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The closed-loop control of rehabilitative technologies by neural commands has shown a great potential to improve motor recovery in patients suffering from paralysis. Brain-machine interfaces (BMI) can be used as a natural control method for such technologies. BMI provides a continuous association between the brain activity and peripheral stimulation, with the potential to induce plastic changes in the nervous system. Paraplegic patients, and especially the ones with incomplete injuries, constitute a potential target population to be rehabilitated with brain-controlled robotic systems, as they may improve their gait function after the reinforcement of their spared intact neural pathways. This paper proposes a closed-loop BMI system to control an ambulatory exoskeleton-without any weight or balance support-for gait rehabilitation of incomplete spinal cord injury (SCI) patients. The integrated system was validated with three healthy subjects, and its viability in a clinical scenario was tested with four SCI patients. Using a cue-guided paradigm, the electroencephalographic signals of the subjects were used to decode their gait intention and to trigger the movements of the exoskeleton. We designed a protocol with a special emphasis on safety, as patients with poor balance were required to stand and walk. We continuously monitored their fatigue and exertion level, and conducted usability and user-satisfaction tests after the experiments. The results show that, for the three healthy subjects, 84.44 ± 14.56% of the trials were correctly decoded. Three out of four patients performed at least one successful BMI session, with an average performance of 77.6 1 ± 14.72%. The shared control strategy implemented (i.e., the exoskeleton could only move during specific periods of time) was effective in preventing unexpected movements during periods in which patients were asked to relax. On average, 55.22 ± 16.69% and 40.45 ± 16.98% of the trials (for healthy subjects and patients, respectively) would have suffered from unexpected activations (i.e., false positives) without the proposed control strategy. All the patients showed low exertion and fatigue levels during the performance of the experiments. This paper constitutes a proof-of-concept study to validate the feasibility of a BMI to control an ambulatory exoskeleton by patients with incomplete paraplegia (i.e., patients with good prognosis for gait rehabilitation).

This technical report describes a 360-channel flexible multi-electrode array with high spatial resolution, wide coverage area and minimal damage to the recorded neural tissue. Among other descriptions of multiunit in vivo neuronal recording in cats, the authors also use the electrode array to show spiral-patterned spread of epileptic neural activity in the neocortex. Arrays of electrodes for recording and stimulating the brain are used throughout clinical medicine and basic neuroscience research, yet are unable to sample large areas of the brain while maintaining high spatial resolution because of the need to individually wire each passive sensor at the electrode-tissue interface. To overcome this constraint, we developed new devices that integrate ultrathin and flexible silicon nanomembrane transistors into the electrode array, enabling new dense arrays of thousands of amplified and multiplexed sensors that are connected using fewer wires. We used this system to record spatial properties of cat brain activity in vivo , including sleep spindles, single-trial visual evoked responses and electrographic seizures. We found that seizures may manifest as recurrent spiral waves that propagate in the neocortex. The developments reported here herald a new generation of diagnostic and therapeutic brain-machine interface devices.

Brain‐machine interfaces (BMIs) that link the brain to a machine are promising for the treatment of neurological disorders through the bi‐directional translation of neural information over extended periods. However, the longevity of such implanted devices remains limited by the deterioration of their signal sensitivity over time due to acute inflammation from insertion trauma and chronic inflammation caused by the foreign body reaction. To address this challenge, a lubricated surface is fabricated to minimize friction during insertion and avoid immunogenicity during neural signal recording. Reduced friction force leads to 86% less impulse on the brain tissue, and thus immediately increases the number of measured signal electrodes by 102% upon insertion. Furthermore, the signal measurable period increases from 8 to 16 weeks due to the prevention of gliosis. By significantly reducing insertion damage and the foreign body reaction, the lubricated immune‐stealthy probe surface (LIPS) can maximize the longevity of implantable BMIs. Signal sensitivity of neural probes deteriorates over time due to acute and chronic inflammations. To overcome these problems, lubricated immune‐stealth probe surface (LIPS) is fabricated with nonimmunogenic properties. Near‐frictionless properties of LIPS minimizing insertion trauma increases signal‐to‐noise ratio and the number of neurons from short‐term neural recording. Furthermore, anti‐biofouling properties of LIPS improve its longevity, doubling the signal recording time‐length.

Brain signals measured over a series of experiments have inherent variability because of different physical and mental conditions among multiple subjects and sessions. Such variability complicates the analysis of data from multiple subjects and sessions in a consistent way, and degrades the performance of subject-transfer decoding in a brain–machine interface (BMI). To accommodate the variability in brain signals, we propose 1) a method for extracting spatial bases (or a dictionary) shared by multiple subjects, by employing a signal-processing technique of dictionary learning modified to compensate for variations between subjects and sessions, and 2) an approach to subject-transfer decoding that uses the resting-state activity of a previously unseen target subject as calibration data for compensating for variations, eliminating the need for a standard calibration based on task sessions. Applying our methodology to a dataset of electroencephalography (EEG) recordings during a selective visual–spatial attention task from multiple subjects and sessions, where the variability compensation was essential for reducing the redundancy of the dictionary, we found that the extracted common brain activities were reasonable in the light of neuroscience knowledge. The applicability to subject-transfer decoding was confirmed by improved performance over existing decoding methods. These results suggest that analyzing multisubject brain activities on common bases by the proposed method enables information sharing across subjects with low-burden resting calibration, and is effective for practical use of BMI in variable environments. •Novel method for extracting spatial bases of brain signals shared by multisubjects.•Subject-transfer decoding using activities on the common spatial bases.•Calibration of the decoders for target subjects using resting-state recordings.•Robust EEG analysis results based on a dataset of more than forty subjects.•Better subject-transfer decoding performance than existing methods.

Self-regulation of sensorimotor oscillations is currently researched in neurorehabilitation, e.g. for priming subsequent physiotherapy in stroke patients, and may be modulated by neurofeedback or transcranial brain stimulation. It has still to be demonstrated, however, whether and under which training conditions such brain self-regulation could also result in motor gains. Thirty-two right-handed, healthy subjects participated in a three-day intervention during which they performed 462 trials of kinesthetic motor-imagery while a brain–robot interface (BRI) turned event-related β-band desynchronization of the left sensorimotor cortex into the opening of the right hand by a robotic orthosis. Different training conditions were compared in a parallel-group design: (i) adaptive classifier thresholding and contingent feedback, (ii) adaptive classifier thresholding and non-contingent feedback, (iii) non-adaptive classifier thresholding and contingent feedback, and (iv) non-adaptive classifier thresholding and non-contingent feedback. We studied the task-related cortical physiology with electroencephalography and the behavioral performance in a subsequent isometric motor task. Contingent neurofeedback and adaptive classifier thresholding were critical for learning brain self-regulation which, in turn, led to behavioral gains after the intervention. The acquired skill for sustained sensorimotor β-desynchronization correlated significantly with subsequent motor improvement. Operant learning of brain self-regulation with a BRI may offer a therapeutic perspective for severely affected stroke patients lacking residual hand function. •Self-regulation of sensorimotor oscillations could be learned within a short-term intervention.•Contingent neurofeedback and performance-adaptive classifier thresholding were critical for learning brain self-regulation.•Operant learning of sensorimotor desynchronization led to behavioral gains after the intervention.•The acquired skill for sustained sensorimotor β-desynchronization correlated significantly with subsequent motor improvement.

Lower-limb robotic exoskeletons are wearable devices that can be beneficial for people with lower-extremity motor impairment because they can be valuable in rehabilitation or assistance. These devices can be controlled mentally by means of brain–machine interfaces (BMI). The aim of the present study was the design of a BMI based on motor imagery (MI) to control the gait of a lower-limb exoskeleton. The evaluation is carried out with able-bodied subjects as a preliminary study since potential users are people with motor limitations. The proposed control works as a state machine, i.e., the decoding algorithm is different to start (standing still) and to stop (walking). The BMI combines two different paradigms for reducing the false triggering rate (when the BMI identifies irrelevant brain tasks as MI), one based on motor imagery and another one based on the attention to the gait of the user. Research was divided into two parts. First, during the training phase, results showed an average accuracy of 68.44 ± 8.46% for the MI paradigm and 65.45 ± 5.53% for the attention paradigm. Then, during the test phase, the exoskeleton was controlled by the BMI and the average performance was 64.50 ± 10.66%, with very few false positives. Participants completed various sessions and there was a significant improvement over time. These results indicate that, after several sessions, the developed system may be employed for controlling a lower-limb exoskeleton, which could benefit people with motor impairment as an assistance device and/or as a therapeutic approach with very limited false activations.

Brain–robot interfaces (BRI) are studied as novel interventions to facilitate functional restoration in patients with severe and persistent motor deficits following stroke. They bridge the impaired connection in the sensorimotor loop by providing brain-state dependent proprioceptive feedback with orthotic devices attached to the hand or arm of the patients. The underlying neurophysiology of this BRI neuromodulation is still largely unknown. We investigated changes of corticospinal excitability with transcranial magnetic stimulation in thirteen right-handed healthy subjects who performed 40min of kinesthetic motor imagery receiving proprioceptive feedback with a robotic orthosis attached to the left hand contingent to event-related desynchronization of the right sensorimotor cortex in the β-band (16–22Hz). Neural correlates of this BRI intervention were probed by acquiring the stimulus–response curve (SRC) of both motor evoked potential (MEP) peak-to-peak amplitudes and areas under the curve. In addition, a motor mapping was obtained. The specificity of the effects was studied by comparing two neighboring hand muscles, one BRI-trained and one control muscle. Robust changes of MEP amplitude but not MEP area occurred following the BRI intervention, but only in the BRI-trained muscle. The steep part of the SRC showed an MEP increase, while the plateau of the SRC showed an MEP decrease. MEP mapping revealed a distributed pattern with a decrease of excitability in the hand area of the primary motor cortex, which controlled the BRI, but an increase of excitability in the surrounding somatosensory and premotor cortex. In conclusion, the BRI intervention induced a complex pattern of modulated corticospinal excitability, which may boost subsequent motor learning during physiotherapy. Brain–robot interface feedback of motor-imagery related sensorimotor β-band desynchronization induced robust and muscle-specific changes of corticospinal excitability with opposing effects on the trained finger extension muscle with decreases (blue/turquoise) and increases (red/yellow) of excitability in the TMS motor map for the primary motor cortex and the surrounding somatosensory and premotor cortex, respectively. The green dot represents the ‘hot spot’, where the MEP stimulus response curve was acquired and resulted in opposing effects for the steep part and the plateau of the TMS stimulus–response curve with increases and decreases of excitability, respectively. The neurophysiological effects where related to modulated synchronization of the stimulated neuronal population and not to the recruitment of additional neurons. The training induced changes of β-band desynchronization in the somatosensory cortex of individual participants correlated with increased corticospinal excitability. [Display omitted] •BRI feedback of motor-imagery related sensorimotor β-band ERD induced robust and muscle-specific changes of corticospinal excitability.•The steep part and the plateau of the TMS stimulus–response curve revealed increases and decreases of excitability, respectively.•M1 and the surrounding somatosensory/premotor cortex revealed opposing effects with decreases and increases of excitability, respectively.•These effects where related to modulated synchronization and not to the recruitment of additional neurons.•The training induced changes of β-band ERD of individual participants correlated with increased corticospinal excitability.

Neurofeedback of self-regulated brain activity in circumscribed cortical regions is used as a novel strategy to facilitate functional restoration following stroke. Basic knowledge about its impact on motor system oscillations and functional connectivity is however scarce. Specifically, a direct comparison between different feedback modalities and their neural signatures is missing. We assessed a neurofeedback training intervention of modulating β-activity in circumscribed sensorimotor regions by kinesthetic motor imagery (MI). Right-handed healthy participants received two different feedback modalities contingent to their MI-associated brain activity in a cross-over design: (I) visual feedback with a brain–computer interface (BCI) and (II) proprioceptive feedback with a brain–robot interface (BRI) orthosis attached to the right hand. High-density electroencephalography was used to examine the reactivity of the cortical motor system during the training session of each task by studying both local oscillatory power entrainment and distributed functional connectivity. Both feedback modalities activated a distributed functional connectivity network of coherent oscillations. A significantly higher skill and lower variability of self-controlled sensorimotor β-band modulation could, however, be achieved in the BRI condition. This gain in controlling regional motor oscillations was accompanied by functional coupling of remote β-band and θ-band activity in bilateral fronto-central regions and left parieto-occipital regions, respectively. The functional coupling of coherent θ-band oscillations correlated moreover with the skill of regional β-modulation thus revealing a motor learning related network. Our findings indicate that proprioceptive feedback is more suitable than visual feedback to entrain the motor network architecture during the interplay between motor imagery and feedback processing thus resulting in better volitional control of regional brain activity. [Display omitted] •Proprioceptive input is superior to visual input to entrain the motor network during neurofeedback.•Proprioceptive feedback increases self-control of regional β-band modulation.•Proprioceptive feedback entrains motor functional connectivity in the θ- and β-band.•Large-scale functional connectivity in θ-band correlates with regional β-band control.

The complexity of information processing in the brain requires the development of technologies that can provide spatial and temporal resolution by means of dense electrode arrays paired with high-channel-count signal acquisition electronics. In this work, we present an ultra-low noise modular 512-channel neural recording circuit that is scalable to up to 4096 simultaneously recording channels. The neural readout application-specific integrated circuit (ASIC) uses a dense 8.2 mm × 6.8 mm 2D layout to enable high-channel count, creating an ultra-light 350 mg flexible module. The module can be deployed on headstages for small animals like rodents and songbirds, and it can be integrated with a variety of electrode arrays. The chip was fabricated in a TSMC 0.18 µm 1.8 V CMOS technology and dissipates a total of 125 mW. Each DC-coupled channel features a gain and bandwidth programmable analog front-end along with 14 b analog-to-digital conversion at speeds up to 30 kS/s. Additionally, each front-end includes programmable electrode plating and electrode impedance measurement capability. We present both standalone and in vivo measurements results, demonstrating the readout of spikes and field potentials that are modulated by a sensory input.

Most brain-computer interfaces (BCIs) rely on one of three types of signals in the electroencephalogram (EEG): P300s, steady-state visually evoked potentials, and event-related desynchronization. EEG is typically recorded non-invasively with electrodes mounted on the human scalp using conductive electrode gel for optimal impedance and data quality. The use of electrode gel entails serious problems that are especially pronounced in real-world settings when experts are not available. Some recent work has introduced dry electrode systems that do not require gel, but often introduce new problems such as comfort and signal quality. The principal goal of this study was to assess a new dry electrode BCI system in a very common task: spelling with a P300 BCI. A total of 23 subjects used a P300 BCI to spell the word \"LUCAS\" while receiving real-time, closed-loop feedback. The dry system yielded classification accuracies that were similar to those obtained with gel systems. All subjects completed a questionnaire after data recording, and all subjects stated that the dry system was not uncomfortable. This is the first field validation of a dry electrode P300 BCI system, and paves the way for new research and development with EEG recording systems that are much more practical and convenient in field settings than conventional systems.