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The rise of adaptive stimulation approaches has shown great therapeutic promise in the growing field of neuromodulation. The discovery and growth of these novel adaptive stimulation paradigms has been largely concentrated around several implantable devices with research application programming interfaces (APIs) that allow for custom applications to be created for clinical neuromodulation studies. However, the sunsetting of devices and ongoing development of new platforms is leading to an increased fragmentation in the research environment- resulting in the reinvention of system features and the inability to leverage previous development efforts for future studies. The Open Mind Neuromodulation Interface (OMNI) is a previously proposed solution to address the weaknesses of the DLL-driven API approach of past neuromodulation research by utilizing an alternative gRPC-enabled microservice framework. Here, we introduce OMNI-BIC, an implementation of the OMNI framework to the CorTec Brain Interchange system. This paper describes the design and implementation of the OMNI-BIC software tools and demonstrates the framework’s capabilities for implementing customized neuromodulation therapies for clinical investigations. Through the development and deployment of the OMNI-BIC system, we hope to improve future clinical studies with the Brain Interchange system and aid in continuing the growth and momentum of the exciting field of adaptive neuromodulation.

Deep brain stimulation (DBS) delivers electrical stimulation directly to brain tissue to treat neurological movement disorders such as Parkinson’s Disease (PD). Adaptive DBS (aDBS) is an advancement on DBS that uses symptom-related biomarkers to adjust therapeutic stimulation parameters in real time to improve clinical outcomes and reduce side-effects. A significant challenge for the field of aDBS is developing automated methods to optimize stimulation parameters using remote assessments of symptom severity. To address this challenge, we designed a prototype at-home data collection platform that can remotely update aDBS algorithms and explore objective assessments of motor symptom severity. Our platform collects neural, inertial, and video data, and supports clinician validation of automated symptom assessments. We deployed the system to the home of an individual with PD and collected pilot data across six days. We evaluated motor symptom severity by recording data with stimulation amplitudes set to varying levels during self-guided clinical tasks and free behavior. We assessed movement features including frequency, speed, and peak angular velocity from video-derived pose estimates and inertial data during three clinical tasks. All features showed a reduction during periods of under-stimulation and were significantly correlated with video-based clinical scores of symptom severity (Spearman rank test, p < 0.006). These results demonstrate that our prototype is capable of remote multimodal data collection and that these data can enhance aDBS research outside the clinic.

Objective. Epilepsy is one of the most common neurological disorders and can have a devastating effect on a person’s quality of life. As such, the search for markers which indicate an upcoming seizure is a critically important area of research which would allow either on-demand treatment or early warning for people suffering with these disorders. There is a growing body of work which uses machine learning methods to detect pre-seizure biomarkers from electroencephalography (EEG), however the high prediction rates published do not translate into the clinical setting. Our objective is to investigate a potential reason for this. Approach. We conduct an empirical study of a commonly used data labelling method for EEG seizure prediction which relies on labelling small windows of EEG data in temporal groups then selecting randomly from those windows to validate results. We investigate a confound for this approach for seizure prediction and demonstrate the ease at which it can be inadvertently learned by a machine learning system. Main results. We find that non-seizure signals can create decision surfaces for machine learning approaches which can result in false high prediction accuracy on validation datasets. We prove this by training an artificial neural network to learn fake seizures (fully decoupled from biology) in real EEG. Significance. The significance of our findings is that many existing works may be reporting results based on this confound and that future work should adhere to stricter requirements in mitigating this confound. The problematic, but commonly accepted approach in the literature for seizure prediction labelling is potentially preventing real advances in developing solutions for these sufferers. By adhering to the guidelines in this paper future work in machine learning seizure prediction is more likely to be clinically relevant.

There is no rapid formula for student success in higher education. Understanding student development theories and leading contributions to student success can be helpful in creating a success plan for students of all demographics. Research indicates that mentoring can significantly increase student retention and their ability to develop as a person and as a student. Mentoring programs can also be focused on specific demographics of students, such as race or ethnicity. Other mentoring programs are designed around different student populations, such as first-generation students, by classification, or separated by majors. Due to the diversity of mentoring programs, a mentor can come from multiple roles within the institution, including counselor, therapist, academic advisor, dean of students, professor, financial aid counselor, or success coach. Essentially anyone at the institution could be a mentor.

Objective. Deep brain stimulation (DBS) programming for movement disorders requires systematic fine tuning of stimulation parameters to ameliorate tremor and other symptoms while avoiding side effects. DBS programming can be a time-consuming process and requires clinical expertise to assess response to DBS to optimize therapy for each patient. In this study, we describe and evaluate an automated, closed-loop, and patient-specific framework for DBS programming that measures tremor using a smartwatch and automatically changes DBS parameters based on the recommendations from a closed-loop optimization algorithm thus eliminating the need for an expert clinician. Approach. Bayesian optimization which is a sample-efficient global optimization method was used as the core of this DBS programming framework to adaptively learn each patient’s response to DBS and suggest the next best settings to be evaluated. Input from a clinician was used initially to define a maximum safe amplitude, but we also implemented ‘safe Bayesian optimization’ to automatically discover tolerable exploration boundaries. Main results. We tested the system in 15 patients (nine with Parkinson’s disease and six with essential tremor). Tremor suppression at best automated settings was statistically comparable to previously established clinical settings. The optimization algorithm converged after testing 15.1 ± 0.7 settings when maximum safe exploration boundaries were predefined, and 17.7 ± 4.9 when the algorithm itself determined safe exploration boundaries. Significance. We demonstrate that fully automated DBS programming framework for treatment of tremor is efficient and safe while providing outcomes comparable to that achieved by expert clinicians.

Virtual reality (VR) offers a robust platform for human behavioral neuroscience, granting unprecedented experimental control over every aspect of an immersive and interactive visual environment. VR experiments have already integrated non-invasive neural recording modalities such as EEG and functional MRI to explore the neural correlates of human behavior and cognition. Integration with implanted electrodes would enable significant increase in spatial and temporal resolution of recorded neural signals and the option of direct brain stimulation for neurofeedback. In this paper, we discuss the first such implementation of a VR platform with implanted electrocorticography (ECoG) and stereo-electroencephalography ( sEEG) electrodes in human, in-patient subjects. Noise analyses were performed to evaluate the effect of the VR headset on neural data collected in two VR-naive subjects, one child and one adult, including both ECOG and sEEG electrodes. Results demonstrate an increase in line noise power (57-63Hz) while wearing the VR headset that is mitigated effectively by common average referencing (CAR), and no significant change in the noise floor bandpower (125-240Hz). To our knowledge, this study represents first demonstrations of VR immersion during invasive neural recording with in-patient human subjects. Clinical Relevance- Immersive virtual reality tasks were well-tolerated and the quality of clinical neural signals preserved during VR immersion with two in-patient invasive neural recording subjects.

Directional DBS electrodes are a novel probe design enabling targeted stimulation of subcortical structures. Here, we leverage methods of broadband time-amplitude correlation and spectral power comparison to demonstrate and characterize the unique directional sensing ability of these novel probes. This work avoids preset frequency banding to highlight the electrode- and anatomy-specific spectral characteristics within each subject's neural recordings. Using precise whole-brain reconstruction, spectral features are associated with specific subcortical structures across six patients implanted for treatment of neurological movement disorders, supporting the utility of directional leads as a tool for biomarker sensing and discovery.

Closed-loop deep brain stimulation is a novel form of therapy that has shown benefit in preliminary studies and may be clinically available in the near future. Initial closed-loop studies have primarily focused on responding to sensed biomarkers with adjustments to stimulation amplitude, which is often perceptible to study participants depending on the slew or “ramp” rate of the amplitude changes. These subjective responses to stimulation ramping can result in transient side effects, illustrating that ramp rate is a unique safety parameter for closed-loop neural systems. This presents a challenge to the future of closed-loop neuromodulation systems: depending on the goal of the control policy, clinicians will need to balance ramp rates to avoid side effects and keep the stimulation therapeutic by responding in time to affect neural dynamics. In this paper, we demonstrate the results of an initial investigation into methodology for finding safe and tolerable ramp rates in four people with Parkinson's disease (PD). Results suggest that optimal ramp rates were found more accurately during varying stimulation when compared to simply toggling between maximal and minimal intensity levels. Additionally, switching frequency instantaneously was tolerable at therapeutic levels of stimulation. Future work should focus on including optimization techniques to find ramp rates.

There is growing interest in using adaptive neuro-modulation to provide a more personalized therapy experience that might improve patient outcomes. Current implant technology, however, can be limited in its adaptive algorithm capability. To enable exploration of adaptive algorithms with chronic implants, we designed and validated the, ‘Picostim DyNeuMo Mk-1’, (DyNeuMo Mk-1 for short), a fully-implantable, adaptive research stimulator that titrates stimulation based on circadian rhythms (e.g. sleep, wake) and the patient’s movement state (e.g. posture, activity, shock, free-fall). The design leverages off-the-shelf consumer technology that provides inertial sensing with low-power, high reliability, and relatively modest cost. The DyNeuMo Mk-1 system was designed, manufactured and verified using ISO 13485 design controls, including ISO 14971 risk management techniques to ensure patient safety, while enabling novel algorithms. The system was validated for an intended use case in movement disorders under an emergency-device authorization from the Medicines and Healthcare Products Regulatory Agency (MHRA). The algorithm configurability and expanded stimulation parameter space allows for a number of applications to be explored in both central and peripheral applications. Intended applications include adaptive stimulation for movement disorders, synchronizing stimulation with circadian patterns, and reacting to transient inertial events such as posture changes, general activity, and walking. With appropriate design controls in place, first-in-human research trials are now being prepared to explore the utility of automated motion-adaptive algorithms.

The history and current practices of out-of-school suspensions significantly impact African-American students; research shows the practices to be overly used and target African-American students. This chapter explores the ways that school violence is responded to disproportionally and is entangled with racial mythology. That is, racial discrimination shows up in structural and interpersonal ways such as suspending and expelling students of Color for the same infractions for which White peers get to return to class such as kicking a trashcan, defiance, and truancies. The chapter closes with recommendations for educators and policymakers, focusing on ways to mitigate the impact of out-of-school suspension practices and racial discrimination in order to improve the future of learning, school discipline, and outcomes of African-American students.

Studying the neural correlates of sleep can lead to revelations in our understanding of sleep and its interplay with different neurological disorders. Sleep research relies on manual annotation of sleep stages based on rules developed for healthy adults. Automating sleep stage annotation can expedite sleep research and enable us to better understand atypical sleep patterns. Our goal was to create a fully unsupervised approach to label sleep and wake states in human electro-corticography (ECoG) data from epilepsy patients. Here, we demonstrate that with continuous data from a single ECoG electrode, hidden semi-Markov models (HSMM) perform best in classifying sleep/wake states without excessive transitions, with a mean accuracy (n=4) of 85.2% compared to using K-means clustering (72.2%) and hidden Markov models (81.5%). Our results confirm that HSMMs produce meaningful labels for ECoG data and establish the groundwork to apply this model to cluster sleep stages and potentially other behavioral states.

Drawing tasks, such as completing an Archimedes spiral, are commonly used to diagnose and evaluate tremor severity in individuals presenting with tremor-like symptoms. Clinical evaluation of these tasks is generally restricted to having a clinician examine and rate performance using one of several rating scales. Although this method is relatively effective in tremor assessment, it restricts tremor assessment to clinical settings and requires each assessment be evaluated individually, placing considerable time demands on both patients and clinicians. Here is presented the first multi-class approach to evaluation of tremor severity based on data remotely recorded by a mobile- or tablet-based drawing application. Taking a divergent approach from previously published work, which focuses solely on binary diagnostic capacity of similar systems, our work seeks to differentiate between healthy subjects, essential tremor patients receiving deep brain stimulation treatment, and those same patients with treatment disabled. Our classification algorithm was highly effective, with overall accuracy of 97.04%. Of note is that all erroneously classified samples within this set were ”treated” samples mistakenly labelled as ”healthy” samples, implying that DBS treatment is capable of completely eliminating tremor. Future work will focus on adapting these findings into a regression algorithm capable of automatically evaluating tremor severity on an established tremor rating scale, allowing fully automated remote assessment of tremor severity for the coming era of telemedicine.

There is growing interest in using adaptive neuro-modulation to provide a more personalized therapy experience that might improve patient outcomes. Current implant technology, however, can be limited in its adaptive algorithm capability. To enable exploration of adaptive algorithms with chronic implants, we designed and validated the ‘Picostim DyNeuMo Mk-1’ (DyNeuMo Mk-1 for short), a fully-implantable, adaptive research stimulator that titrates stimulation based on circadian rhythms (e.g. sleep, wake) and the patient’s movement state (e.g. posture, activity, shock, free-fall). The design leverages off-the-shelf consumer technology that provides inertial sensing with low-power, high reliability, and relatively modest cost. The DyNeuMo Mk-1 system was designed, manufactured and verified using ISO 13485 design controls, including ISO 14971 risk management techniques to ensure patient safety, while enabling novel algorithms. The system was validated for an intended use case in movement disorders under an emergency-device authorization from the Medicines and Healthcare Products Regulatory Agency (MHRA). The algorithm configurability and expanded stimulation parameter space allows for a number of applications to be explored in both central and peripheral applications. Intended applications include adaptive stimulation for movement disorders, synchronizing stimulation with circadian patterns, and reacting to transient inertial events such as posture changes, general activity, and walking. With appropriate design controls in place, first-in-human research trials are now being prepared to explore the utility of automated motion-adaptive algorithms.

Increased beta band synchrony has been demonstrated to be a biomarker of Parkinson’s disease (PD). This abnormal synchrony can often be prolonged in long bursts of beta activity, which may interfere with normal sensorimotor processing. Previous closed loop deep brain stimulation (DBS) algorithms used averaged beta power to drive neurostimulation, which were indiscriminate to physiological (short) versus pathological (long) beta burst durations. We present a closed-loop DBS algorithm using beta burst duration as the control signal. Benchtop validation results demonstrate the feasibility of the algorithm in real-time by responding to pre-recorded STN data from a PD participant. These results provide the basis for future improved closed-loop algorithms focused on burst durations for in mitigating symptoms of PD.

Impaired gait in Parkinson’s disease is marked by slow, arrhythmic stepping, and often includes freezing of gait episodes where alternating stepping halts completely. Wearable inertial sensors offer a way to detect these gait changes and novel deep brain stimulation (DBS) systems can respond with clinical therapy in a real-time, closed-loop fashion. In this paper, we present two novel closed-loop DBS algorithms, one using gait arrhythmicity and one using a logistic-regression model of freezing of gait detection as control signals. Benchtop validation results demonstrate the feasibility of running these algorithms in conjunction with a closed-loop DBS system by responding to real-time human subject kinematic data and pre-recorded data from leg-worn inertial sensors from a participant with Parkinson’s disease. We also present a novel control policy algorithm that changes neurostimulator frequency in response to the kinematic inputs. These results provide a foundation for further development, iteration, and testing in a clinical trial for the first closed-loop DBS algorithms using kinematic signals to therapeutically improve and understand the pathophysiological mechanisms of gait impairment in Parkinson’s disease.

Deep brain stimulation (DBS) is a safe and established treatment for essential tremor (ET). However, there remains considerable room for improvement due to concerns associated with the initial implant surgery, semi-regular revision surgeries for battery replacements, and side effects including paresthesia, gait ataxia, and emotional disinhibition that have been associated with continuous, or conventional, DBS (cDBS) treatment. Adaptive DBS (aDBS) seeks to ameliorate some of these concerns by using feedback from either an external wearable or implanted sensor to modulate stimulation parameters as needed. aDBS has been demonstrated to be as or more effective than cDBS, but the purely binary control system most commonly deployed by aDBS systems likely still provides sub-optimal treatment and may introduce new issues. One example of these issues is rebound effect, in which the tremor symptoms of an ET patient receiving DBS therapy temporarily worsen after cessation of stimulation before leveling out to a steady state. Here is presented a quantitative analysis of rebound effect in 3 patients receiving DBS for ET. Rebound was evident in all 3 patients by both clinical assessment and inertial measurement unit data, peaking by the latter at T p = 6.65 minutes after cessation of stimulation. Using features extracted from neural data, linear regression was applied to predict tremor severity, with $R_{avg{\text{ }}}^2 = 0.82$. These results strongly suggest that rebound effect and the additional information made available by rebound effect should be considered and exploited when designing novel aDBS systems.

One significant hindrance to effective diagnosis of movement disorders (MDs) and analysis of their progression is the requirement for patients to conduct tests in the presence of a clinician. Here is presented a pilot study for diagnosis of essential tremor (ET), the world's most common MD, through analysis of a tablet- or mobile-based drawing task that may be selected at will, with the spiral- and line-drawing tasks of the Fahn-Tolosa-Marin tremor rating scale serving as our task in this work. This system replaces the need for pen-and-paper drawing tests while permitting advanced quantitative analysis of drawing smoothness, pressure applied, and other measures. Data is securely recorded and stored in the cloud, from which all analysis was conducted remotely. This will enable longitudinal analysis of patient disease progression without the need for excessive clinical visits. Several features were extracted and recursive feature elimination applied to rank the features' individual contribution to our classifier. Maximum cross-validated classification accuracy on a preliminary sample set was 98.3%. Future work will involve collecting healthy subject data from an age-controlled population and extending this diagnostic application to additional conditions, as well as incorporating regression-based symptom severity analysis. This highly promising new technology has the potential to substantially alleviate the demands placed on both clinicians and patients by bringing MD treatment more into line with the era of personalized medicine.

Objective: Deep brain stimulation (DBS) is a safe and established treatment for essential tremor (ET) and several other movement disorders. One approach to improving DBS therapy is adaptive DBS (aDBS), in which stimulation parameters are modulated in real time based on biofeedback from either external or implanted sensors. Previously tested systems have fallen short of translational applicability due to the requirement for patients to continuously wear the necessary sensors or processing devices, as well as privacy and security concerns. Approach: We designed and implemented a translation-ready training data collection system for fully implanted aDBS. Two patients chronically implanted with electrocorticography strips over the hand portion of M1 and DBS probes in the ipsilateral ventral intermediate nucleus of the thalamus for treatment of ET were recruited for this study. Training was conducted using a translation-ready distributed training procedure, allowing a substantially higher degree of control over data collection than previous works. A linear classifier was trained using this system, biased towards activating stimulation in accordance with clinical considerations. Main Results: The clinically relevant average false negative rate, defined as fraction of time during which stimulation dropped below 1/2 clinical levels during movement epochs, was 0.036. Tremor suppression, calculated through analysis of gyroscope data, was 33.2% more effective on average with aDBS than with continuous DBS. During a period of free movement with aDBS, one patient reported a slight paresthesia; patients noticed no difference in treatment efficacy between systems. Significance: Here is presented the first translation-ready training procedure for a fully embedded aDBS control system for MDs and one of the first examples of such a system in ET, adding to the consensus that fully implanted aDBS systems are sufficiently mature for broader deployment in treatment of movement disorders.

Brain stimulation has emerged as an effective treatment for a wide range of neurological and psychiatric diseases. Parkinson’s disease, epilepsy, and essential tremor have FDA indications for electrical brain stimulation using intracranially implanted electrodes. Interfacing implantable brain devices with local and cloud computing resources have the potential to improve electrical stimulation efficacy, disease tracking, and management. Epilepsy, in particular, is a neurological disease that might benefit from the integration of brain implants with off-the-body computing for tracking disease and therapy. Recent clinical trials have demonstrated seizure forecasting, seizure detection, and therapeutic electrical stimulation in patients with drug-resistant focal epilepsy. In this paper, we describe a next-generation epilepsy management system that integrates local handheld and cloud-computing resources wirelessly coupled to an implanted device with embedded payloads (sensors, intracranial EEG telemetry, electrical stimulation, classifiers, and control policy implementation). The handheld device and cloud computing resources can provide a seamless interface between patients and physicians, and realtime intracranial EEG can be used to classify brain state (wake/sleep, preseizure, and seizure), implement control policies for electrical stimulation, and track patient health. This system creates a flexible platform in which low demand analytics requiring fast response times are embedded in the implanted device and more complex algorithms are implemented in offthebody local and distributed cloud computing environments. The system enables tracking and management of epileptic neural networks operating over time scales ranging from milliseconds to months., Brain Implants integrated with Local and Distributed Computing Devices provide a seamless interface between patients and physicians, and real-time intracranial EEG can be used to classify brain state (wake/sleep, pre-seizure, seizure), implement control policies for electrical stimulation, and track patient health. The system enables tracking and management of epileptic neural networks operating over time scales ranging from milliseconds to months.

Essential Tremor is the most common neurological movement disorder. This progressive disease causes uncontrollable rhythmic motions -most often affecting the patient's dominant upper extremity- that occur during volitional movement and make it difficult for the patient to perform everyday tasks. Medication may also become ineffective as the disorder progresses. For many patients, deep brain stimulation (DBS) of the thalamus is an effective means of treating this condition when medication fails. In current use, however, clinicians set the patient's stimulator to apply stimulation at all times- whether it is needed or not. This practice leads to excess power use, and more rapid depletion of batteries that require surgical replacement. In the work described here, for the first time, neural sensing of movement (using chronically-implanted cortical electrodes) is used to enable or disable stimulation for tremor. Therapeutic stimulation is delivered only when the patient is actively using their effected limb, thereby reducing the total stimulation applied, and potentially extending the lifetime of surgically-implanted batteries. This work, which involves both implanted and external subsystems, paves the way for the future fully-implanted closed-loop deep brain stimulators.

Deep brain stimulation (DBS) has become a widespread and valuable treatment for patients with movement disorders such as essential tremor (ET). However, current DBS treatment constantly delivers stimulation in an open loop, which can be inefficient. Closing the loop with sensors to provide feedback may increase power efficiency and reduce side effects for patients. New implantable neuromodulation platforms, such as the Medtronic Activa PC+S DBS system, offer important data sources by providing chronic neural sensing capabilities and a means of investigating dynamic stimulation based on symptom measurements. The authors implanted in a single patient with ET an Activa PC+S system, a cortical strip of electrodes on the hand sensorimotor cortex, and therapeutic electrodes in the ventral intermediate nucleus of the thalamus. In this paper they describe the effectiveness of the platform when sensing cortical movement intentions while the patient actually performed and imagined performing movements. Additionally, they demonstrate dynamic closed-loop DBS based on several wearable sensor measurements of tremor intensity.

Neurostimulation is used to treat a variety of neurological diseases. Historically, these implantable neurostimulator systems have relied on providing therapeutic benefit to patients using tonic, or open-loop, stimulation. In this paper, we review the state of the art in current spinal cord stimulation therapy to identify the needs of the field for a new tool to enable new research. We then present an overview of the design and capabilities of the Nexus-I Research Development Kit for investigational human-use research activities for patients who have been implanted with the Model 97715 Intellis neurostimulator.

Deep brain stimulators (DBS) are a neurotechnological means of treating a variety of movement disorders, including essential tremor (ET). Current stimulation systems apply an electrical current to targets in the brain at a constant rate for as long as they are implanted and activated – treating symptoms but causing uncomfortable side-effects and inefficient power usage. Some users feel estranged or isolated for various reasons. Next-generation DBS systems could use the patient’s self-modulated neural signals to trigger stimulation. These brain-computer interface-triggered DBS (BCI-DBS) systems would give the user the ability to moderate side-effects and reduce battery power consumption. It’s not yet clear, however, whether neural control will alleviate or exacerbate psychosocial problems. To explore these concerns, we conducted interviews with an ET patient using an experimental BCI-DBS platform. Our interviews offer preliminary insights about what problems ET patients may face while using BCI-DBS.

Objective. Deep brain stimulation (DBS) is a safe and established treatment for essential tremor (ET) and several other movement disorders. One approach to improving DBS therapy is adaptive DBS (aDBS), in which stimulation parameters are modulated in real time based on biofeedback from either external or implanted sensors. Previously tested systems have fallen short of translational applicability due to the requirement for patients to continuously wear the necessary sensors or processing devices, as well as privacy and security concerns. Approach. We designed and implemented a translation-ready training data collection system for fully implanted aDBS. Two patients chronically implanted with electrocorticography strips over the hand portion of M1 and DBS probes in the ipsilateral ventral intermediate nucleus of the thalamus for treatment of ET were recruited for this study. Training was conducted using a translation-ready distributed training procedure, allowing a substantially higher degree of control over data collection than previous works. A linear classifier was trained using this system, biased towards activating stimulation in accordance with clinical considerations. Main results. The clinically relevant average false negative rate, defined as fraction of time during which stimulation dropped below 1 2 clinical levels during movement epochs, was 0.036. Tremor suppression, calculated through analysis of gyroscope data, was 33.2% more effective on average with aDBS than with continuous DBS. During a period of free movement with aDBS, one patient reported a slight paresthesia; patients noticed no difference in treatment efficacy between systems. Significance. Here is presented the first translation-ready training procedure for a fully embedded aDBS control system for MDs and one of the first examples of such a system in ET, adding to the consensus that fully implanted aDBS systems are sufficiently mature for broader deployment in treatment of movement disorders.