SSVEP-Based Brain–Computer Interface Controlled Functional Electrical Stimulation System for Upper Extremity Rehabilitation

Zhao, Xingang; Chu, Yaqi; Han, Jianda; Zhang, Zhiqiang

doi:10.1109/tsmc.2016.2523762

Cited by 57 publications

(32 citation statements)

References 33 publications

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“…In stroke patients, post-rehabilitation electromyographic recordings showed increased activity in the paretic finger following BCI-driven rehabilitation using an orthosis, which exhibits improvement in neuromuscular coherence for movement control (Ramos-Murguialday et al, 2013). Furthermore, BCIdriven proprioceptive feedback-based and functional electrical stimulation-based rehabilitation strategies could reinforce motor control (Zhao et al, 2016;Darvishi et al, 2017;Selfslagh et al, 2019).…”

Section: Neuroplasticity and Bci-driven Motor Rehabilitationmentioning

confidence: 99%

Intra- and Inter-subject Variability in EEG-Based Sensorimotor Brain Computer Interface: A Review

Saha

Baumert

2020

Front. Comput. Neurosci.

178

109

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Brain computer interfaces (BCI) for the rehabilitation of motor impairments exploit sensorimotor rhythms (SMR) in the electroencephalogram (EEG). However, the neurophysiological processes underpinning the SMR often vary over time and across subjects. Inherent intra-and inter-subject variability causes covariate shift in data distributions that impede the transferability of model parameters amongst sessions/subjects. Transfer learning includes machine learning-based methods to compensate for inter-subject and inter-session (intra-subject) variability manifested in EEG-derived feature distributions as a covariate shift for BCI. Besides transfer learning approaches, recent studies have explored psychological and neurophysiological predictors as well as inter-subject associativity assessment, which may augment transfer learning in EEG-based BCI. Here, we highlight the importance of measuring inter-session/subject performance predictors for generalized BCI frameworks for both normal and motor-impaired people, reducing the necessity for tedious and annoying calibration sessions and BCI training.

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Section: Neuroplasticity and Bci-driven Motor Rehabilitationmentioning

confidence: 99%

Intra- and Inter-subject Variability in EEG-Based Sensorimotor Brain Computer Interface: A Review

Saha

Baumert

2020

Front. Comput. Neurosci.

178

109

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“…The frequency of this activity is a multiple of or the same as the frequency of the visual stimulus. Previous studies have demonstrated that EEG signals over the visual cortex are natural responses to flickering stimuli between 5 and 27 Hz, with the strongest response at frequencies approximately 15 or 20 Hz [5,6], which can be used to develop brain-computer interface applications [7,8]. Based on the behavioral characteristics of brain electrical activity, a response decrement resulting from repeated visual stimulation is defined as habituation, suggesting robustness of the brain system [9,10].…”

Section: Introductionmentioning

confidence: 99%

Effects of repetitive SSVEPs on EEG complexity using multiscale inherent fuzzy entropy

et al. 2020

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Multiscale inherent fuzzy entropy is an objective measurement of electroencephalography (EEG) complexity, reflecting the habituation of brain systems. Entropy dynamics are generally believed to reflect the ability of the brain to adapt to a visual stimulus environment. In this study, we explored repetitive steady-state visual evoked potential (SSVEP)-based EEG complexity by assessing multiscale inherent fuzzy entropy with relative measurements. We used a wearable EEG device with Oz and Fpz electrodes to collect EEG signals from 40 participants under the following three conditions: a resting state (closed-eyes (CE) and open-eyes (OE) stimulation with five 15-Hz CE SSVEPs and stimulation with five 20-Hz OE SSVEPs. We noted monotonic enhancement of occipital EEG relative complexity with increasing stimulus times in CE and OE conditions. The occipital EEG relative complexity was significantly higher for the fifth SSVEP than for the first SSEVP (FDR-adjusted p < 0.05). Similarly, the prefrontal EEG relative complexity tended to be significantly higher in the OE condition compared to that in the CE condition (FDR-adjusted p < 0.05). The results also indicate that multiscale inherent fuzzy entropy is superior to other competing multiscale-based entropy methods. In conclusion, EEG relative complexity increases with stimulus times, a finding that reflects the strong habituation of brain systems. These results suggest that multiscale inherent fuzzy entropy is an EEG pattern with which brain complexity can be assessed using repetitive SSVEP

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“…Steady-state visually evoked potentials are based on the brains electrical signals generated when the retina is excited by a visual stimulus. This technique is preferred in brain interfacing research because of good signal-to-noise ratio [24]. The focus of our current research is an evaluation of noninvasive BMI, which is viewed as the technical evolution of VR and AR which enables users to translate action conceived in the virtual world into actions in the real world.…”

Section: Domain Definition and Motivationmentioning

confidence: 99%

Immersive Technology for Human‐Centric Cyberphysical Systems in Complex Manufacturing Processes: A Comprehensive Overview of the Global Patent Profile Using Collective Intelligence

Govindarajan

Trappey²

2018

Complexity

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Immersive technology for human-centric cyberphysical systems includes broad concepts that enable users in the physical world to connect with the cyberworld with a sense of immersion. Complex systems such as virtual reality, augmented reality, braincomputer interfaces, and brain-machine interfaces are emerging as immersive technologies that have the potential for improving manufacturing systems. Industry 4.0 includes all technologies, standards, and frameworks for the fourth industrial revolution to facilitate intelligent manufacturing. Industrial immersive technologies will be used for smart manufacturing innovation in the context of Industry 4.0's human machine interfaces. This research provides a thorough review of the literature, construction of a domain ontology, presentation of patent metatrend statistical analysis, and data mining analysis using a technology function matrix and highlights technical and functional development trends using latent Dirichlet allocation (LDA) models. A total of 179 references from the IEEE and IET databases and 2,672 patents are systematically analyzed to identify current trends. The paper establishes an essential foundation for the development of advanced human-centric cyberphysical systems in complex manufacturing processes.

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SSVEP-Based Brain–Computer Interface Controlled Functional Electrical Stimulation System for Upper Extremity Rehabilitation

Cited by 57 publications

References 33 publications

Intra- and Inter-subject Variability in EEG-Based Sensorimotor Brain Computer Interface: A Review

Intra- and Inter-subject Variability in EEG-Based Sensorimotor Brain Computer Interface: A Review

Effects of repetitive SSVEPs on EEG complexity using multiscale inherent fuzzy entropy

Immersive Technology for Human‐Centric Cyberphysical Systems in Complex Manufacturing Processes: A Comprehensive Overview of the Global Patent Profile Using Collective Intelligence

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