Regression-based reconstruction of human grip force trajectories with noninvasive scalp electroencephalography.

OBJECTIVE: Robotic devices show promise in restoring motor abilities to individuals with upper limb paresis or amputations. However, these systems are still limited in obtaining reliable signals from the human body to effectively control them. We propose that these robotic devices can be controlled through scalp electroencephalography (EEG), a neuroimaging technique that can capture motor commands through brain rhythms. In this work, we studied if EEG can be used to predict an individual's grip forces produced by the hand. APPROACH: Brain rhythms and grip forces were recorded from able-bodied human subjects while they performed an isometric force production task and a grasp-and-lift task. Grip force trajectories were reconstructed with a linear model that incorporated delta band (0.1-1 Hz) voltage potentials and spectral power in the theta (4-8 Hz), alpha (8-13 Hz), beta (13-30 Hz), low gamma (30-50 Hz), mid gamma (70-110 Hz), and high gamma (130-200 Hz) bands. Trajectory reconstruction models were trained and tested through 10-fold cross validation. MAIN RESULTS: Modest accuracies were attained in reconstructing grip forces during isometric force production (median r = 0.42), and the grasp-and-lift task (median r = 0.51). Predicted trajectories were also analyzed further to assess the linear models' performance based on task requirements. For the isometric force production task, we found that predicted grip trajectories did not yield static grip forces that were distinguishable in magnitude across three task conditions. For the grasp-and-lift task, we estimate there would be an approximate 25% error in distinguishing when a user wants to hold or release an object. SIGNIFICANCE: These findings indicate that EEG, a noninvasive neuroimaging modality, has predictive information in neural features associated with finger force control and can potentially contribute to the development of brain machine interfaces (BMI) for performing activities of daily living.

Grasp Performance of a Soft Synergy-Based Prosthetic Hand: A Pilot Study.

Current prosthetic hands are frequently rejected in part due to limited functionality and versatility. We assessed the feasibility of a novel prosthetic hand, the SoftHand Pro (SHP), whose design combines soft robotics and hand postural synergies. Able-bodied subjects ( ) tracked cursor motion by opening and closing the SHP and performed a grasp-lift-hold-release (GLHR) task with a sensorized cylindrical object of variable weight. The SHP control was driven by electromyographic (EMG) signals from two antagonistic muscles. Although the time to perform the GLHR task was longer for the SHP than native hand for the first few trials (10.2 ± 1.4 s and 2.13 ± 0.09 s, respectively), performance was much faster on subsequent trials (~5 s). The SHP steady-state grip force was significantly modulated as a function of object weight ( ). For the native hand, however, peak and steady-state grip forces were modulated to a greater extent (+68% and +91%, respectively). These changes were mediated by the modulation of EMG amplitude and co-contraction. These data suggest that the SHP has a promise for prosthetic applications and point-to-design modifications that could improve the SHP.

Proof of Concept of an Online EMG-Based Decoding of Hand Postures and Individual Digit Forces for Prosthetic Hand Control.

INTRODUCTION: Options currently available to individuals with upper limb loss range from prosthetic hands that can perform many movements, but require more cognitive effort to control, to simpler terminal devices with limited functional abilities. We attempted to address this issue by designing a myoelectric control system to modulate prosthetic hand posture and digit force distribution. METHODS: We recorded surface electromyographic (EMG) signals from five forearm muscles in eight able-bodied subjects while they modulated hand posture and the flexion force distribution of individual fingers. We used a support vector machine (SVM) and a random forest regression (RFR) to map EMG signal features to hand posture and individual digit forces, respectively. After training, subjects performed grasping tasks and hand gestures while a computer program computed and displayed online feedback of all digit forces, in which digits were flexed, and the magnitude of contact forces. We also used a commercially available prosthetic hand, the i-Limb (Touch Bionics), to provide a practical demonstration of the proposed approach's ability to control hand posture and finger forces. RESULTS: Subjects could control hand pose and force distribution across the fingers during online testing. Decoding success rates ranged from 60% (index finger pointing) to 83-99% for 2-digit grasp and resting state, respectively. Subjects could also modulate finger force distribution. DISCUSSION: This work provides a proof of concept for the application of SVM and RFR for online control of hand posture and finger force distribution, respectively. Our approach has potential applications for enabling in-hand manipulation with a prosthetic hand.

A Wireless FSCV Monitoring IC With Analog Background Subtraction and UWB Telemetry.

A 30-µW wireless fast-scan cyclic voltammetry monitoring integrated circuit for ultra-wideband (UWB) transmission of dopamine release events in freely-behaving small animals is presented. On-chip integration of analog background subtraction and UWB telemetry yields a 32-fold increase in resolution versus standard Nyquist-rate conversion alone, near a four-fold decrease in the volume of uplink data versus single-bit, third-order, delta-sigma modulation, and more than a 20-fold reduction in transmit power versus narrowband transmission for low data rates. The 1.5- mm(2) chip, which was fabricated in 65-nm CMOS technology, consists of a low-noise potentiostat frontend, a two-step analog-to-digital converter (ADC), and an impulse-radio UWB transmitter (TX). The duty-cycled frontend and ADC/UWB-TX blocks draw 4 µA and 15 µA from 3-V and 1.2-V supplies, respectively. The chip achieves an input-referred current noise of 92 pA(rms) and an input current range of ±430 nA at a conversion rate of 10 kHz. The packaged device operates from a 3-V coin-cell battery, measures 4.7 × 1.9 cm(2), weighs 4.3 g (including the battery and antenna), and can be carried by small animals. The system was validated by wirelessly recording flow-injection of dopamine with concentrations in the range of 250 nM to 1 µM with a carbon-fiber microelectrode (CFM) using 300-V/s FSCV.

Predicting hand forces from scalp electroencephalography during isometric force production and object grasping.

In this study, we demonstrate the feasibility of predicting hand forces from brain activity recorded with scalp electroencephalography (EEG). Ten able-bodied subjects participated in two tasks: an isometric force production task and a grasp-and-lift task using unconstrained and constrained grasps. We found that EEG electrodes spanning central areas of the scalp were highly correlated to force rate trajectories. Moreover, EEG grand averages in central sites resembled force rate trajectories as opposed to force trajectories. The grasp-and-lift task resulted in higher decoding accuracies than the isometric force production task: across nine subjects, median accuracies for the isometric force production task were r=0.35 whereas median accuracies for unconstrained grasping were r=0.51 and for constrained grasping were r=0.50. Such results could lead to an understanding of the neural representation behind the control of hand forces and could be implemented in the neural control of closed-loop hand-based neuroprostheses.