Real-time robotic control relies on high-speed tactile arrays, but increasing the number of sensing pixels to cover large areas often leads to greater scanning delays, with readout speeds for large arrays rarely exceeding 100 Hz. To overcome this restriction, we developed compressive tactile subsampling methods that take advantage of spatial patterns in tactile data. By sampling fewer pixels in each frame and reconstructing the tactile signal using a learned tactile dictionary, these methods enable quicker readout. Using a$32\times 32$tactile sensor array, we evaluated classification accuracy and reconstruction error for tactile interactions with 30 daily and 3D printed objects using a robotic arm. Compared to traditional raster scanning, our method produced 18 times faster frame rates while maintaining minimal reconstruction and classification error. By implementing this scalable technique into software, low-cost tactile arrays may be transformed and robots can attain high-resolution, high-speed touch sensing across their bodies. More details in our preprint [1].

Recent years have seen a growing interest in the development of shared control strategies for upper limb prostheses. In this work, we take a critical step towards developing transhumeral devices by proposing a biomimetic control strategy for cooperative hand placement. This is achieved through a novel adaptation of Dynamic Movement Primitives (DMPs), enabling the generation of smooth trajectories from a rest position to arbitrary points within a user’s reach space. Our method revolves around a key observation that DMP forcing-function weights can be modeled (p2values of 0.63, 0.43, and 0.02 (horizontal, vertical, and depth). Validation on 519 trajectories via 5-fold cross-validation showed significant improvements (p < 0.01) over Extended DMP and kernel-based methods. Real-time human-in-the-loop experiments revealed a median minimum cumulative-distance deviation of 0.0733 m (8.5% error) motion with a prosthesis as compared with an intact limb. To our knowledge, this is the first study to explore shared control for transhumeral prostheses, and our observations on human motion modeling may inspire future Learning-from-Demonstration studies.

The human somatosensory system integrates multimodal sensory feedback, including tactile, proprioceptive, and thermal signals, to enable comprehensive perception and effective interaction with the environment. Inspired by the biological mechanism, we present a sensorized soft anthropomorphic hand equipped with diverse sensors designed to emulate the sensory modalities of the human hand. This system incorporates biologically inspired encoding schemes that convert multimodal sensory data into spike trains, enabling highly-efficient processing through Spiking Neural Networks (SNNs). By utilizing these neuromorphic signals, the proposed framework achieves 97.14% accuracy in object recognition across varying poses, significantly outperforming previous studies on soft hands. Additionally, we introduce a novel differentiator neuron model to enhance material classification by capturing dynamic thermal responses. Our results demonstrate the benefits of multimodal sensory fusion and highlight the potential of neuromorphic approaches for achieving efficient, robust, and human-like perception in robotic systems.

This demonstration showcases how compressive subsampling enhances the temporal resolution of large-area tactile sensor arrays, achieving high spatiotemporal fidelity in systems previously limited by slow response times of raster scanning. By applying compressive subsampling, the tactile system can track dynamic interactions, such as a tennis ball ricochet, soft object deformation, and the impact of a fired foam projectile. Participants can interact with several tactile sensor arrays in compressive subsampling and traditional raster-scan modes, providing a direct comparison. Interactive experiments include: (1) indenting soft, rapidly deforming objects, (2) bouncing a tennis ball to observe impact angles, and (3) targeting a sensor ‘dartboard’ with a NERF gun. These hands-on experiences allow visitors to appreciate the ease of implementing compressive subsampling with standard tactile arrays and reveal its potential for high-temporal-resolution applications in robotics and tactile sensing.ISCAS Track: Sensory Circuits and Systems

Whole-body, large-area tactile skin is well-cited as a requirement for emerging humanoids, prosthetics, and other robotic devices operating in unstructured environments. However, developing effective large-area tactile sensing skin has been elusive due to many complexities, particularly challenged by wiring and big data management, and remains a persisting issue. This review describes the challenges of large-area tactile skin and summarizes the recent progress in addressing the wiring and data management problems. This review summarizes tactile wiring solutions, such as time, frequency, spike/code multiplexing, tomographic approaches, wireless communication, and tactile data management solutions, such as send-on-delta, hardware compression, and compressed sensing. Several notable scalable architectures are also highlighted, and possible future directions are discussed. Unlike previous reviews focused on power management, manufacturing techniques, and neural encoding, this review concentrates specifically on the scalable communication methods developed for large tactile skins, which have not been previously reviewed. Future research can use this review as a guide to build on the current state of the art to develop advanced large-area tactile skin technologies that support the next generation of environment-aware robotic devices.

Powered upper limb prostheses offer a particularly interesting case of human-machine interaction, where the user and the robot are physically coupled as an open chain manipulator. The biological and mechanical degrees of freedom (DOF) must collaborate for the user to manipulate objects in the environment. Current state-of-the-art systems use machine learning models to classify electromyogram (EMG) signals into motion intent primitives, allowing users to move the prosthetic joints sequentially at a fixed velocity. This interface is intended to work for simple systems but does not extend well into higher DOF. Consequently, current commercially available systems are limited to 1 or 2 powered DOF. In this paper, we present a semi-autonomous (SA) hybrid gaze-EMG interface that allows users to command the device in task-space instead of joint-space. Target end-effector poses are selected by tracking the user’s gaze vector, and then EMG signals guide the prosthetic along a calculated trajectory towards that pose. To examine how prosthesis interface performance scales with available mechanical DOF, we had 4 subjects complete virtual pick and place tasks with SA and traditional controller interfaces, varying the available DOF in the prosthetic wrist. Our results show that with the SA interface, increased DOF leads to a significant (p≤0.05) reduction in compensatory motion of the upper arm, more effective (p≤0.01) utilization of the increased configuration space, and overall more efficient motion (p≤0.01) than traditional classification based interfaces. These findings indicate that when given SA interfaces, subjects can benefit from fully articulated prosthetic devices, which motivates more clinical research into SA systems and commercial development of higher-DOF devices.

EEG-based unimodal method has demonstrated significant success in the detection of driving fatigue. Nonetheless, data from a single modality might be not sufficient to optimize fatigue detection due to incomplete information. To address this limitation and enhance the performance of driving fatigue detection, a novel multimodal architecture combining hybrid electroencephalograph (EEG) and eye tracking data was proposed in this work. Specifically, the EEG and eye tracking data were separately input into encoders, generating two one-dimensional (1D) features. Subsequently, these 1D features were fed into a cross-modal predictive alignment module to improve fusion efficiency and two 1D attention modules to enhance feature representation. Furthermore, the fused features were recognized by a linear classifier. To evaluate the effectiveness of the proposed multimodal method, comprehensive validation tasks were conducted, including intra-session, cross-session, and cross-subject evaluations. In the intra-session task, the proposed architecture achieves an exceptional average accuracy of 99.93%. Moreover, in the cross-session task, our method demonstrates an average accuracy of 88.67%, surpassing the performance of EEG-only approach by 8.52%, eye tracking-only method by 5.92%, multimodal deep canonical correlation analysis (DCCA) technique by 0.42%, and multimodal deep generalized canonical correlation analysis (DGCCA) approach by 0.84%. Similarly, in the cross-subject task, the proposed approach achieves an average accuracy of 78.19%, outperforming EEG-only method by 5.87%, eye tracking-only approach by 4.21%, DCCA method by 0.55%, and DGCCA approach by 0.44%. The experimental results conclusively illustrate the superior effectiveness of the proposed method compared to both single modality approaches and canonical correlation analysis-based multimodal methods.

In this research, the main functional characteristics of nociceptors are considered for designing a novel neuromorphic circuit in TSMC 180 nm CMOS technology with a 1.8 V supply voltage and 780$\mu \text{W}$power consumption. Indeed, the current study is the first analog circuit realization of the functional model of nociceptors. The proposed circuit includes three spiking neurons. The first and second neurons determine the pressure level and the active area of the stimulus, respectively. The pressure to active area ratio is sensed by the third neuron and helps for better discrimination of the input stimuli. To evaluate the performance of the proposed nociceptor circuit, we perform numerical simulations and robotic experiments. In this way, the 3D-printed objects with different sharpness are touched by a custom-made tactile sensor and the spiking responses from the nociceptor neuromorphic circuit are then collected. Next, machine learning algorithms are applied offline to classify objects based on the rate and time of the spike responses. The results show that by analyzing the spike patterns obtained from the bio-inspired circuit, it is possible to recognize stimuli after the emission of a few spikes. The proposed approach is the proof of concept that circuit implementation of the functional model of the nociceptors expands the range of object recognition and hence facilitates the fabrication of novel tactile sensory systems for bio-robotic and prosthetic applications.

Intention decoding is an indispensable procedure in hands-free human–computer interaction (HCI). A conventional eye-tracker system using a single-model fixation duration may issue commands that ignore users' real expectations. Here, an eye-brain hybrid brain–computer interface (BCI) interaction system was introduced for intention detection through the fusion of multimodal eye-tracker and event-related potential (ERP) [a measurement derived from electroencephalography (EEG)] features. Eye-tracking and EEG data were recorded from 64 healthy participants as they performed a 40-min customized free search task of a fixed target icon among 25 icons. The corresponding fixation duration of eye tracking and ERP were extracted. Five previously-validated linear discriminant analysis (LDA)-based classifiers [including regularized LDA, stepwise LDA, Bayesian LDA, shrinkage linear discriminant analysis (SKLDA), and spatial-temporal discriminant analysis] and the widely-used convolutional neural network (CNN) method were adopted to verify the efficacy of feature fusion from both offline and pseudo-online analysis, and the optimal approach was evaluated by modulating the training set and system response duration. Our study demonstrated that the input of multimodal eye tracking and ERP features achieved a superior performance of intention detection in the single-trial classification of active search tasks. Compared with the single-model ERP feature, this new strategy also induced congruent accuracy across classifiers. Moreover, in comparison with other classification methods, we found that SKLDA exhibited a superior performance when fusing features in offline tests (ACC = 0.8783, AUC = 0.9004) and online simulations with various sample amounts and duration lengths. In summary, this study revealed a novel and effective approach for intention classification using an eye-brain hybrid BCI and further supported the real-life application of hands-free HCI in a more precise and stable manner.

Objective assessment of the brain's responsiveness in comatose patients on Extracorporeal Membrane Oxygenation (ECMO) support is essential to clinical care, but current approaches are limited by subjective methodology and inter-rater disagreement. Quantitative electroencephalogram (EEG) algorithms could potentially assist clinicians, improving diagnostic accuracy. We developed a quantitative, stimulus-based algorithm to assess EEG reactivity features in comatose patients on ECMO support. Patients underwent a stimulation protocol of increasing intensity (auditory, peripheral, and nostril stimulation). A total of 129 20-s EEG epochs were collected from 24 patients (age [Formula: see text], 10 females, 14 males) on ECMO support with a Glasgow Coma Scale[Formula: see text]8. EEG reactivity scores ([Formula: see text]-scores) were calculated using aggregated spectral power and permutation entropy for each of five frequency bands ([Formula: see text], [Formula: see text], [Formula: see text], [Formula: see text], [Formula: see text]. Parameter estimation techniques were applied to [Formula: see text]-scores to identify properties that replicate the decision process of experienced clinicians performing visual analysis. Spectral power changes from audio stimulation were concentrated in the [Formula: see text] band, whereas peripheral stimulation elicited an increase in spectral power across multiple bands, and nostril stimulation changed the entropy of the [Formula: see text] band. The findings of this pilot study on [Formula: see text]-score lay a foundation for a future prediction tool with clinical applications.

Trust in autonomous vehicles (AV) is of critical importance and demands comprehensive interdisciplinary research. While most studies utilize subjective measures, we employ electroencephalography (EEG) to study in a more objective manner the cognitive states associated with trust during AV driving. Subjects drove a simulated AV in Conditional Automation Driving (SAE L3) and Full Automation Driving (SAE L5) modes. In the experimental design, malfunctions were induced at both automation levels. Self-reported trust in the AV was reduced immediately after Full Automation malfunctions, but not after Conditional Automation malfunctions when subjects were able to take over vehicle control to avoid danger. EEG analyses reveal that during Full Automation malfunctions, there was a significant enhancement in approach motivation (i.e. desire to re-engage) and a disruption of right frontal functional clustering that supports executive cognition (i.e. planning and decision-making). No neurocognitive disruptions were observed during Conditional Automation malfunctions. Our results demonstrate that it is not automation malfunctions per se (e.g. failure to decelerate) that deteriorate trust, but rather the inability for human drivers to adaptively mitigate the risk of negative outcomes (e.g. risk of crashing) resulting from those malfunctions. This is reflected in changes in brain activity associated with motivational state and action planning. Keeping the human driver on-the-loop protects against trust loss. Frontal alpha EEG is a neural correlate of trust-in-automation, with potential for trust monitoring using wearable technology to support driver-vehicle adaptivity.

Prosthetic hands, today, have anthropomorphic, multifinger design. A common control method uses pattern recognition of electromyogram signals. However, these prostheses do not capture the human hand's sensory perception, which is critical for prosthesis embodiment and dexterous object manipulation. This problem can be solved by sensorized electronic skin (e-skin) composed of various sensors that transduce sensory percepts such as touch, pressure, temperature, and pain, just as human skin does. This review will present the physiology of the receptors that encode tactile, thermal, nociceptive, and proprioceptive information. The e-skin is designed to mimic these receptors and their responses. We review each sensor subtype, and its design and performance when embedded in the e-skin. Next, we review the spiking response of the receptors, which are then relayed to sensory nerves and encoded by the brain as sensory percepts. The e-skin system is designed to produce neuromorphic or receptorlike spiking activity. Computational models to mimic these sensory nerve signals are presented and then various methods to interface with the nervous system are explored and compared. We conclude the review with the state of the art in e-skin design and deployment in closed-loop applications that demonstrate the benefits of sensory feedback for amputees.

Brain computer interface (BCI) technique has been demonstrated that human intentions or stimulus perception can be recognized using EEG signal recorded from the human scalp. When an intention is initiated in the brain or an external stimulus is perceived, the underlying relevant processing alters brain activity. This alteration in brain activity can be reflected in EEG signal. The intention or stimulus perception is therefore classified based on the alteration in brain activity. It might be difficult to classify brain activities in the perception of shape-analogous English letters because the similar shape could lead to less difference in brain activity. In order to explore classification feasibility and classification performance of shape-analogous letters using EEG signal, we performed an experiment of shape-analogous letter perception, in where participants perceived four letters (i.e., 'p', 'q', 'b' and 'd') while EEG signal was recorded. The F-score method was employed to assess the discriminative power for each feature, and a subgroup of features with high discriminative powers was then selected and fed into classifiers. Five classifiers (i.e., k-Nearest Neighbors (kNN), Support Vector Machine (SVM), Linear Discriminant Analysis (LDA), Random Forest (RF) and AdaBoost (ADA)), which are either pervasive or advanced in the field of machine learning, were utilized to classify brain activities in perception of shape-analogous letters. For each classifier, its parameters and the number of used features were optimized. Based on the performance comparison among the classifiers, Random Forest (RF) classifier achieved a maximal accuracy of 74.1%, but it was not statistically significantly better than the SVM. Our study demonstrated that brain activities in perception of shape-analogous English letters can be classified based on EEG signal and showed that random forest classifier outperformed other classifiers according to the results of comparison.

THIS paper presents the development of a bidirectional fabric-based soft pneumatic actuator requiring low fluid pressurization for actuation, which is incorporated into a soft robotic gripper to demonstrate its utility. The bidirectional soft fabric-based actuator is able to provide both flexion and extension. Fabrication of the fabric actuators is simple as compared to the steps involved in traditional silicone-based approach. In addition, the fabric actuators are able to generate comparably larger vertical grip resistive force at lower operating pressure than elastomeric actuators and 3D-printed actuators, being able to generate resistive grip force up to 20N at 120 kPa. Five of the bidirectional soft fabric-based actuators are deployed within a five-fingered soft robotic gripper, complete with five casings and a base. It is capable of grasping a variety of objects with maximum width or diameter closer to its bending curvature. A cutting task involved bimanual manipulation was demonstrated successfully with the gripper. To incorporate intelligent control for such a task, a soft force made completely of compliant material was attached to the gripper, which allows determination of whether the cutting task is completed. To the authors' knowledge, this work is the first study which incorporates two soft robotic grippers for bimanual manipulation with one of the grippers sensorized to provide closed loop control.

This is a live demonstration of the work described in [l]. The paper ID of this submission is 1292. The goal of this work is to maintain specific pressures on the model residual limb (MRL) to counteract the pressure changes caused by loading/limb movement. Custom textile force sensors are embedded in between the air bladders and the socket. These force sensors communicate with the fluidic control board, which based on a proportional algorithm maintains airflow to the bladders, in response to the changing loads on the socket.

During grasping or natural movement a prosthesis experiences varying loads, which can directly impact the comfort and fit of a socket on the user's residual limb. To alleviate this issue, four independent air bladders were integrated in a custom prosthetic socket, which contacted a model residual limb. The purpose of the bladders was to maintain specific pressures on the model residual limb to counteract pressure changes caused by an applied load. To sense pressure, calibrated custom piezoresistive sensors were placed between the air bladder and the model residual limb. A closed-loop algorithm was implemented to utilize sensor feedback and update airflow into each bladder to achieve a pressure equilibrium that maintains a static socket-limb system. The RMS error of internal pressure using pulse width modulation (PWM) bladder regulation decreased by 33% when compared no pressure regulation on the top bladder. We showed that this adaptive biomedical system for the human machine interface between a residual limb and socket can improve stability for prosthesis users using real-time pressure feedback.

This work introduces the smallest wirelessly powered neural implant to date. We provide experiment verification by successfully stimulating the sciatic nerve of a rat. Power is deliverd over a 1.7 GHz inductive link at a distance of 0.5 cm. A method is also proposed to generate biphasic current pulses without the use of a controller. The entire system is fabricated in a 0.13 μm CMOS process and occupies merely 180 μm × 180 μm.

This is a live demonstration of the work described in [1]. The paper ID of this submission is 1634. The goal of this work is to use a neuromorphic model for providing tactile feedback to a prosthetic hand to improve grasping functionality. Custom force sensors are placed on the fingertips of a bebionc3 (Steeper, Leeds, UK) prosthetic hand and communicate with the prosthesis controller (Infinite Biomedical Technologies, Baltimore, USA). The prosthesis grip force is used as the input to a leaky integrate and fire (LIF) with spike rate adaption neuron model to produce a tactile signal represented by spiking information, which is similar to the behavior of mechanoreceptors found in humans. The prosthesis controller uses the spiking information to modulate the grip force and allow the hand to grasp a delicate object.

To develop subject-specific classifier to recognize mental states fast and reliably is an important issue in brain-computer interfaces (BCI), particularly in practical real-time applications such as wheelchair or neuroprosthetic control. In this paper, a sequential decision-making strategy is explored in conjunction with an optimal wavelet analysis for EEG classification. The subject-specific wavelet parameters based on a grid-search method were first developed to determine evidence accumulative curve for the sequential classifier. Then we proposed a new method to set the two constrained thresholds in the sequential probability ratio test (SPRT) based on the cumulative curve and a desired expected stopping time. As a result, it balanced the decision time of each class, and we term it balanced threshold SPRT (BTSPRT). The properties of the method were illustrated on 14 subjects' recordings from offline and online tests. Results showed the average maximum accuracy of the proposed method to be 83.4% and the average decision time of 2.77[Formula: see text]s, when compared with 79.2% accuracy and a decision time of 3.01[Formula: see text]s for the sequential Bayesian (SB) method. The BTSPRT method not only improves the classification accuracy and decision speed comparing with the other nonsequential or SB methods, but also provides an explicit relationship between stopping time, thresholds and error, which is important for balancing the speed-accuracy tradeoff. These results suggest that BTSPRT would be useful in explicitly adjusting the tradeoff between rapid decision-making and error-free device control.

Spiking neural networks are well suited to perform time-dependent pattern recognition problems by encoding the temporal dimension in precise spike times. With an appropriate set of weights, a spiking neuron can emit precisely timed action potentials in response to spatiotemporal input spikes. However, deriving supervised learning rules for spike mapping is nontrivial due to the increased complexity. Existing methods rely on heuristic approaches that do not guarantee a convex objective function and, therefore, may not converge to a global minimum. In this paper, we present a novel technique to obtain the weights of spiking neurons by formulating the problem in a convex optimization framework, rendering it be compatible with the established methods. We introduce techniques to influence the weight distribution and membrane trajectory, and then study how these factors affect robustness in the presence of noise. In addition, we show how the existence of a solution can be determined and assess memory capacity limits of a neuron model using synthetic examples. The practical utility of our technique is further assessed by its application to gait-event detection using the experimental data.

Limb tracking is an important aspect of human-machine interfaces (HMI). These systems, however, can often be limited by complex algorithms requiring significant processing power, obtrusive and immobile sensing techniques, and high costs. In this work, we utilize a sensor fusion algorithm implemented in commercial inertial measurement units (IMU) to combine accelerometer and gyroscope measurements in an effort to minimize computational requirements of the limb tracking system. In addition, previously developed methods were implemented to eliminate sensor drift by including information from a magnetometer. We tested the accuracy of our system by computing the root mean squared error (RMSE) of the true angle between the headings of two sensors and the estimate of that angle through quaternion-vector manipulations. An average RMSE of approximately 2.9° was achieved. Our limb tracking system is wearable, minimally complex, low-cost, and simple to use which has proven useful in multiple HMI applications discussed herein.

This paper introduces a spiking hierarchical model for object recognition which utilizes the precise timing information inherently present in the output of biologically inspired asynchronous address event representation (AER) vision sensors. The asynchronous nature of these systems frees computation and communication from the rigid predetermined timing enforced by system clocks in conventional systems. Freedom from rigid timing constraints opens the possibility of using true timing to our advantage in computation. We show not only how timing can be used in object recognition, but also how it can in fact simplify computation. Specifically, we rely on a simple temporal-winner-take-all rather than more computationally intensive synchronous operations typically used in biologically inspired neural networks for object recognition. This approach to visual computation represents a major paradigm shift from conventional clocked systems and can find application in other sensory modalities and computational tasks. We showcase effectiveness of the approach by achieving the highest reported accuracy to date (97.5% ± 3.5%) for a previously published four class card pip recognition task and an accuracy of 84.9% ± 1.9% for a new more difficult 36 class character recognition task.

This study proposes a method to control a prosthetic hand by EMG signals based on muscle synergies. The muscle synergy model suggests a framework to transform commands of the central nervous system to a set of complex muscular movements. Using this method, we have tried to realize the proportional control of multiple degrees of freedom (DOF). This study focuses on controlling four kinds of hand/wrist movements of the prosthesis: open, close, pronate, and supinate. The nonnegative matrix factorization (NMF) algorithm is used to map muscle activities into these four movements through the calculation of a muscle synergy matrix. An EMG feature selection process along with a control scheme has been added, which smooths the output thereby stabilizing the movements. Ten healthy subjects performed an online experiment comprised of two tests: 1) proportional control on single DOF, and 2) simultaneous control of multiple DOFs. The results indicate that fluid hand/wrist movements could be estimated from EMG. The average R2values achieved by all subjects for the single-DOF test and the multiple-DOF test are 0.97 and 0.93, respectively.

Wireless power delivery and telemetry have enabled completely implantable neural devices. Current day implants are controlled, monitored, and powered wirelessly, eliminating the need for batteries and prolonging the lifetime. A brief overview of wireless platforms for such implantable devices is presented in this paper alongside an in-depth discussion of wireless platform for peripheral nerve implants covering design requirements, link design, and safety. Initial acute studies on the performance of the wireless power and data links in rodents are also presented.

This paper describes a novel approach to the problem of autonomous Robot Navigation in environments having less or no source of illumination. We have aimed at depth estimation and object recognition aspects, using the bio-inspired Dynamic Vision Sensor (DVS) asynchronous time-based image sensor (ATIS) silicon retina. Experiments were conducted in a dark environment using the ATIS camera, coupled with a simple point-like white LED light source mounted on the same. Switching the LED on for a fraction of time in the dark environment produced a diverging ripple of events in the ATIS. We show how this event response can be used to quantify the distance of the planar obstacle from the camera and also to characterize the object for use in object recognition. The ripple effect observed can be attributed to the high temporal resolution of the ATIS retina, the small rise time of the LED and the light intensity profile on the wall. In the initial sections of the paper, we have shown the theoretical basis for the phenomenon observed and then moved on to describe the proof of concept for depth estimation and object recognition. The algorithms can be used in robotic systems mounted with the ATIS and LED for real time depth perception and object recognition.

We are developing an intelligent compact and modular knee-ankle-foot robot gait rehabilitation at outpatient and home settings. The robot is designed with a novel compact compliant force controllable actuator. We adopt a modular design for the knee and ankle joint so that the robot can assist patients with different conditions of gait impairments. The light-weight anthropomorphic structure designed based on biomechanical studies is built with advanced composite materials to achieve portability. A prototype of the robot has been built for testing. In this paper, we present the mechanical design of the robot with focus on the actuator and mechanism design and analysis, with testing results to demonstrate the performance of the actuator.

EEG and field potential rhythms established in the cortex and thalamus may accommodate the propagation of seizures. This article describes the interaction between thalamus and cortex during pentylenetetrazol (PTZ) seizures in rats with and without prior treatment with ethosuximide (ESM), a well-known antiepileptic drug (AED) that raises the threshold for seizures, was given before PTZ. The AED was given before PTZ convulsant administration. We track this thalamo-cortical association with a novel measure we have called the cross-bicoherence gain, or BISCOH. This quantity allows us to measure the spectral coherence in a purely higher order spectralmethodology. BISCOH is able to track the formation of nonlinearities at specific frequencies in the recorded EEG. BISCOH showed a strong increase in low alpha wave harmonic generationat 10 and 12.5 Hz after ESM treatment (p < 0.02 and p < 0.007, respectively). Conventional coherence failed to show distinctive and significant changes in thalamo-cortical coupling after ESM treatment at those frequencies and instead showed changes at 5 Hz. This rise in cortical rhythms is evidence of harmonic generation or new frequency formation in the thalamo-cortical system withAED therapy. BISCOH could become a powerful tool in unraveling changes in coherence due to neuroelectric modulation resulting from drug treatment or electrical stimulation.

Long term monitoring of neuronal activity in awake behaving subjects can provide fundamental information about brain dynamics for both neuroscience and neuroengineering applications. Recent advances in VLSI systems has focused on designing wireless neural recording systems which can be mounted on animals and acquire neural signals in real time. These advances provide an unparalleled opportunity to study phenomenon such as neural plasticity in both a basic science setting (learning and memory), and also a clinical setting (injury and recovery). Here we present an integrated VLSI system for wireless telemetry of the entire spectrum of neural signals, spikes, local field potentials, electrocorticograms (ECoG) and electroencephalograms (EEG). The system integrates two custom designed VLSI chips, a 16 channel neural interface which can amplify, filter and digitize neural data up to 16 kS/sec and 12 bits and a low power ultra-wideband (UWB) chip which can transmit data at rates up to 14 Mbps. The entire system which includes these VLSI circuits, a digital interface board and a battery, is small, 1.2 × 1.2 × 2.6 in3, and light weight, 33 grams, so it can be chronically mounted on a rat. The system consumes 32.8 mA at 3.3V and can record for 6 hours running from the 200 mAh coin cell battery. Bench-top and in vitro characterization of the system showed comparable performance to the wired recording system.

Neuroengineering is one of the fastest growing disciplines in the Biomedical Engineering community, especially in societies such as IEEE Engineering in Medicine in Biology and Medicine. I will begin my presentation with the overview of the field of Neuroengineering, spanning the cellular to brain, and from basic bench research to clinical applications. Progress in the field is covered by the journals such as the IEEE Transactions on Neural Systems and Rehabilitation Engineering that I am Editor in Chief of, covers this field. Within the field of Neuroengineering, and the journal, the ldquohot topicrdquo is brain machine interface, particularly the development of neural prostheses. I will present the basic ideas behind building the brain machine interface and expand to our recent work on the development of a dexterous arm and neural control of this dexterous arm prosthesis. The talk will present the technology, signal processing methods, the neuroscience foundations, and our work on controlling dexterous finger motions from neural signals. I will conclude the talk with some thoughts on the technological challenges faced in building the interfaces to brain and potential applications of tapping into the powers of the mind.

We show that an ongoing locomotor pattern can be modulated by application of discrete electrical stimuli to the spinal cord at specific phases of the locomotor cycle. Data is presented from a series of experiments on in vitro lamprey spinal cords, which were used as an animal model for severe spinal cord injury. For any given stimulus, the effects on frequency, length, and symmetry of locomotor output show a strong dependence on the phase at which stimulation is applied. The most significant changes are seen when stimulation occurs during motor bursting: stimuli applied to the ipsilateral spinal hemicord increase the burst length, while stimuli applied to the contralateral spinal hemicord decrease the burst length. Simulations using experimentally-measured phase-dependent responses indicate that by monitoring the state of the neural system, it should be possible to apply stimuli at the appropriate times to modulate the lamprey "gait" on a cycle-by-cycle basis. Eventually, this approach could lead to development of a neuroprosthetic device for restoring locomotion after paralysis.

This paper presents a four-wavelength near-infrared imaging system that can assist surgeons with intraoperative monitoring and imaging of blood flow of the vessel being occluded. The algorithm for this system, based on the Beer-Lambert law, calculates the relative concentrations of deoxyhemoglobin, oxyhemoglobin and water, which are the major NIR absorbers in tissues. Regional blood volume and oxygen saturation can be determined from these measurements. This proof-of-concept study investigated the utility of the algorithm on detecting rat infrarenal abdominal aortic blood flow subjected to various degrees of occlusion. The images provided a good visualization of the aorta because of the high concentration of oxyhemoglobin in the blood stream. The imager was able to detect when blood flow was completely stopped. Average intensity values of the blood volume images correlated well with the laser Doppler recordings.

Accurate detection and characterization of changes in the EEG signal is crucial for clinical assessment of the neurological system condition. Several distance measures are tested and evaluated for their effectiveness of detecting injury-related changes in EEG. Itakura distance is found to be a very efficient means to characterize changes in EEG for both signaling injury and predicting recovery. The efficiency of the Itakura distance measure is further established through a comparison study of spectral distance measure and Kullback-Leibler information.

Time series analysis applications of eigenstructure algorithms focus on temporal frequency estimation. The authors show that the ESPRIT algorithm can also be applied to simple phase delays for sinusoids. They show that a time delay data model can be rendered in the ESPRIT matrix pencil structure. The PRO-ESPRIT formulation can be then utilized to solve for phase delays among sinusoids. An application area for this algorithm is the estimation of short time delays for low frequency sinusoids comprising EEG (electroencephalographic) recordings derived from different neural sites during epileptic seizure activity.

The automatic internal defibrillator delivers a low-energy shock directly to the heart. Optimal strategies for these shock deliveries are determined by studying a three-dimensional computer model of the electric fields produced by initial defibillation electrodes. A finite-element analysis technique is used to calculate energy and current density distributions in three commonly used electrode configurations: (1) patch-patch (PP), (2) catheter-patch (CP), and (3) catheter-catheter (CC). analysis of these simulations indicates that : (1) the PP and CP configurations are more effective at channeling energy to the myocardium than the CC configuration; (2) small electrodes and the edges of the electrodes give rise to high local current densities which might cause damage to the myocardium: (3) energy delivered to the myocardium is not significantly altered for different electrode placements tested; (4) electrode size influences current density distribution, especially near the electrodes; and (5) energy distribution is sensitive to the relative conductances of the myocardial tissue and blood.>

Current trends in robotics are pointing to the design of dextrous manipulators patterned after human hands. This article reports on some studies of the human hand and how it relates to the design of anthropomorphic manipulators. We discuss hand anatomy and a mathematical model that relates tendon displacement to joint angle. This enables the understanding of the role of the tendons in normal hand function as well as in disability. This information may be used to design robot or hand prosthesis tendons after their human counterparts. We present an example of a shape memory alloy actuator based design. The actuators emulate human tendon mechanisms.

This paper presents a dynamic programming (DP) algorithm to calculate robot manipulator kinematics and plan optimized trajectories. The algorithm optimizes desireable cost functions such as absolute accuracy or time of travel. We show that, when necessary, we can accept sub-optimal (higher cost) alternative to our advantage. For example, if an optimal trajectory is blocked by an obstacle, the robot can reach the destination by following a sub-optimal path. We present computer simulations of many diverse applications of DP to robot kinematic problems. DP algorithm requires more computations than conventional techniques, but permits optimization of objective criteria and flexibility in path planning.