Real-time EEG-based brain-computer interface to a virtual avatar enhances cortical involvement in human treadmill walking

Luu, Trieu Phat; Nakagome, Sho; He, Yongtian; Contreras-Vidal, José L.

doi:10.1038/s41598-017-09187-0

Cited by 74 publications

(88 citation statements)

References 62 publications

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“…At the same time, lower alpha activity over widespread areas of the scalp (from frontal to parietal cortex, and in some cases over the whole scalp) has also been observed when walking as compared to standing or resting (Beurskens et al, 2016;Peterson & Ferris, 2018). In addition, a decreased alpha power was observed when performing cognitively engaging tasks such as walking in an interactive virtual environment (Wagner et al, 2014) and closed-loop brain-computer interface (BCI) control of a virtual avatar walking (Luu et al, 2017).…”

Section: Psds Of CMI During Walkingmentioning

confidence: 99%

Alteration of brain dynamics during natural dual-task walking

Nenna

Protzak

et al. 2020

Preprint

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While walking in our natural environment, we continuously solve additional cognitive tasks. This increases the demand of resources needed for both the cognitive and motor systems, resulting in Cognitive-Motor Interference (CMI). While it is well known that a performance decrease in one or both tasks can be observed, little is known about human brain dynamics underlying CMI during dual-task walking. Moreover, a large portion of previous investigations on CMI took place in static settings, emphasizing the experimental rigor but overshadowing the ecological validity. To address these problems, we developed a dual-task walking scenario in virtual reality (VR) combined with Mobile Brain/Body Imaging (MoBI). We aimed at investigating how brain dynamics are modulated during natural overground walking while simultaneously performing a visual discrimination task in an ecologically valid scenario. Even though the visual task did not affect performance while walking, a P3 amplitude reduction along with changes in power spectral densities (PSDs) during dual-task walking were observed. Replicating previous results, this reflects the impact of walking on the parallel processing of visual stimuli, even when the cognitive task is particularly easy. This standardized and easy to modify VR-paradigm helps to systematically study CMI, allowing researchers to control the complexity of different tasks and sensory modalities. Future investigations implementing an improved virtual design with more challenging cognitive and motor tasks will have to investigate the roles of both cognition and motion, allowing for a better understanding of the functional architecture of attention reallocation between cognitive and motor systems during active behavior.

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Section: Psds Of CMI During Walkingmentioning

confidence: 99%

Alteration of brain dynamics during natural dual-task walking

Nenna

Protzak

et al. 2020

Preprint

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“…By contrast, BMI approaches directly measures brain activity to control robotic devices, therefore encourage neural activities that drive the designated motor tasks. These approaches encourage voluntary control and motor intent from users, which have been demonstrated as one of the main principles in rehabilitation and may trigger stronger neuroplasticity (Koenig et al 2011, Luu et al 2017. Additionally, better understanding of neural representations of human movements from advanced BMI technology may propel the development of a novel training paradigm for improving the efficacy of rehabilitation in a top-down approach.…”

Section: Clinical Relevancementioning

confidence: 99%

“…Among the identified studies, (He et al 2014) reconstructed the joint angles and electromyography (EMG) envelopes of lower limbs of the subjects in an offline analysis. The method was later adapted to control a virtual avatar on a screen in realtime (Luu et al 2017). Vouga et al (2017) demonstrated a realtime BMI for a monkey exoskeleton.…”

Section: Output From the Brain-machine Interfacesmentioning

confidence: 99%

“…The US Food and Drug Administration (FDA) has recognized exoskeletons as Class II medical devices with special controls, and has cleared four exoskeleton devices for marketing in the US: ReWalk Personal (ReWalk Robotics, Israel), Indego (Parker Hannifin, USA), Ekso GT (Ekso Bionics, USA), and Medical HAL (Cyberdyne, Japan). Several studies have reviewed existing lower limb exoskeletons in a clinical context, evaluating the outcomes, effectiveness, possible benefits , Dijkers et al 2016, Lajeunesse et al 2016, Louie and Eng 2016 and potential risks and adverse events (He et al 2017). Although the design forms of these orthoses and exoskeletons differ greatly, at core they are all powered robotic devices that assist walking for medically related purposes.…”

Section: Introductionmentioning

confidence: 99%

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Brain–machine interfaces for controlling lower-limb powered robotic systems

et al. 2018

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Objective. Lower-limb, powered robotics systems such as exoskeletons and orthoses have emerged as novel robotic interventions to assist or rehabilitate people with walking disabilities. These devices are generally controlled by certain physical maneuvers, for example pressing buttons or shifting body weight. Although effective, these control schemes are not what humans naturally use. The usability and clinical relevance of these robotics systems could be further enhanced by brain–machine interfaces (BMIs). A number of preliminary studies have been published on this topic, but a systematic understanding of the experimental design, tasks, and performance of BMI-exoskeleton systems for restoration of gait is lacking. Approach. To address this gap, we applied standard systematic review methodology for a literature search in PubMed and EMBASE databases and identified 11 studies involving BMI-robotics systems. The devices, user population, input and output of the BMIs and robot systems respectively, neural features, decoders, denoising techniques, and system performance were reviewed and compared. Main results. Results showed BMIs classifying walk versus stand tasks are the most common. The results also indicate that electroencephalography (EEG) is the only recording method for humans. Performance was not clearly presented in most of the studies. Several challenges were summarized, including EEG denoising, safety, responsiveness and others. Significance. We conclude that lower-body powered exoskeletons with automated gait intention detection based on BMIs open new possibilities in the assistance and rehabilitation fields, although the current performance, clinical benefits and several key challenging issues indicate that additional research and development is required to deploy these systems in the clinic and at home. Moreover, rigorous EEG denoising techniques, suitable performance metrics, consistent trial reporting, and more clinical trials are needed to advance the field.

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“…The decoding of this brain activity is a necessary step to build valid brain-computer interfaces (BCIs) able to generate gait artificially [85]. As a perspective, a real-time closed-loop BCI that decodes lower limb joint angles from scalp EEG during treadmill walking in order to control the walking movements of a virtual avatar has already been built [86]. This kind of approach could be useful in rehabilitation programs since healthy subjects are able to adapt an avatar's gait pattern controlled via a closed-loop EEG-based BCI in eight days of training.…”

Section: Discussionmentioning

confidence: 99%

Cortical Oscillations during Gait: Wouldn’t Walking Be So Automatic?

et al. 2020

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Gait is often considered as an automatic movement but cortical control seems necessary to adapt gait pattern with environmental constraints. In order to study cortical activity during real locomotion, electroencephalography (EEG) appears to be particularly appropriate. It is now possible to record changes in cortical neural synchronization/desynchronization during gait. Studying gait initiation is also of particular interest because it implies motor and cognitive cortical control to adequately perform a step. Time-frequency analysis enables to study induced changes in EEG activity in different frequency bands. Such analysis reflects cortical activity implied in stabilized gait control but also in more challenging tasks (obstacle crossing, changes in speed, dual tasks . . . ). These spectral patterns are directly influenced by the walking context but, when analyzing gait with a more demanding attentional task, cortical areas other than the sensorimotor cortex (prefrontal, posterior parietal cortex, etc.) seem specifically implied. While the muscular activity of legs and cortical activity are coupled, the precise role of the motor cortex to control the level of muscular contraction according to the gait task remains debated. The decoding of this brain activity is a necessary step to build valid brain-computer interfaces able to generate gait artificially.

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Real-time EEG-based brain-computer interface to a virtual avatar enhances cortical involvement in human treadmill walking

Cited by 74 publications

References 62 publications

Alteration of brain dynamics during natural dual-task walking

Alteration of brain dynamics during natural dual-task walking

Brain–machine interfaces for controlling lower-limb powered robotic systems

Cortical Oscillations during Gait: Wouldn’t Walking Be So Automatic?

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