Showing papers in "IEEE Transactions on Biomedical Engineering in 2005"

Impedance characterization and modeling of electrodes for biomedical applications

Abstract: A low electrode-electrolyte impedance interface is critical in the design of electrodes for biomedical applications. To design low-impedance interfaces a complete understanding of the physical processes contributing to the impedance is required. In this work a model describing these physical processes is validated and extended to quantify the effect of organic coatings and incubation time. Electrochemical impedance spectroscopy has been used to electrically characterize the interface for various electrode materials: platinum, platinum black, and titanium nitride; and varying electrode sizes: 1 cm/sup 2/, and 900 /spl mu/m/sup 2/. An equivalent circuit model comprising an interface capacitance, shunted by a charge transfer resistance, in series with the solution resistance has been fitted to the experimental results. Theoretical equations have been used to calculate the interface capacitance impedance and the solution resistance, yielding results that correspond well with the fitted parameter values, thereby confirming the validity of the equations. The effect of incubation time, and two organic cell-adhesion promoting coatings, poly-L-lysine and laminin, on the interface impedance has been quantified using the model. This demonstrates the benefits of using this model in developing a better understanding of the physical processes occurring at the interface in more complex, biomedically relevant situations.

Assessment of walking features from foot inertial sensing

Abstract: An ambulatory monitoring system is developed for the estimation of spatio-temporal gait parameters. The inertial measurement unit embedded in the system is composed of one biaxial accelerometer and one rate gyroscope, and it reconstructs the sagittal trajectory of a sensed point on the instep of the foot. A gait phase segmentation procedure is devised to determine temporal gait parameters, including stride time and relative stance; the procedure allows to define the time intervals needed for carrying an efficient implementation of the strapdown integration, which allows to estimate stride length, walking speed, and incline. The measurement accuracy of walking speed and inclines assessments is evaluated by experiments carried on adult healthy subjects walking on a motorized treadmill. Root-mean-square errors less than 0.18 km/h (speed) and 1.52% (incline) are obtained for tested speeds and inclines varying in the intervals [3, 6] km/h and [-5, +15]%, respectively. Based on the results of these experiments, it is concluded that foot inertial sensing is a promising tool for the reliable identification of subsequent gait cycles and the accurate assessment of walking speed and incline.

A Gaussian mixture model based classification scheme for myoelectric control of powered upper limb prostheses

Abstract: This paper introduces and evaluates the use of Gaussian mixture models (GMMs) for multiple limb motion classification using continuous myoelectric signals. The focus of this work is to optimize the configuration of this classification scheme. To that end, a complete experimental evaluation of this system is conducted on a 12 subject database. The experiments examine the GMMs algorithmic issues including the model order selection and variance limiting, the segmentation of the data, and various feature sets including time-domain features and autoregressive features. The benefits of postprocessing the results using a majority vote rule are demonstrated. The performance of the GMM is compared to three commonly used classifiers: a linear discriminant analysis, a linear perceptron network, and a multilayer perceptron neural network. The GMM-based limb motion classification system demonstrates exceptional classification accuracy and results in a robust method of motion classification with low computational load.

Spatio-spectral filters for improving the classification of single trial EEG

Abstract: Data recorded in electroencephalogram (EEG)-based brain-computer interface experiments is generally very noisy, nonstationary, and contaminated with artifacts that can deteriorate discrimination/classification methods. In this paper, we extend the common spatial pattern (CSP) algorithm with the aim to alleviate these adverse effects. In particular, we suggest an extension of CSP to the state space, which utilizes the method of time delay embedding. As we will show, this allows for individually tuned frequency filters at each electrode position and, thus, yields an improved and more robust machine learning procedure. The advantages of the proposed method over the original CSP method are verified in terms of an improved information transfer rate (bits per trial) on a set of EEG-recordings from experiments of imagined limb movements.

Support vector machines for automated gait classification

Abstract: Ageing influences gait patterns causing constant threats to control of locomotor balance. Automated recognition of gait changes has many advantages including, early identification of at-risk gait and monitoring the progress of treatment outcomes. In this paper, we apply an artificial intelligence technique [support vector machines (SVM)] for the automatic recognition of young-old gait types from their respective gait-patterns. Minimum foot clearance (MFC) data of 30 young and 28 elderly participants were analyzed using a PEAK-2D motion analysis system during a 20-min continuous walk on a treadmill at self-selected walking speed. Gait features extracted from individual MFC histogram-plot and Poincare/spl acute/-plot images were used to train the SVM. Cross-validation test results indicate that the generalization performance of the SVM was on average 83.3% (/spl plusmn/2.9) to recognize young and elderly gait patterns, compared to a neural network's accuracy of 75.0/spl plusmn/5.0. A "hill-climbing" feature selection algorithm demonstrated that a small subset (3-5) of gait features extracted from MFC plots could differentiate the gait patterns with 90% accuracy. Performance of the gait classifier was evaluated using areas under the receiver operating characteristic plots. Improved performance of the classifier was evident when trained with reduced number of selected good features and with radial basis function kernel. These results suggest that SVMs can function as an efficient gait classifier for recognition of young and elderly gait patterns, and has the potential for wider applications in gait identification for falls-risk minimization in the elderly.

Spike detection using the continuous wavelet transform

Abstract: This paper combines wavelet transforms with basic detection theory to develop a new unsupervised method for robustly detecting and localizing spikes in noisy neural recordings. The method does not require the construction of templates, or the supervised setting of thresholds. We present extensive Monte Carlo simulations, based on actual extracellular recordings, to show that this technique surpasses other commonly used methods in a wide variety of recording conditions. We further demonstrate that falsely detected spikes corresponding to our method resemble actual spikes more than the false positives of other techniques such as amplitude thresholding. Moreover, the simplicity of the method allows for nearly real-time execution.

Hollow metal microneedles for insulin delivery to diabetic rats

Abstract: The goal of this study was to design, fabricate, and test arrays of hollow microneedles for minimally invasive and continuous delivery of insulin in vivo. As a simple, robust fabrication method suitable for inexpensive mass production, we developed a modified-LIGA process to micromachine molds out of polyethylene terephthalate using an ultraviolet laser, coated those molds with nickel by electrodeposition onto a sputter-deposited seed layer, and released the resulting metal microneedle arrays by selectively etching the polymer mold. Mechanical testing showed that these microneedles were sufficiently strong to pierce living skin without breaking. Arrays containing 16 microneedles measuring 500 /spl mu/m in length with a 75 /spl mu/m tip diameter were then inserted into the skin of anesthetized, diabetic, hairless rats. Insulin delivery through microneedles caused blood glucose levels to drop steadily to 47% of pretreatment values over a 4-h insulin delivery period and were then approximately constant over a 4-h postdelivery monitoring period. Direct measurement of plasma insulin levels showed a peak value of 0.43 ng/ml. Together, these data suggest that microneedles can be fabricated and used for in vivo insulin delivery.

Continuous myoelectric control for powered prostheses using hidden Markov models

Abstract: This paper represents an ongoing investigation of dexterous and natural control of upper extremity prostheses using the myoelectric signal. The scheme described within uses a hidden Markov model (HMM) to process four channels of myoelectric signal, with the task of discriminating six classes of limb movement. The HMM-based approach is shown to be capable of higher classification accuracy than previous methods based upon multilayer perceptrons. The method does not require segmentation of the myoelectric signal data, allowing a continuous stream of class decisions to be delivered to a prosthetic device. Due to the fact that the classifier learns the muscle activation patterns for each desired class for each individual, a natural control actuation results. The continuous decision stream allows complex sequences of manipulation involving multiple joints to be performed without interruption. The computational complexity of the HMM in its operational mode is low, making it suitable for a real-time implementation. The low computational overhead associated with training the HMM also enables the possibility of adaptive classifier training while in use.

Elastic registration of biological images using vector-spline regularization

Abstract: We present an elastic registration algorithm for the alignment of biological images. Our method combines and extends some of the best techniques available in the context of medical imaging. We express the deformation field as a B-spline model, which allows us to deal with a rich variety of deformations. We solve the registration problem by minimizing a pixelwise mean-square distance measure between the target image and the warped source. The problem is further constrained by way of a vector-spline regularization which provides some control over two independent quantities that are intrinsic to the deformation: its divergence, and its curl. Our algorithm is also able to handle soft landmark constraints, which is particularly useful when parts of the images contain very little information or when its repartition is uneven. We provide an optimal analytical solution in the case when only landmarks and smoothness considerations are taken into account. We have applied our approach to perform the elastic registration of images such as electrophoretic gels and fly embryos. The validation of the results by experts has been favorable in all cases.

Design of a novel MRI compatible manipulator for image guided prostate interventions

Abstract: This paper reports a novel remotely actuated manipulator for access to prostate tissue under magnetic resonance imaging guidance (APT-MRI) device, designed for use in a standard high-field MRI scanner. The device provides three-dimensional MRI guided needle placement with millimeter accuracy under physician control. Procedures enabled by this device include MRI guided needle biopsy, fiducial marker placements, and therapy delivery. Its compact size allows for use in both standard cylindrical and open configuration MRI scanners. Preliminary in vivo canine experiments and first clinical trials are reported.

A new approach to accurate measurement of uniaxial joint angles based on a combination of accelerometers and gyroscopes

Abstract: A new method of measuring joint angle using a combination of accelerometers and gyroscopes is presented. The method proposes a minimal sensor configuration with one sensor module mounted on each segment. The model is based on estimating the acceleration of the joint center of rotation by placing a pair of virtual sensors on the adjacent segments at the center of rotation. In the proposed technique, joint angles are found without the need for integration, so absolute angles can be obtained which are free from any source of drift. The model considers anatomical aspects and is personalized for each subject prior to each measurement. The method was validated by measuring knee flexion-extension angles of eight subjects, walking at three different speeds, and comparing the results with a reference motion measurement system. The results are very close to those of the reference system presenting very small errors (rms=1.3, mean=0.2, SD=1.1 deg) and excellent correlation coefficients (0.997). The algorithm is able to provide joint angles in real-time, and ready for use in gait analysis. Technically, the system is portable, easily mountable, and can be used for long term monitoring without hindrance to natural activities.

Sequential finite element model of tissue electropermeabilization

Abstract: Permeabilization, when observed on a tissue level, is a dynamic process resulting from changes in membrane permeability when exposing biological cells to external electric field (E). In this paper we present a sequential finite element model of E distribution in tissue which considers local changes in tissue conductivity due to permeabilization. These changes affect the pattern of the field distribution during the high voltage pulse application. The presented model consists of a sequence of static models (steps), which describe E distribution at discrete time intervals during tissue permeabilization and in this way present the dynamics of electropermeabilization. The tissue conductivity for each static model in a sequence is determined based on E distribution from the previous step by considering a sigmoid dependency between specific conductivity and E intensity. Such a dependency was determined by parameter estimation on a set of current measurements, obtained by in vivo experiments. Another set of measurements was used for model validation. All experiments were performed on rabbit liver tissue with inserted needle electrodes. Model validation was carried out in four different ways: 1) by comparing reversibly permeabilized tissue computed by the model and the reversibly permeabilized area of tissue as obtained in the experiments; 2) by comparing the area of irreversibly permeabilized tissue computed by the model and the area where tissue necrosis was observed in experiments; 3) through the comparison of total current at the end of pulse and computed current in the last step of sequential electropermeabilization model; 4) by comparing total current during the first pulse and current computed in consecutive steps of a modeling sequence. The presented permeabilization model presents the first approach of describing the course of permeabilization on tissue level. Despite some approximations (ohmic tissue behavior) the model can predict the permeabilized volume of tissue, when exposed to electrical treatment. Therefore, the most important contribution and novelty of the model is its potentiality to be used as a tool for determining parameters for effective tissue permeabilization.

Quantifying errors in spectral estimates of HRV due to beat replacement and resampling

Abstract: Spectral estimates of heart rate variability (HRV) often involve the use of techniques such as the fast Fourier transform (FFT), which require an evenly sampled time series. HRV is calculated from the variations in the beat-to-beat (RR) interval timing of the cardiac cycle which are inherently irregularly spaced in time. In order to produce an evenly sampled time series prior to FFT-based spectral estimation, linear or cubic spline resampling is usually employed. In this paper, by using a realistic artificial RR interval generator, interpolation and resampling is shown to result in consistent over-estimations of the power spectral density (PSD) compared with the theoretical solution. The Lomb-Scargle (LS) periodogram, a more appropriate spectral estimation technique for unevenly sampled time series that uses only the original data, is shown to provide a superior PSD estimate. Ectopy removal or replacement is shown to be essential regardless of the spectral estimation technique. Resampling and phantom beat replacement is shown to decrease the accuracy of PSD estimation, even at low levels of ectopy or artefact. A linear relationship between the frequency of ectopy/artefact and the error (mean and variance) of the PSD estimate is demonstrated. Comparisons of PSD estimation techniques performed on real RR interval data during minimally active segments (sleep) demonstrate that the LS periodogram provides a less noisy spectral estimate of HRV.

Insulin kinetics in type-1 diabetes: continuous and bolus delivery of rapid acting insulin

Abstract: We investigated insulin lispro kinetics with bolus and continuous subcutaneous insulin infusion (CSII) modes of insulin delivery. Seven subjects with type-1 diabetes treated by CSII with insulin lispro have been studied during prandial and postprandial conditions over 12 hours. Eleven alternative models of insulin kinetics have been proposed implementing a number of putative characteristics. We assessed 1) the effect of insulin delivery mode, i.e., bolus or basal, on the insulin absorption rate, the effects of 2) insulin association state and 3) insulin dose on the rate of insulin absorption, 4) the remote insulin effect on its volume of distribution, 5) the effect of insulin dose on insulin disappearance, 6) the presence of insulin degradation at the injection site, and finally 7) the existence of two pathways, fast and slow, of insulin absorption. An iterative two-stage parameter estimation technique was used. Models were validated through assessing physiological feasibility of parameter estimates, posterior identifiability, and distribution of residuals. Based on the principle of parsimony, best model to fit our data combined the slow and fast absorption channels and included local insulin degradation. The model estimated that 67(53-82)% [mean (interquartile range)] of delivered insulin passed through the slow absorption channel [absorption rate 0.011(0.004-0.029) min/sup -1/] with the remaining 33% passed through the fast channel [absorption rate 0.021(0.011-0.040) min/sup -1/]. Local degradation rate was described as a saturable process with Michaelis-Menten characteristics [V/sub MAX/=1.93(0.62-6.03) mU min/sup -1/, K/sub M/=62.6(62.6-62.6) mU]. Models representing the dependence of insulin absorption rate on insulin disappearance and the remote insulin effect on its volume of distribution could not be validated suggesting that these effects are not present or cannot be detected during physiological conditions.

Needle steering and motion planning in soft tissues

Abstract: In this work, needle insertion into deformable tissue is formulated as a trajectory planning and control problem. A new concept of needle steering has been developed and a needle manipulation Jacobian defined using numerical needle insertion models that include needle deflection and soft tissue deformation. This concept is used in conjunction with a potential-field-based path planning technique to demonstrate needle tip placement and obstacle avoidance. Results from open loop insertion experiments are provided.

Interactive simulation of needle insertion models

Abstract: A novel interactive virtual needle insertion simulation is presented. The simulation models are based on measured planar tissue deformations and needle insertion forces. Since the force-displacement relationship is only of interest along the needle shaft, a condensation technique is shown to reduce the computational complexity of linear simulation models significantly. As the needle penetrates or is withdrawn from the tissue model, the boundary conditions that determine the tissue and needle motion change. Boundary condition and local material coordinate changes are facilitated by fast low-rank matrix updates. A large-strain elastic needle model is coupled to the tissue models to account for needle deflection and bending during simulated insertion. A haptic environment, based on these novel interactive simulation techniques, allows users to manipulate a three-degree-of-freedom virtual needle as it penetrates virtual tissue models, while experiencing steering torques and lateral needle forces through a planar haptic interface.

Noninvasive measurement of heartbeat, respiration, snoring and body movements of a subject in bed via a pneumatic method

Abstract: We have developed a noninvasive pneumatics-based system by which to measure heartbeat, respiration, snoring, and body movements of a subject in bed. A thin, air-sealed cushion is placed under the bed mattress of the subject and the small movements attributable to human automatic vital functions are measured as changes in pressure using a pressure sensor having an almost flat frequency response from 0.1 to 5 kHz and a sensitivity of 56 mV/Pa. Using the newly developed system, heartbeat, respiration, apnea, snoring and body movements are clearly measured. In addition, the optimal signal-to-noise (S/N) ratio by which to evaluate the reliability of the heart rate measurement is presented. Heart rates were measured for four different body postures, 13 different subjects, four different bed mattresses, and three different sensor positions. For these measurements, the S/N ratios ranged from 15.9 to 23.5 dB, and so were determined to be reliable.

Encoding frequency Modulation to improve cochlear implant performance in noise

Abstract: Different from traditional Fourier analysis, a signal can be decomposed into amplitude and frequency modulation components. The speech processing strategy in most modern cochlear implants only extracts and encodes amplitude modulation in a limited number of frequency bands. While amplitude modulation encoding has allowed cochlear implant users to achieve good speech recognition in quiet, their performance in noise is severely compromised. Here, we propose a novel speech processing strategy that encodes both amplitude and frequency modulations in order to improve cochlear implant performance in noise. By removing the center frequency from the subband signals and additionally limiting the frequency modulation's range and rate, the present strategy transforms the fast-varying temporal fine structure into a slowly varying frequency modulation signal. As a first step, we evaluated the potential contribution of additional frequency modulation to speech recognition in noise via acoustic simulations of the cochlear implant. We found that while amplitude modulation from a limited number of spectral bands is sufficient to support speech recognition in quiet, frequency modulation is needed to support speech recognition in noise. In particular, improvement by as much as 71 percentage points was observed for sentence recognition in the presence of a competing voice. The present result strongly suggests that frequency modulation be extracted and encoded to improve cochlear implant performance in realistic listening situations. We have proposed several implementation methods to stimulate further investigation.

Methodological principles of T wave alternans analysis: a unified framework

Abstract: Visible T wave alternans (TWA) in the electrocardiogram (ECG) had been regarded as an infrequent phenomenon during the first 80 years of electrocardiography. Nevertheless, computerized analysis changed this perception. In the last two decades, a variety of techniques for automatic TWA analysis have been proposed. These techniques have allowed researchers to detect nonvisible TWA in a wide variety of clinical and experimental conditions. Such studies have recently shown that TWA is related to cardiac instability and increased arrhythmogenicity. Comparison of TWA analysis methods is a difficult task due to the diversity of approaches. In this paper, we propose a unified framework which holds the existing methods. In the light of this framework, the methodological principles of the published TWA analysis schemes are compared and discussed. This framework may have an important role to develop new approaches to this problem.

An automatic beat detection algorithm for pressure signals

Abstract: Beat detection algorithms have many clinical applications including pulse oximetry, cardiac arrhythmia detection, and cardiac output monitoring. Most of these algorithms have been developed by medical device companies and are proprietary. Thus, researchers who wish to investigate pulse contour analysis must rely on manual annotations or develop their own algorithms. We designed an automatic detection algorithm for pressure signals that locates the first peak following each heart beat. This is called the percussion peak in intracranial pressure (ICP) signals and the systolic peak in arterial blood pressure (ABP) and pulse oximetry (SpO/sub 2/) signals. The algorithm incorporates a filter bank with variable cutoff frequencies, spectral estimates of the heart rate, rank-order nonlinear filters, and decision logic. We prospectively measured the performance of the algorithm compared to expert annotations of ICP, ABP, and SpO/sub 2/ signals acquired from pediatric intensive care unit patients. The algorithm achieved a sensitivity of 99.36% and positive predictivity of 98.43% on a dataset consisting of 42,539 beats.

Communication scheduling to minimize thermal effects of implanted biosensor networks in homogeneous tissue

Abstract: A network of biosensors can be implanted in a human body for health monitoring, diagnostics, or as a prosthetic device. Biosensors can be organized into clusters where most of the communication takes place within the clusters, and long range transmissions to the base station are performed by the cluster leader to reduce the energy cost. In some applications, the tissues are sensitive to temperature increase and may be damaged by the heat resulting from normal operations and the recharging of sensor nodes. Our work is the first to consider rotating the cluster leadership to minimize the heating effects on human tissues. We explore the factors that lead to temperature increase, and the process for calculating the specific absorption rate (SAR) and temperature increase of implanted biosensors by using the finite-difference time-domain (FDTD) method. We improve performance by rotating the cluster leader based on the leadership history and the sensor locations. We propose a simplified scheme, temperature increase potential, to efficiently predict the temperature increase in tissues surrounding implanted sensors. Finally, a genetic algorithm is proposed to exploit the search for an optimal temperature increase sequence.

Band-tunable and multiplexed integrated circuits for simultaneous recording and stimulation with microelectrode arrays

Abstract: Two thin-film microelectrode arrays with integrated circuitry have been developed for extracellular neural recording in behaving animals. An eight-site probe for simultaneous neural recording and stimulation has been designed that includes on-chip amplifiers that can be individually bypassed, allowing direct access to the iridium sites for electrical stimulation. The on-probe amplifiers have a gain of 38.9 dB, an upper-cutoff frequency of 9.9 kHz, and an input-referred noise of 9.2 /spl mu/V /sub rms/ integrated from 100 Hz to 10 kHz. The low-frequency cutoff of the amplifier is tunable to allow the recording of field potentials and minimize stimulus artifact. The amplifier consumes 68 /spl mu/W from /spl plusmn/1.5 V supplies and occupies 0.177 mm/sup 2/ in 3 /spl mu/m features. In vivo recordings have shown that the preamplifiers can record single-unit activity 1 ms after the onset of stimulation on sites as close as 20 /spl mu/m to the stimulating electrode. A second neural recording array has been developed which multiplexes 32 neural signals onto four output data leads. Providing gain on this array eliminates the need for bulky head-mounted circuitry and reduces motion artifacts. The time-division multiplexing circuitry has crosstalk between consecutive channels of less than 6% at a sample rate of 20 kHz per channel. Amplified, time-division-multiplexed multichannel neural recording allows the large-scale recording of neuronal activity in freely behaving small animals with minimum number of interconnect leads.

Motion estimation in beating heart surgery

Abstract: Minimally invasive beating-heart surgery offers substantial benefits for the patient, compared to conventional open surgery. Nevertheless, the motion of the heart poses increased requirements to the surgeon. To support the surgeon, algorithms for an advanced robotic surgery system are proposed, which offer motion compensation of the beating heart. This implies the measurement of heart motion, which can be achieved by tracking natural landmarks. In most cases, the investigated affine tracking scheme can be reduced to an efficient block matching algorithm allowing for realtime tracking of multiple landmarks. Fourier analysis of the motion parameters shows two dominant peaks, which correspond to the heart and respiration rates of the patient. The robustness in case of disturbance or occlusion can be improved by specially developed prediction schemes. Local prediction is well suited for the detection of single tracking outliers. A global prediction scheme takes several landmarks into account simultaneously and is able to bridge longer disturbances. As the heart motion is strongly correlated with the patient's electrocardiogram and respiration pressure signal, this information is included in a novel robust multisensor prediction scheme. Prediction results are compared to those of an artificial neural network and of a linear prediction approach, which shows the superior performance of the proposed algorithms.

The development and test of a device for the reconstruction of 3-D position and orientation by means of a kinematic sensor assembly with rate gyroscopes and accelerometers

Abstract: In this paper, we propose a device for the Position and Orientation (PO showing that the wearable device hereby presented permits the 3-D reconstruction of the movement of the body segment to which it is affixed for time-limited clinical applications.

An ultra-low-power programmable analog bionic ear processor

Abstract: We report a programmable analog bionic ear (cochlear implant) processor in a 1.5-/spl mu/m BiCMOS technology with a power consumption of 211 /spl mu/W and 77-dB dynamic range of operation. The 9.58 mm/spl times/9.23 mm processor chip runs on a 2.8 V supply and has a power consumption that is lower than state-of-the-art analog-to-digital (A/D)-then-DSP designs by a factor of 25. It is suitable for use in fully implanted cochlear-implant systems of the future which require decades of operation on a 100-mAh rechargeable battery with a finite number of charge-discharge cycles. It may also be used as an ultra-low-power spectrum-analysis front end in portable speech-recognition systems. The power consumption of the processor includes the 100 /spl mu/W power consumption of a JFET-buffered electret microphone and an associated on-chip microphone front end. An automatic gain control circuit compresses the 77-dB input dynamic range into a narrower internal dynamic range (IDR) of 57 dB at which each of the 16 spectral channels of the processor operate. The output bits of the processor are scanned and reported off chip in a format suitable for continuous-interleaved-sampling stimulation of electrodes. Power-supply-immune biasing circuits ensure robust operation of the processor in the high-RF-noise environment typical of cochlear implant systems.

Visual spatial attention control in an independent brain-computer interface

Abstract: This paper presents a novel brain computer interface (BCI) design employing visual evoked potential (VEP) modulations in a paradigm involving no dependency on peripheral muscles or nerves. The system utilizes electrophysiological correlates of visual spatial attention mechanisms, the self-regulation of which is naturally developed through continuous application in everyday life. An interface involving real-time biofeedback is described, demonstrating reduced training time in comparison to existing BCIs based on self-regulation paradigms. Subjects were cued to covertly attend to a sequence of letters superimposed on a flicker stimulus in one visual field while ignoring a similar stimulus of a different flicker frequency in the opposite visual field. Classification of left/right spatial attention is achieved by extracting steady-state visual evoked potentials (SSVEPs) elicited by the stimuli. Six out of eleven physically and neurologically healthy subjects demonstrate reliable control in binary decision-making, achieving at least 75% correct selections in at least one of only five sessions, each of approximately 12-min duration. The highest-performing subject achieved over 90% correct selections in each of four sessions. This independent BCI may provide a new method of real-time interaction for those with little or no peripheral control, with the added advantage of requiring only brief training.

Motion artifact cancellation in NIR spectroscopy using Wiener filtering

Abstract: We present a Wiener filtering based algorithm for the elimination of motion artifacts present in Near Infrared (NIR) spectroscopy measurements. Until now, adaptive filtering was the only technique used in the noise cancellation in NIR studies. The results in this preliminary study revealed that the proposed method gives better estimates than the classical adaptive filtering approach without the need for additional sensor measurements. Moreover, this novel technique has the potential to filter out motion artifacts in functional near infrared (fNIR) signals, too.

Micropower circuits for bidirectional wireless telemetry in neural recording applications

Abstract: State-of-the art neural recording systems require electronics allowing for transcutaneous, bidirectional data transfer. As these circuits will be implanted near the brain, they must be small and low power. We have developed micropower integrated circuits for recovering clock and data signals over a transcutaneous power link. The data recovery circuit produces a digital data signal from an ac power waveform that has been amplitude modulated. We have also developed an FM transmitter with the lowest power dissipation reported for biosignal telemetry. The FM transmitter consists of a low-noise biopotential amplifier and a voltage controlled oscillator used to transmit amplified neural signals at a frequency near 433 MHz. All circuits were fabricated in a standard 0.5-/spl mu/m CMOS VLSI process. The resulting chip is powered through a wireless inductive link. The power consumption of the clock and data recovery circuits is measured to be 129 /spl mu/W; the power consumption of the transmitter is measured to be 465 /spl mu/W when using an external surface mount inductor. Using a parasitic antenna less than 2 mm long, a received power level was measured to be -59.73 dBm at a distance of one meter.

Spatiotemporal blind source separation approach to atrial activity estimation in atrial tachyarrhythmias

Abstract: The analysis and characterization of atrial tachyarrhythmias requires, in a previous step, the extraction of the atrial activity (AA) free from ventricular activity and other artefacts. This contribution adopts the blind source separation (BSS) approach to AA estimation from multilead electrocardiograms (ECGs). Previously proposed BSS methods for AA extraction-e.g., independent component analysis (ICA)-exploit only the spatial diversity introduced by the multiple spatially-separated electrodes. However, AA typically shows certain degree of temporal correlation, with a narrowband spectrum featuring a main frequency peak around 3.5-9 Hz. Taking advantage of this observation, we put forward a novel two-step BSS-based technique which exploits both spatial and temporal information contained in the recorded ECG signals. The spatiotemporal BSS algorithm is validated on simulated and real ECGs from a significant number of atrial fibrillation (AF) and atrial flutter (AFL) episodes, and proves consistently superior to a spatial-only ICA method. In simulated ECGs, a new methodology for the synthetic generation of realistic AF episodes is proposed, which includes a judicious comparison between the known AA content and the estimated AA sources. Using this methodology, the ICA technique obtains correlation indexes of 0.751, whereas the proposed approach obtains a correlation of 0.830 and an error in the estimated signal reduced by a factor of 40%. In real ECG recordings, we propose to measure performance by the spectral concentration (SC) around the main frequency peak. The spatiotemporal algorithm outperforms the ICA method, obtaining a SC of 58.8% and 44.7%, respectively.

Audio-biofeedback for balance improvement: an accelerometry-based system

Abstract: This paper introduces a prototype audio-biofeedback system for balance improvement through the sonification using trunk kinematic information In tests of this system, normal healthy subjects performed several trials in which they stood quietly in three sensory conditions while wearing an accelerometric sensory unit and headphones The audio-biofeedback system converted in real-time the two-dimensional horizontal trunk accelerations into a stereo sound by modulating its frequency, level, and left/right balance Preliminary results showed that subjects improved balance using this audio-biofeedback system and that this improvement was greater the more that balance was challenged by absent or unreliable sensory cues In addition, high correlations were found between the center of pressure displacement and trunk acceleration, suggesting accelerometers may be useful for quantifying standing balance