Machine learning seizure prediction: one problematic but accepted practice.

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.

Closing the Loop With Cortical Sensing: The Development of Adaptive Deep Brain Stimulation for Essential Tremor Using the Activa PC+S.

Deep Brain Stimulation (DBS) is an important tool in the treatment of pharmacologically resistant neurological movement disorders such as essential tremor (ET) and Parkinson's disease (PD). However, the open-loop design of current systems may be holding back the true potential of invasive neuromodulation. In the last decade we have seen an explosion of activity in the use of feedback to "close the loop" on neuromodulation in the form of adaptive DBS (aDBS) systems that can respond to the patient's therapeutic needs.

Robustness of Beta Desynchronization from Chronically Implanted Cortical Electrodes on Multiple Time Scales.

Adaptive deep brain stimulation (aDBS) promises a significant improvement in patient outcomes, compared to existing deep brain stimulation devices. Fully implanted systems represent the next step to the clinical adoption of aDBS. We take advantage of a unique longitudinal data set formed as part of an effort to investigate aDBS for essential tremor to verify the long term reliability of electrocorticography strips over the motor cortex as a source of bio-markers for control of adaptive stimulation.

A Pilot Study on Data-Driven Adaptive Deep Brain Stimulation in Chronically Implanted Essential Tremor Patients.

Deep brain stimulation (DBS) is an established therapy for Parkinson's disease (PD) and essential-tremor (ET). In adaptive DBS (aDBS) systems, online tuning of stimulation parameters as a function of neural signals may improve treatment efficacy and reduce side-effects. State-of-the-art aDBS systems use symptom surrogates derived from neural signals-so-called neural markers (NMs)-defined on the patient-group level, and control strategies assuming stationarity of symptoms and NMs.

A tablet- and mobile-based application for remote diagnosis and analysis of movement disorder symptoms.

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.

Rebound effect in deep brain stimulation for essential tremor and symptom severity estimation from neural data.

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.

Proceedings of the Sixth Deep Brain Stimulation Think Tank Modulation of Brain Networks and Application of Advanced Neuroimaging, Neurophysiology, and Optogenetics.

The annual deep brain stimulation (DBS) Think Tank aims to create an opportunity for a multidisciplinary discussion in the field of neuromodulation to examine developments, opportunities and challenges in the field. The proceedings of the Sixth Annual Think Tank recapitulate progress in applications of neurotechnology, neurophysiology, and emerging techniques for the treatment of a range of psychiatric and neurological conditions including Parkinson's disease, essential tremor, Tourette syndrome, epilepsy, cognitive disorders, and addiction. Each section of this overview provides insight about the understanding of neuromodulation for specific disease and discusses current challenges and future directions.

A machine-learning approach to volitional control of a closed-loop deep brain stimulation system.

Objective: Deep brain stimulation (DBS) is a well-established treatment for essential tremor, but may not be an optimal therapy, as it is always on, regardless of symptoms. A closed-loop (CL) DBS, which uses a biosignal to determine when stimulation should be given, may be better. Cortical activity is a promising biosignal for use in a closed-loop system because it contains features that are correlated with pathological and normal movements.

Automated Deep Brain Stimulation Programming for Tremor.

Deep brain stimulation (DBS) programming, the systematic selection of fixed electrical stimulation parameters that deliver maximal therapeutic benefit while limiting side effects, poses several challenges in the treatment of movement disorders. DBS programming requires the expertise of trained neurologists or nurses who assess patient symptoms according to standardized clinical rating scales and use patient reports of DBS-related side effects to adjust stimulation parameters and optimize therapy. In this paper, we describe and validate an automated software platform for DBS programming for tremor associated with Parkinson's disease and essential tremor.

Adaptive deep brain stimulation for Parkinson's disease using motor cortex sensing.

Objective: Contemporary deep brain stimulation (DBS) for Parkinson's disease is delivered continuously, and adjustments based on patient's changing symptoms must be made manually by a trained clinician. Patients may be subjected to energy intensive settings at times when they are not needed, possibly resulting in stimulation-induced adverse effects, such as dyskinesia. One solution is 'adaptive' DBS, in which stimulation is modified in real time based on neural signals that co-vary with the severity of motor signs or of stimulation-induced adverse effects.

Run-to-Run Optimization Control Within Exact Inverse Framework for Scan Tracking.

A run-to-run optimization controller uses a reduced set of measurement parameters, in comparison to more general feedback controllers, to converge to the best control point for a repetitive process. A new run-to-run optimization controller is presented for the scanning fiber device used for image acquisition and display. This controller utilizes very sparse measurements to estimate a system energy measure and updates the input parameterizations iteratively within a feedforward with exact-inversion framework.

Cortical Brain-Computer Interface for Closed-Loop Deep Brain Stimulation.

Essential tremor is the most common neurological movement disorder. This progressive disease causes uncontrollable rhythmic motions-most often affecting the patient'sdominant upper extremity-thatoccur during volitional movement and make it difficult for the patient to perform everyday tasks. Medication may also become ineffective as the disorder progresses.

Proceedings of the Fourth Annual Deep Brain Stimulation Think Tank: A Review of Emerging Issues and Technologies.

This paper provides an overview of current progress in the technological advances and the use of deep brain stimulation (DBS) to treat neurological and neuropsychiatric disorders, as presented by participants of the Fourth Annual DBS Think Tank, which was convened in March 2016 in conjunction with the Center for Movement Disorders and Neurorestoration at the University of Florida, Gainesveille FL, USA. The Think Tank discussions first focused on policy and advocacy in DBS research and clinical practice, formation of registries, and issues involving the use of DBS in the treatment of Tourette Syndrome. Next, advances in the use of neuroimaging and electrochemical markers to enhance DBS specificity were addressed.

Chronic electrocorticography for sensing movement intention and closed-loop deep brain stimulation with wearable sensors in an essential tremor patient.

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.

Empirical Movement Models for Brain Computer Interfaces.

For brain-computer interfaces (BCIs) which provide the user continuous position control, there is little standardization of performance metrics or evaluative tasks. One candidate metric is Fitts's law, which has been used to describe aimed movements across a range of computer interfaces, and has recently been applied to BCI tasks. Reviewing selected studies, we identify two basic problems with Fitts's law: its predictive performance is fragile, and the estimation of 'information transfer rate' from the model is unsupported.

Proceedings of the Third Annual Deep Brain Stimulation Think Tank: A Review of Emerging Issues and Technologies.

The proceedings of the 3rd Annual Deep Brain Stimulation Think Tank summarize the most contemporary clinical, electrophysiological, imaging, and computational work on DBS for the treatment of neurological and neuropsychiatric disease. Significant innovations of the past year are emphasized. The Think Tank's contributors represent a unique multidisciplinary ensemble of expert neurologists, neurosurgeons, neuropsychologists, psychiatrists, scientists, engineers, and members of industry.

Long-term detection of Parkinsonian tremor activity from subthalamic nucleus local field potentials.

Current deep brain stimulation paradigms deliver continuous stimulation to deep brain structures to ameliorate the symptoms of Parkinson's disease. This continuous stimulation has undesirable side effects and decreases the lifespan of the unit's battery, necessitating earlier replacement. A closed-loop deep brain stimulator that uses brain signals to determine when to deliver stimulation based on the occurrence of symptoms could potentially address these drawbacks of current technology.

Motor cortical decoding performance depends on controlled system order.

Recent advances in intracortical brain-machine interfaces (BMIs) for position control have leveraged state estimators to decode intended movements from cortical activity. We revisit the underlying assumptions behind the use of Kalman filters in this context, focusing on the fact that identified cortical coding models capture closed-loop task dynamics. We show that closed-loop models can be partitioned, exposing feedback policies of the brain which are separate from interface and task dynamics.

A proxy method for real-time 3-DOF haptic rendering of streaming point cloud data.

This paper presents a new haptic rendering method for streaming point cloud data. It provides haptic rendering of moving physical objects using data obtained from RGB-D cameras. Thus, real-time haptic interaction with moving objects can be achieved using noncontact sensors.

Creating the feedback loop: closed-loop neurostimulation.

Current DBS therapy delivers a train of electrical pulses at set stimulation parameters. This open-loop design is effective for movement disorders, but therapy may be further optimized by a closed loop design. The technology to record biosignals has outpaced our understanding of their relationship to the clinical state of the whole person.

Applying best practices from digital control systems to BMI implementation.

Many brain-machine interface (BMI) algorithms, such as the population vector decoder, must estimate neural spike rates before transforming this information into an external output signal. Often, rate estimation is performed via the selection of a bin width corresponding to the effective sampling rate of the decoding algorithm. Here, we implement real-time rate estimation by extending prior work on the optimization of Gaussian filters for offline rate estimation.

Lower bounds on the optimal control of soft tissue grasping.

The interaction between surgical tools and soft tissue has nonlinear mechanical properties. In robot-assisted surgery, a pre-specified trajectory of tool positions (or applied forces) is desired for achieving a surgical treatment. Due to the computational complexity involved in solving the nonlinear optimal trajectory following problem, a tractable path is to find lower bounds on the optimal cost.