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The use of scalp electroencephalography (EEG) signals for brain-computer interface (BCI) to control end effectors in real time, while providing mobile capabilities for use at home neurorehabilitation, requires of software and hardware robust solutions. Moreover, to ensure democratized access to these systems, low cost, interoperability, and ease of use are essential. These challenges were addressed in the design, development and validation of the NeuroExo BCI System. As a proof of concept, the system was tested with an exoskeleton system for upper-limb stroke rehabilitation as the end effector. 

Objective: We designed and validated a wireless, low-cost, easy-to-use, mobile, dry-electrode headset for scalp electroencephalography (EEG) recordings for closed-loop brain–computer (BCI) interface and internet-of-things (IoT) applications. Approach: The EEG-based BCI headset was designed from commercial off-the-shelf (COTS) components using a multi-pronged approach that balanced interoperability, cost, portability, usability, form factor, reliability, and closed-loop operation. Main Results: The adjustable headset was designed to accommodate 90% of the population. A patent-pending self-positioning dry electrode bracket allowed for vertical self-positioning while parting the user’s hair to ensure contact of the electrode with the scalp. In the current prototype, five EEG electrodes were incorporated in the electrode bracket spanning the sensorimotor cortices bilaterally, and three skin sensors were included to measure eye movement and blinks. An inertial measurement unit (IMU) provides monitoring of head movements. The EEG amplifier operates with 24-bit resolution up to 500 Hz sampling frequency and can communicate with other devices using 802.11 b/g/n WiFi. It has high signal–to–noise ratio (SNR) and common–mode rejection ratio (CMRR) (121 dB and 110 dB, respectively) and low input noise. In closed-loop BCI mode, the system can operate at 40 Hz, including real-time adaptive noise cancellation and 512 MB of processor memory. It supports LabVIEW as a backend coding language and JavaScript (JS), Cascading Style Sheets (CSS), and HyperText Markup Language (HTML) as front-end coding languages and includes training and optimization of support vector machine (SVM) neural classifiers. Extensive bench testing supports the technical specifications and human-subject pilot testing of a closed-loop BCI application to support upper-limb rehabilitation and provides proof-of-concept validation for the device’s use at both the clinic and at home. Significance: The usability, interoperability, portability, reliability, and programmability of the proposed wireless closed-loop BCI system provides a low-cost solution for BCI and neurorehabilitation research and IoT applications. 

Abstract Objective. Transcutaneous spinal cord stimulation (TSS) has been shown to be a promising non-invasive alternative to epidural spinal cord stimulation for improving outcomes of people with spinal cord injury (SCI). However, studies on the effects of TSS on cortical activation are limited. Our objectives were to evaluate the spatiotemporal effects of TSS on brain activity, and determine changes in functional connectivity under several different stimulation conditions. As a control, we also assessed the effects of functional electrical stimulation (FES) on cortical activity. Approach . Non-invasive scalp electroencephalography (EEG) was recorded during TSS or FES while five neurologically intact participants performed one of three lower-limb tasks while in the supine position: (1) A no contraction control task, (2) a rhythmic contraction task, or (3) a tonic contraction task. After EEG denoising and segmentation, independent components (ICs) were clustered across subjects to characterize sensorimotor networks in the time and frequency domains. ICs of the event related potentials (ERPs) were calculated for each cluster and condition. Next, a Generalized Partial Directed Coherence (gPDC) analysis was performed on each cluster to compare the functional connectivity between conditions and tasks. Main results . IC analysis of EEG during TSS resulted in three clusters identified at Brodmann areas (BA) 9, BA 6, and BA 4, which are areas associated with working memory, planning, and movement control. Lastly, we found significant ( p  < 0.05, adjusted for multiple comparisons) increases and decreases in functional connectivity of clusters during TSS, but not during FES when compared to the no stimulation conditions. Significance. The findings from this study provide evidence of how TSS recruits cortical networks during tonic and rhythmic lower limb movements. These results have implications for the development of spinal cord-based computer interfaces, and the design of neural stimulation devices for the treatment of pain and sensorimotor deficit. 

Mobile brain-body imaging (MoBI) technology allows the study of the brain in action and the context of complex natural settings. MoBI devices are wearable devices that typically record the scalp electroencephalogram (EEG) and head motion of the user. MoBI systems have applications in neuroscience, rehabilitation, design, and other applications. Here, we propose design principles for MoBI systems for use in brain-machine interfaces for rehabilitation by individuals with movement disabilities. This design study discusses the validity of the process of utilizing 3D anthropometric data as a basis to design a MoBI headset for an optimized fit and ergonomics. The study also discusses the need for ensuring that EEG sensors keep constant contact with the scalp and face for the best scan quality. Moreover, the need for singlehanded correct positioning of the headset is discussed to address disabilities in the older populations and clinical populations with motor impairments. 

Neurotechnology has traditionally been central to the diagnosis and treatment of neurological disorders. While these devices have initially been utilized in clinical and research settings, recent advancements in neurotechnology have yielded devices that are more portable, user friendly, and less expensive. These improvements allow laypeople to monitor their brain waves and interface their brains with external devices. Such improvements have led to the rise of wearable neurotechnology that is marketed to the consumer. While many of the consumer devices are marketed for innocuous applications, such as use in video games, there is potential for them to be repurposed for medical uses. How do we manage neurotechnologies that skirt the line between medical and consumer applications and what can be done to ensure consumer safety? Here, we characterize neurotechnology based on medical and consumer applications and summarize currently marketed uses of consumer-grade wearable headsets. We lay out concerns that may arise due to the similar claims associated with both medical and consumer devices, the possibility of consumer devices being repurposed for medical uses, and the potential for medical uses of neurotechnology to influence commercial markets related to employment and self-enhancement. 

Two stages of the creative writing process were characterized through mobile scalp electroencephalography (EEG) in a 16-week creative writing workshop. Portable dry EEG systems (four channels: TP09, AF07, AF08, TP10) with synchronized head acceleration, video recordings, and journal entries, recorded mobile brain-body activity of Spanish heritage students. Each student's brain-body activity was recorded as they experienced spaces in Houston, Texas (“Preparation” stage), and while they worked on their creative texts (“Generation” stage). We used Generalized Partial Directed Coherence (gPDC) to compare the functional connectivity among both stages. There was a trend of higher gPDC in the Preparation stage from right temporo-parietal (TP10) to left anterior-frontal (AF07) brain scalp areas within 1–50 Hz, not reaching statistical significance. The opposite directionality was found for the Generation stage, with statistical significant differences ( p < 0.05) restricted to the delta band (1–4 Hz). There was statistically higher gPDC observed for the inter-hemispheric connections AF07–AF08 in the delta and theta bands (1–8 Hz), and AF08 to TP09 in the alpha and beta (8–30 Hz) bands. The left anterior-frontal (AF07) recordings showed higher power localized to the gamma band (32–50 Hz) for the Generation stage. An ancillary analysis of Sample Entropy did not show significant difference. The information transfer from anterior-frontal to temporal-parietal areas of the scalp may reflect multisensory interpretation during the Preparation stage, while brain signals originating at temporal-parietal toward frontal locations during the Generation stage may reflect the final decision making process to translate the multisensory experience into a creative text. 

Wearable robotic devices are being designed to assist the elderly population and other patients with locomotion disabilities. However, wearable robotics increases the risk from falling. Neuroimaging studies have provided evidence for the involvement of frontocentral and parietal cortices in postural control and this opens up the possibility of using decoders for early detection of balance loss by using electroencephalography (EEG). This study investigates the presence of commonly identified components of the perturbation evoked responses (PEP) when a person is in an exoskeleton. We also evaluated the feasibility of using single-trial EEG to predict the loss of balance using a convolution neural network. Overall, the model achieved a mean 5-fold cross-validation test accuracy of 75.2 % across six subjects with 50% as the chance level. We employed a gradient class activation map-based visualization technique for interpreting the decisions of the CNN and demonstrated that the network learns from PEP components present in these single trials. The high localization ability of Grad-CAM demonstrated here, opens up the possibilities for deploying CNN for ERP/PEP analysis while emphasizing on model interpretability. 

In this communication, a translational roadmap for a noninvasive Brain Machine Interface (BMI) system for rehabilitation is presented. This multi-faceted project addresses important engineering, clinical, end user and regulatory challenges. The goal is to improve the feasibility of at-home neurorehabilitation for patients with chronic stroke by providing a low-cost, portable, form fitting, reliable, and easy-to-use system. The proposed BMI system is also designed to enable direct communication between the end-user and clinician, allowing for continuous patient specific rehabilitation optimization. 

Neural decoding of human locomotion, including automated gait intention detection and continuous decoding of lower limb joint angles, has been of great interest in the field of Brain Machine Interface (BMI). However, neural decoding of gait in developing children has yet to be demonstrated. In this study, we collected physiological data (electroencephalography (EEG), electromyography (EMG)), and kinematic data from children performing different locomotion tasks. We also developed a state space estimation model to decode lower limb joint angles from scalp EEG. Fluctuations in the amplitude of slow cortical potentials of EEG in the delta band (0.1 – 3 Hz) were used for prediction. The decoding accuracies (Pearson’s r values) were promising (Hip: 0.71; Knee: 0.59; Ankle: 0.51). Our results demonstrate the feasibility of neural decoding of children walking and have implications for the development of a real-time closed-loop BMI system for the control of a pediatric exoskeleton.