Industrial perspectives on brain-computer interface technology.

Neuromodulation therapies offer a unique opportunity for translating brain-computer interface (BCI) technologies into a clinical setting. Several diseases such as Parkinson's disease are effectively treated by invasive device stimulation therapies, and the addition of sensing and algorithm technology is an obvious evolutionary expansion of capabilities. In addition, this infrastructure might enable a roadmap of novel BCI technologies. While the initial applications are focused on epilepsy and movement disorders, the technology is potentially transferable to a broader base of disorders, including stroke and rehabilitation. The ultimate potential of BCI technology will be determined by forthcoming chronic evaluation in multiple neurologic disorders.

Continuous Assessment of Levodopa Response in Parkinson's Disease Using Wearable Motion Sensors.

OBJECTIVE: Fluctuations in response to levodopa in Parkinson's disease (PD) are difficult to treat as tools to monitor temporal patterns of symptoms are hampered by several challenges. The objective was to use wearable sensors to quantify the dose response of tremor, bradykinesia, and dyskinesia in individuals with PD. METHODS: Thirteen individuals with PD and fluctuating motor benefit were instrumented with wrist and ankle motion sensors and recorded by video. Kinematic data were recorded as subjects completed a series of activities in a simulated home environment through transition from off to on medication. Subjects were evaluated using the unified Parkinson disease rating scale motor exam (UPDRS-III) at the start and end of data collection. Algorithms were applied to the kinematic data to score tremor, bradykinesia, and dyskinesia. A blinded clinician rated severity observed on video. Accuracy of algorithms was evaluated by comparing scores with clinician ratings using a receiver operating characteristic (ROC) analysis. RESULTS: Algorithm scores for tremor, bradykinesia, and dyskinesia agreed with clinician ratings of video recordings (ROC area > 0.8). Summary metrics extracted from time intervals before and after taking medication provided quantitative measures of therapeutic response (p < 0.01). Radar charts provided intuitive visualization, with graphical features correlated with UPDRS-III scores (R = 0.81). CONCLUSION: A system with wrist and ankle motion sensors can provide accurate measures of tremor, bradykinesia, and dyskinesia as patients complete routine activities. SIGNIFICANCE: This technology could provide insight on motor fluctuations in the context of daily life to guide clinical management and aid in development of new therapies.

Computer-Guided Deep Brain Stimulation Programming for Parkinson's Disease.

OBJECTIVE: Pilot study to evaluate computer-guided deep brain stimulation (DBS) programming designed to optimize stimulation settings using objective motion sensor-based motor assessments. MATERIALS AND METHODS: Seven subjects (five males; 54-71 years) with Parkinson's disease (PD) and recently implanted DBS systems participated in this pilot study. Within two months of lead implantation, the subject returned to the clinic to undergo computer-guided programming and parameter selection. A motion sensor was placed on the index finger of the more affected hand. Software guided a monopolar survey during which monopolar stimulation on each contact was iteratively increased followed by an automated assessment of tremor and bradykinesia. After completing assessments at each setting, a software algorithm determined stimulation settings designed to minimize symptom severities, side effects, and battery usage. RESULTS: Optimal DBS settings were chosen based on average severity of motor symptoms measured by the motion sensor. Settings chosen by the software algorithm identified a therapeutic window and improved tremor and bradykinesia by an average of 35.7% compared with baseline in the "off" state (p < 0.01). CONCLUSIONS: Motion sensor-based computer-guided DBS programming identified stimulation parameters that significantly improved tremor and bradykinesia with minimal clinician involvement. Automated motion sensor-based mapping is worthy of further investigation and may one day serve to extend programming to populations without access to specialized DBS centers.

Motion sensor strategies for automated optimization of deep brain stimulation in Parkinson's disease.

BACKGROUND: Deep brain stimulation (DBS) is a well-established treatment for Parkinson's disease (PD). Optimization of DBS settings can be a challenge due to the number of variables that must be considered, including presence of multiple motor signs, side effects, and battery life. METHODS: Nine PD subjects visited the clinic for programming at approximately 1, 2, and 4 months post-surgery. During each session, various stimulation settings were assessed and subjects performed motor tasks while wearing a motion sensor to quantify tremor and bradykinesia. At the end of each session, a clinician determined final stimulation settings using standard practices. Sensor-based ratings of motor symptom severities collected during programming were then used to develop two automated programming algorithms--one to optimize symptom benefit and another to optimize battery life. Therapeutic benefit was compared between the final clinician-determined DBS settings and those calculated by the automated algorithm. RESULTS: Settings determined using the symptom optimization algorithm would have reduced motor symptoms by an additional 13 percentage points when compared to clinician settings, typically at the expense of increased stimulation amplitude. By adding a battery life constraint, the algorithm would have been able to decrease stimulation amplitude by an average of 50% while maintaining the level of therapeutic benefit observed using clinician settings for a subset of programming sessions. CONCLUSIONS: Objective assessment in DBS programming can identify settings that improve symptoms or obtain similar benefit as clinicians with improvement in battery life. Both options have the potential to improve post-operative patient outcomes.

Motion sensor dyskinesia assessment during activities of daily living.

BACKGROUND: Dyskinesia throughout the levodopa dose cycle has been previously measured in patients with Parkinson's disease (PD) using a wrist-worn motion sensor during the stationary tasks of arms resting and extended. Quantifying dyskinesia during unconstrained activities poses a unique challenge since these involuntary movements are kinematically similar to voluntary movement. OBJECTIVE: To determine the feasibility of using motion sensors to measure dyskinesia during activities of daily living. METHODS: Fifteen PD subjects performed scripted activities of daily living while wearing motion sensors on bilateral hands, thighs, and ankles over the course of a levodopa dose cycle. Videos were scored by clinicians using the modified Abnormal Involuntary Movement Scale to rate dyskinesia severity in separate body regions, with the total score used as an overall measure. Kinematic features were extracted from the motion data and algorithms were generated to output severity scores. RESULTS: Movements when subjects were experiencing dyskinesia were less smooth than when they were not experiencing dyskinesia. Dyskinesia scores predicted by the model using all sensors were highly correlated with clinician scores, with a correlation coefficient of 0.86 and normalized root-mean-square-error of 7.4%. Accurate predictions were maintained when two sensors on the most affected side of the body (one on the upper extremity and one on the lower extremity) were used. CONCLUSIONS: A system with motion sensors may provide an accurate measure of overall dyskinesia that can be used to monitor patients as they complete typical activities, and thus provide insight on symptom fluctuation in the context of daily life.

User-in-the-loop continuous and proportional control of a virtual prosthesis in a posture matching task.

As the development of dexterous prosthetic hand and wrist units continues, there is a need for command interfaces that will enable a user to operate these multi-joint devices in a natural, coordinated manner. In previous work, we have demonstrated that it is possible to simultaneously decode hand and wrist kinematics from myoelectric signals recorded from the forearm in an offline manner. The goal of this study was to quantify the performance of this command interface during real-time control of a kinematic prosthesis. One subject with intact limbs controlled a virtual prosthesis and attempted to match a series of target postures using the proposed control scheme as well as using the movements of the intact limb. Initial results indicate that subjects can complete these target matching tasks in the virtual environment. Future work will evaluate the controllability of the proposed strategy relative to traditional control schemes.

Electromyogram-based neural network control of transhumeral prostheses.

Upper-limb amputation can cause a great deal of functional impairment for patients, particularly for those with amputation at or above the elbow. Our long-term objective is to improve functional outcomes for patients with amputation by integrating a fully implanted electromyographic (EMG) recording system with a wireless telemetry system that communicates with the patient's prosthesis. We believe that this should generate a scheme that will allow patients to robustly control multiple degrees of freedom simultaneously. The goal of this study is to evaluate the feasibility of predicting dynamic arm movements (both flexion/extension and pronation/supination) based on EMG signals from a set of muscles that would likely be intact in patients with transhumeral amputation. We recorded movement kinematics and EMG signals from seven muscles during a variety of movements with different complexities. Time-delayed artificial neural networks were then trained offline to predict the measured arm trajectories based on features extracted from the measured EMG signals. We evaluated the relative effectiveness of various muscle subsets. Predicted movement trajectories had average root-mean-square errors of approximately 15.7° and 24.9° and average R(2) values of approximately 0.81 and 0.46 for elbow flexion/extension and forearm pronation/supination, respectively.

Virtual reality environment for simulating tasks with a myoelectric prosthesis: an assessment and training tool.

Intuitively and efficiently controlling multiple degrees of freedom is a major hurdle in the field of upper limb prosthetics. A virtual reality myoelectric transhumeral prosthesis simulator has been developed for cost-effectively testing novel control algorithms and devices. The system acquires EMG commands and residual limb kinematics, simulates the prosthesis dynamics, and displays the combined residual limb and prosthesis movements in a virtual reality environment that includes force-based interactions with virtual objects. A virtual Box and Block Test is demonstrated. Three normally-limbed subjects performed the simulated test using a sequential and a synchronous control method. With the sequential method, subjects moved on average 6.7±1.9 blocks in 120 seconds, similar to the number of blocks transhumeral amputees are able to move with their physical prostheses during clinical evaluation. With the synchronous method, subjects moved 6.7±2.2 blocks. The virtual reality prosthesis simulator is thus a promising tool for developing and evaluating control methods, prototyping novel prostheses, and training amputees.