The most reported machine learning (ML) algorithms used for classification were linear discriminant analysis (LDA) (48.64%) and support vector machine (16.21%), while only one study used a deep learning algorithm: a Convolutional Neural Network (CNN). The reported accuracy ranged from 38.02 to 100%, and the Information Transfer Rate from 1.55 to 49.25 bits per minute. While LDA is still the most used AI algorithm, CNN has shown promising results, but due to their high technical implementation requirements, many researchers do not justify its implementation as worthwile. To achieve quick and accurate online BCIs for motor rehabilitation applications, future works on SSVEP-, P300-based and hybrid BCIs should focus on optimizing the visual stimulation module and the training stage of ML and DL algorithms.

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“…The hBCI combines different approaches to utilize the advantages of multiple BCI modalities. It can be also a combination of brain activity with non-brain activity, and various other psychological signals were shown to be a promising option of hBCI development ( Scherer et al, 2007 ; Choi et al, 2016 ). Thus, the input signals can consist of the combination of two brain characteristics using EEG signals, or EEG with eye movements (EOG), muscle activity (electromyography, EMG), or with heart signal (ECG or EKG).…”

Section: Introductionmentioning

confidence: 99%

Brain Computer Interfaces for Improving the Quality of Life of Older Adults and Elderly Patients

et al. 2020

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All people experience aging, and the related physical and health changes, including changes in memory and brain function. These changes may become debilitating leading to an increase in dependence as people get older. Many external aids and tools have been developed to allow older adults and elderly patients to continue to live normal and comfortable lives. This mini-review describes some of the recent studies on cognitive decline and motor control impairment with the goal of advancing non-invasive brain computer interface (BCI) technologies to improve health and wellness of older adults and elderly patients. First, we describe the state of the art in cognitive prosthetics for psychiatric diseases. Then, we describe the state of the art of possible assistive BCI applications for controlling an exoskeleton, a wheelchair and smart home for elderly people with motor control impairments. The basic age-related brain and body changes, the effects of age on cognitive and motor abilities, and several BCI paradigms with typical tasks and outcomes are thoroughly described. We also discuss likely future trends and technologies to assist healthy older adults and elderly patients using innovative BCI applications with minimal technical oversight.

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A hybrid BCI-controlled FES system for hand-wrist motor function

Cited by 8 publications

References 27 publications

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

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

Artificial Intelligence Algorithms in Visual Evoked Potential-Based Brain-Computer Interfaces for Motor Rehabilitation Applications: Systematic Review and Future Directions

Brain Computer Interfaces for Improving the Quality of Life of Older Adults and Elderly Patients

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