Speaking and cognitive distractions during EEG-based brain control of a virtual neuroprosthesis-arm

BACKGROUND: Brain-computer interface (BCI) systems have been developed to provide paralyzed individuals the ability to command the movements of an assistive device using only their brain activity. BCI systems are typically tested in a controlled laboratory environment were the user is focused solely on the brain-control task. However, for practical use in everyday life people must be able to use their brain-controlled device while mentally engaged with the cognitive responsibilities of daily activities and while compensating for any inherent dynamics of the device itself. BCIs that use electroencephalography (EEG) for movement control are often assumed to require significant mental effort, thus preventing users from thinking about anything else while using their BCI. This study tested the impact of cognitive load as well as speaking on the ability to use an EEG-based BCI. FINDINGS: Six participants controlled the two-dimensional (2D) movements of a simulated neuroprosthesis-arm under three different levels of cognitive distraction. The two higher cognitive load conditions also required simultaneously speaking during BCI use. On average, movement performance declined during higher levels of cognitive distraction, but only by a limited amount. Movement completion time increased by 7.2%, the percentage of targets successfully acquired declined by 11%, and path efficiency declined by 8.6%. Only the decline in percentage of targets acquired and path efficiency were statistically significant (p < 0.05). CONCLUSION: People who have relatively good movement control of an EEG-based BCI may be able to speak and perform other cognitively engaging activities with only a minor drop in BCI-control performance

Interpretable surface-based detection of focal cortical dysplasias:a Multi-centre Epilepsy Lesion Detection study

One outstanding challenge for machine learning in diagnostic biomedical imaging is algorithm interpretability. A key application is the identification of subtle epileptogenic focal cortical dysplasias (FCDs) from structural MRI. FCDs are difficult to visualize on structural MRI but are often amenable to surgical resection. We aimed to develop an open-source, interpretable, surface-based machine-learning algorithm to automatically identify FCDs on heterogeneous structural MRI data from epilepsy surgery centres worldwide. The Multi-centre Epilepsy Lesion Detection (MELD) Project collated and harmonized a retrospective MRI cohort of 1015 participants, 618 patients with focal FCD-related epilepsy and 397 controls, from 22 epilepsy centres worldwide. We created a neural network for FCD detection based on 33 surface-based features. The network was trained and cross-validated on 50% of the total cohort and tested on the remaining 50% as well as on 2 independent test sites. Multidimensional feature analysis and integrated gradient saliencies were used to interrogate network performance. Our pipeline outputs individual patient reports, which identify the location of predicted lesions, alongside their imaging features and relative saliency to the classifier. On a restricted 'gold-standard' subcohort of seizure-free patients with FCD type IIB who had T1 and fluid-attenuated inversion recovery MRI data, the MELD FCD surface-based algorithm had a sensitivity of 85%. Across the entire withheld test cohort the sensitivity was 59% and specificity was 54%. After including a border zone around lesions, to account for uncertainty around the borders of manually delineated lesion masks, the sensitivity was 67%. This multicentre, multinational study with open access protocols and code has developed a robust and interpretable machine-learning algorithm for automated detection of focal cortical dysplasias, giving physicians greater confidence in the identification of subtle MRI lesions in individuals with epilepsy

Transdermal Electrical Neuromodulation for Anxiety and Sleep Problems in High-Functioning Autism Spectrum Disorder: Feasibility and Preliminary Findings

Background: Autism spectrum disorder (ASD) is associated with anxiety and sleep problems. We investigated transdermal electrical neuromodulation (TEN) of the cervical nerves in the neck as a safe, effective, comfortable and non-pharmacological therapy for decreasing anxiety and enhancing sleep quality in ASD. Methods: In this blinded, sham-controlled study, seven adolescents and young adults with high-functioning ASD underwent five consecutive treatment days, one day of the sham followed by four days of subthreshold TEN for 20 min. Anxiety-provoking cognitive tasks were performed after the sham/TEN. Measures of autonomic nervous system activity, including saliva &alpha;-amylase and cortisol, electrodermal activity, and heart rate variability, were collected from six participants. Results: Self-rated and caretaker-rated measures of anxiety were significantly improved with TEN treatment as compared to the sham, with effect sizes ranging from medium to large depending on the rating scale. Sleep scores from caretaker questionnaires also improved, but not significantly. Performance on two of the three anxiety-provoking cognitive tasks and heart rate variability significantly improved with TEN stimulation as compared to the sham. Four of the seven (57%) participants were responders, defined as a &ge; 30% improvement in self-reported anxiety. Salivary &alpha;-amylase decreased with more TEN sessions and decreased from the beginning to the end of the session on TEN days for responders. TEN was well-tolerated without significant adverse events. Conclusions: This study provides preliminary evidence that TEN is well-tolerated in individuals with ASD and can improve anxiety

Discreet discrete commands for assistive and neuroprosthetic devices

Many new assistive devices are available for individuals paralyzed below the neck due to spinal cord injury. Severely paralyzed individuals must be able to command their complex assistive devices using remaining activity from the neck up. Electromyographic (EMG) sensors enable people to use contractions of head and neck muscles to generate multiple proportional command signals. Electroencephalographic (EEG) signals can also be used to generate commands for assistive device control by conveying information about imagined or attempted movements. Fully-implanted wireless biopotential detection systems are now being developed to reliably detect EMGs, EEGs, or a mixture of the two from recording electrodes implanted just under the skin or scalp thus eliminating the need for externally worn hardware on the head or face. This present study shows how novel patterns of jaw muscle contractions, detected via biopotential sensors on the scalp surface or implanted just under the scalp, can be used to generate reliable discrete EMG commands, which can be differentiated from patterns generated during normal activities, such as chewing. These jaw contractions can be detected with sensors already in place to detect other muscle- or brain-based command signals thus adding to the functionality of current device control systems

Clinical trials for pediatric traumatic brain injury: definition of insanity?

Traumatic brain injury (TBI) is a leading cause of morbidity and mortality in children both in the United States and throughout the world. Despite valiant efforts and multiple clinical trials completed over the last few decades, there are no high-level recommendations for pediatric TBI available in current guidelines. In this review, the authors explore key findings from the major pediatric clinical trials in children with TBI that have shaped present-day recommendations and the insights gained from them. The authors also offer a perspective on potential efforts to improve the efficacy of future clinical trials in children following TBI

Altered modulation of sensorimotor rhythms with chronic paralysis

After paralysis, the disconnection between the cortex and its peripheral targets leads to neuroplasticity throughout the nervous system. However, it is unclear how chronic paralysis specifically impacts cortical oscillations associated with attempted movement of impaired limbs. We hypothesized that μ- (8-13 Hz) and β- (15-30 Hz) event-related desynchronization (ERD) would be less modulated for individuals with hand paralysis due to cervical spinal cord injury (SCI). To test this, we compared the modulation of ERD from magnetoencephalography (MEG) during attempted and imagined grasping performed by participants with cervical SCI ( = 12) and able-bodied controls ( = 13). Seven participants with tetraplegia were able to generate some electromyography (EMG) activity during attempted grasping, whereas the other five were not. The peak and area of ERD were significantly decreased for individuals without volitional muscle activity when they attempted to grasp compared with able-bodied subjects and participants with SCI,with some residual EMG activity. However, no significant differences were found between subject groups during mentally simulated tasks (i.e., motor imagery) where no muscle activity or somatosensory consequences were expected. These findings suggest that individuals who are unable to produce muscle activity are capable of generating ERD when attempting to move, but the characteristics of this ERD are altered. However, for people who maintain volitional muscle activity after SCI, there are no significant differences in ERD characteristics compared with able-bodied controls. These results provide evidence that ERD is dependent on the level of intact muscle activity after SCI. Source space MEG was used to investigate sensorimotor cortical oscillations in individuals with SCI. This study provides evidence that individuals with cervical SCI exhibit decreased ERD when they attempt to grasp if they are incapable of generating muscle activity. However, there were no significant differences in ERD between paralyzed and able-bodied participants during motor imagery. These results have important implications for the design and evaluation of new therapies, such as motor imagery and neurofeedback interventions

Approaches to Multimodality Monitoring in Pediatric Traumatic Brain Injury

Traumatic brain injury (TBI) is a leading cause of morbidity and mortality in children. Improved methods of monitoring real-time cerebral physiology are needed to better understand when secondary brain injury develops and what treatment strategies may alleviate or prevent such injury. In this review, we discuss emerging technologies that exist to better understand intracranial pressure (ICP), cerebral blood flow, metabolism, oxygenation and electrical activity. We also discuss approaches to integrating these data as part of a multimodality monitoring strategy to improve patient care

Simulation of high-frequency sinusoidal electrical block of mammalian myelinated axons

High frequency alternating current (HFAC) sinusoidal waveforms can block conduction in mammalian peripheral nerves. A mammalian axon model was used to simulate the response of nerves to HFAC conduction block. Sinusoidal waveforms from 1 to 40 kHz were delivered to eight simulated axon diameters ranging from 7.3 to 16 microm. Conduction block was obtained between 3 to 40 kHz. The minimum peak to peak current at which block was obtained, defined as the block threshold, increased with increasing frequency. Block threshold varied inversely with axon diameter. Upon initiation, the HFAC waveform produced one or more action potentials. These simulation results closely parallel previous experimental results of high frequency motor block of the rat sciatic and cat pudendal nerve. During HFAC block, the axons showed a dynamic steady state depolarization of multiple nodes, strongly suggesting a depolarization mechanism for HFAC conduction block

Effects of MEG-based neurofeedback for hand rehabilitation after tetraplegia: preliminary findings in cortical modulations and grip strength

OBJECTIVE: Neurofeedback (NF) trains people to volitionally modulate their cortical activity to affect a behavioral outcome. We evaluated the feasibility of using NF to improve hand function after chronic cervical-level spinal cord injury (SCI) using biologically-relevant visual feedback of motor-related brain activity and an intuitive control scheme. APPROACH: The NF system acquired magnetoencephalography (MEG) data in real-time to provide feedback of event-related desynchronization (ERD) measured over the sensorimotor cortex during attempted hand grasping. During brain control, stronger ERD resulting from attempted grasping drove the virtual hand towards a more closed grasp, while less ERD drove the hand more open. MAIN RESULTS: Eight individuals with partial or complete hand impairment due to chronic SCI controlled the NF to perform a grasping task that increased in difficulty as the participants achieved success. During their first NF session, participants achieved an average success rate of 63.7 ± 6.4% (chance level of 13.9%). After as few as one intervention session, four of the seven individuals evaluated for ERD changes had significantly strengthened ERD and three of the four participants with measurable grip strength prior to NF had increased grip strength. Interestingly, both individuals who participated in a longer-term study (i.e. \u3e8 NF sessions) had improved grip strength and significantly strengthened ERD. SIGNIFICANCE: This study demonstrates that MEG-based NF training can change brain activity in individuals with hand impairment due to SCI and has the potential to induce acute changes in grip strength. Future studies will evaluate whether neuroplasticity induced with long term NF can improve hand function for those with moderate impairment