Dataset Information

A mobile brain-body imaging dataset recorded during treadmill walking with a brain-computer interface.

ABSTRACT: We present a mobile brain-body imaging (MoBI) dataset acquired during treadmill walking in a brain-computer interface (BCI) task. The data were collected from eight healthy subjects, each having three identical trials. Each trial consisted of three conditions: standing, treadmill walking, and treadmill walking with a closed-loop BCI. During the BCI condition, subjects used their brain activity to control a virtual avatar on a screen to walk in real-time. Robust procedures were designed to record lower limb joint angles (bilateral hip, knee, and ankle) using goniometers synchronized with 60-channel scalp electroencephalography (EEG). Additionally, electrooculogram (EOG), EEG electrodes impedance, and digitized EEG channel locations were acquired to aid artifact removal and EEG dipole-source localization. This dataset is unique in that it is the first published MoBI dataset recorded during walking. It is useful in addressing several important open research questions, such as how EEG is coupled with gait cycle during closed-loop BCI, how BCI influences neural activity during walking, and how a BCI decoder may be optimized.

SUBMITTER: He Y

PROVIDER: S-EPMC5914288 | biostudies-literature | 2018 Apr

REPOSITORIES: biostudies-literature

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A mobile brain-body imaging dataset recorded during treadmill walking with a brain-computer interface.

He Yongtian Y Luu Trieu Phat TP Nathan Kevin K Nakagome Sho S Contreras-Vidal Jose L JL

Scientific data 20180424

We present a mobile brain-body imaging (MoBI) dataset acquired during treadmill walking in a brain-computer interface (BCI) task. The data were collected from eight healthy subjects, each having three identical trials. Each trial consisted of three conditions: standing, treadmill walking, and treadmill walking with a closed-loop BCI. During the BCI condition, subjects used their brain activity to control a virtual avatar on a screen to walk in real-time. Robust procedures were designed to record ...[more]

PMID: 29688217

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Project description:Gait is a common but rather complex activity that supports mobility in daily life. It requires indeed sophisticated coordination of lower and upper limbs, controlled by the nervous system. The relationship between limb kinematics and muscular activity with neural activity, referred to as neurokinematic and neuromuscular connectivity (NKC/NMC) respectively, still needs to be elucidated. Recently developed analysis techniques for mobile high-density electroencephalography (hdEEG) recordings have enabled investigations of gait-related neural modulations at the brain level. To shed light on gait-related neurokinematic and neuromuscular connectivity patterns in the brain, we performed a mobile brain/body imaging (MoBI) study in young healthy participants. In each participant, we collected hdEEG signals and limb velocity/electromyography signals during treadmill walking. We reconstructed neural signals in the alpha (8-13 Hz), beta (13-30 Hz), and gamma (30-50 Hz) frequency bands, and assessed the co-modulations of their power envelopes with myogenic/velocity envelopes. Our results showed that the myogenic signals have larger discriminative power in evaluating gait-related brain-body connectivity with respect to kinematic signals. A detailed analysis of neuromuscular connectivity patterns in the brain revealed robust responses in the alpha and beta bands over the lower limb representation in the primary sensorimotor cortex. There responses were largely contralateral with respect to the body sensor used for the analysis. By using a voxel-wise analysis of variance on the NMC images, we revealed clear modulations across body sensors; the variability across frequency bands was relatively lower, and below significance. Overall, our study demonstrates that a MoBI platform based on hdEEG can be used for the investigation of gait-related brain-body connectivity. Future studies might involve more complex walking conditions to gain a better understanding of fundamental neural processes associated with gait control, or might be conducted in individuals with neuromotor disorders to identify neural markers of abnormal gait.

Project description:BackgroundSpinal cord injury (SCI) can leave the affected individuals with paraparesis or paraplegia, thus rendering them unable to ambulate. Since there are currently no restorative treatments for this population, novel approaches such as brain-controlled prostheses have been sought. Our recent studies show that a brain-computer interface (BCI) can be used to control ambulation within a virtual reality environment (VRE), suggesting that a BCI-controlled lower extremity prosthesis for ambulation may be feasible. However, the operability of our BCI has not yet been tested in a SCI population.MethodsFive participants with paraplegia or tetraplegia due to SCI underwent a 10-min training session in which they alternated between kinesthetic motor imagery (KMI) of idling and walking while their electroencephalogram (EEG) were recorded. Participants then performed a goal-oriented online task, where they utilized KMI to control the linear ambulation of an avatar while making 10 sequential stops at designated points within the VRE. Multiple online trials were performed in a single day, and this procedure was repeated across 5 experimental days.ResultsClassification accuracy of idling and walking was estimated offline and ranged from 60.5% (p?=?0.0176) to 92.3% (p?=?1.36×10-20) across participants and days. Offline analysis revealed that the activation of mid-frontal areas mostly in the ? and low ? bands was the most consistent feature for differentiating between idling and walking KMI. In the online task, participants achieved an average performance of 7.4±2.3 successful stops in 273±51 sec. These performances were purposeful, i.e. significantly different from the random walk Monte Carlo simulations (p<0.01), and all but one participant achieved purposeful control within the first day of the experiments. Finally, all participants were able to maintain purposeful control throughout the study, and their online performances improved over time.ConclusionsThe results of this study demonstrate that SCI participants can purposefully operate a self-paced BCI walking simulator to complete a goal-oriented ambulation task. The operation of the proposed BCI system requires short training, is intuitive, and robust against participant-to-participant and day-to-day neurophysiological variations. These findings indicate that BCI-controlled lower extremity prostheses for gait rehabilitation or restoration after SCI may be feasible in the future.

Dataset Information

A mobile brain-body imaging dataset recorded during treadmill walking with a brain-computer interface.

Publications

A mobile brain-body imaging dataset recorded during treadmill walking with a brain-computer interface.

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