- Bioengineering, Computational Neuroscience, Heart rate variability, Neuroscience, Brain-computer interfaces, Brain Computer Interface, and 16 moreERP (Cognitive Psychology), Prosthesis, Attention (Psychology), Robotic Hands, Hybrid Neural-robotic Systems, Brain machine interface, Neural Interfacing, Brain-Machine Interfaces, Peripheral Nervous System, Nonlinear dynamics, Bifurcation, Stability and Chaos, Machine Learning, Statistical machine learning, Support Vector Machines, and Point processesedit
Likelihood-based encoding models founded on point processes have received significant attention in the literature because of their ability to reveal the information encoded by spiking neural populations. We propose an approximation to the... more
Likelihood-based encoding models founded on point processes have received significant attention in the literature because of their ability to reveal the information encoded by spiking neural populations. We propose an approximation to the likelihood of a point-process model of neurons that holds under assumptions about the continuous time process that are physiologically reasonable for neural spike trains: the presence of a refractory period, the predictability of the conditional intensity function, and its integrability. These are properties that apply to a large class of point processes arising in applications other than neuroscience. The proposed approach has several advantages over conventional ones. In particular, one can use standard fitting procedures for generalized linear models based on iteratively reweighted least squares while improving the accuracy of the approximation to the likelihood and reducing bias in the estimation of the parameters of the underlying continuous-time model. As a result, the proposed approach can use a larger bin size to achieve the same accuracy as conventional approaches would with a smaller bin size. This is particularly important when analyzing neural data with high mean and instantaneous firing rates. We demonstrate these claims on simulated and real neural spiking activity. By allowing a substantive increase in the required bin size, our algorithm has the potential to lower the barrier to the use of point-process methods in an increasing number of applications.
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The presence of recurring arrhythmic events (also known as cardiac dysrhythmia or irregular heartbeats), as well as erroneous beat detection due to low signal quality, significantly affects estimation of both time and frequency domain... more
The presence of recurring arrhythmic events (also known as cardiac dysrhythmia or irregular heartbeats), as well as erroneous beat detection due to low signal quality, significantly affects estimation of both time and frequency domain indices of heart rate variability (HRV). A reliable, real-time classification and correction of ECG-derived heartbeats is a necessary prerequisite for an accurate online monitoring of HRV and cardiovascular control. We have developed a novel point-process-based method for real-time R-R interval error detection and correction. Given an R-wave event, we assume that the length of the next R-R interval follows a physiologically motivated, time-varying inverse Gaussian probability distribution. We then devise an instantaneous automated detection and correction procedure for erroneous and arrhythmic beats by using the information on the probability of occurrence of the observed beat provided by the model. We test our algorithm over two datasets from the PhysioNet archive. The Fantasia normal rhythm database is artificially corrupted with known erroneous beats to test both the detection procedure and correction procedure. The benchmark MIT-BIH Arrhythmia database is further considered to test the detection procedure of real arrhythmic events and compare it with results from previously published algorithms. Our automated algorithm represents an improvement over previous procedures, with best specificity for the detection of correct beats, as well as highest sensitivity to missed and extra beats, artificially misplaced beats, and for real arrhythmic events. A near-optimal heartbeat classification and correction, together with the ability to adapt to time-varying changes of heartbeat dynamics in an online fashion, may provide a solid base for building a more reliable real-time HRV monitoring device.
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The focus of the PhysioNet/CinC Challenge 2012 is to develop methods for patient-specific prediction of in-hospital mortality using general descriptors recorded at the time of admission to the ICU and up to 37 time-series measurements... more
The focus of the PhysioNet/CinC Challenge 2012 is to develop methods for patient-specific prediction of in-hospital mortality using general descriptors recorded at the time of admission to the ICU and up to 37 time-series measurements collected during the first 48 hours after admission. We developed an algorithm that uses both general descriptors and time-series measurements to predict the in-hospital death (IHD) of ICU patients in Event 1, and to provide a probability estimate of IHD in Event 2. Both aggregated variables and general descriptors were used as features of quadratic Support Vector Machine (SVM) classifiers. Six SVMs were trained using, for each one, all the positive examples plus, in turn, one sixth of the negative examples in the training set. Finally, a Generalized Linear Model with probit link was used to predict the probability of IHD for Event 2 using the raw outputs of the six SVMs as regressors. A positive binary prediction of IHD for Event 1 was made when the probability estimate was higher than an optimized threshold. Official final results of the challenge reported that our entry achieved an Event 2 score of 17.8835, which is the best score out of the total 23 submissions, and Event 1 score of 0.534454 (second best score).
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The P300 is an endogenous event-related potential (ERP) that is naturally elicited by rare and significant external stimuli. P300s are used increasingly frequently in brain-computer interfaces (BCIs) because the users of ERP-based BCIs... more
The P300 is an endogenous event-related potential (ERP) that is naturally elicited by rare and significant external stimuli. P300s are used increasingly frequently in brain-computer interfaces (BCIs) because the users of ERP-based BCIs need no special training. However, P300 waves are hard to detect and, therefore, multiple target stimulus presentations are needed before an interface can make a reliable decision. While significant improvements have been made in the detection of P300s, no particular attention has been paid to the variability in shape and timing of P300 waves in BCIs. In this paper we start filling this gap by documenting, modelling and exploiting a modulation in the amplitude of P300s related to the number of non-targets preceding a target in a Donchin speller. The basic idea in our approach is to use an appropriately weighted average of the responses produced by a classifier during multiple stimulus presentations, instead of the traditional plain average. This makes it possible to weigh more heavily events that are likely to be more informative, thereby increasing the accuracy of classification. The optimal weights are determined through a mathematical model that precisely estimates the accuracy of our speller as well as the expected performance improvement w.r.t. the traditional approach. Tests with two independent datasets show that our approach provides a marked statistically significant improvement in accuracy over the top-performing algorithm presented in the literature to date. The method and the theoretical models we propose are general and can easily be used in other P300-based BCIs with minimal changes.
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Recordings from thin-film Longitudinal Intra-Fascicular Electrodes (tfLIFE) together with a wavelet-based denoising and a correlation-based spike sorting algorithm, give access to firing patterns of muscle spindle afferents. In this study... more
Recordings from thin-film Longitudinal Intra-Fascicular Electrodes (tfLIFE) together with a wavelet-based denoising and a correlation-based spike sorting algorithm, give access to firing patterns of muscle spindle afferents. In this study we use a point process probability structure to assess mechanical stimulus-response characteristics of muscle spindle spike trains. We assume that the stimulus intensity is primarily a linear combination of the spontaneous firing rate, the muscle extension, and the stretch velocity. By using the ability of the point process framework to provide an objective goodness of fit analysis, we were able to distinguish two classes of spike clusters with different statistical structure. We found that spike clusters with higher SNR have a temporal structure that can be fitted by an inverse Gaussian distribution while lower SNR clusters follow a Poisson-like distribution. The point process algorithm is further able to provide the instantaneous intensity function associated with the stimulus-response model with the best goodness of fit. This important result is a first step towards a point process decoding algorithm to estimate the muscle length and possibly provide closed loop Functional Electrical Stimulation (FES) systems with natural sensory feedback information.
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The oddball protocol is often used in brain-computer interfaces (BCIs) to induce P300 ERPs, although, recently, some issues have been shown to detrimentally effect its performance. In this paper, we study a new periodic protocol and... more
The oddball protocol is often used in brain-computer interfaces (BCIs) to induce P300 ERPs, although, recently, some issues have been shown to detrimentally effect its performance. In this paper, we study a new periodic protocol and explore whether it can compete with the standard oddball protocol within the context of a BCI mouse. We found that the new protocol consistently and significantly outperforms the standard oddball protocol in relation to information transfer rates (33 bits/min for the former and 22 bits/min for the latter, measured at 90% accuracy) as well as P300 amplitudes. Furthermore, we performed a comparison of two periodic protocols with two less conventional oddball-like protocols that reveals the importance of the interactions between task and sequence in determining the success of a protocol.
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Abstract This paper introduces a novel methodology able to provide time varying estimates of the Lyapunov Spectrum within a point process framework. The algorithm is applied to ECG-derived data to characterize heartbeat nonlinear dynamics... more
Abstract This paper introduces a novel methodology able to provide time varying estimates of the Lyapunov Spectrum within a point process framework. The algorithm is applied to ECG-derived data to characterize heartbeat nonlinear dynamics by using a cubic autoregressive point process model. Estimation of the model parameters is ensured by the Laguerre expansion of the Wiener-Volterra kernels along with a maximum local log-likelihood procedure.
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A brain-machine interface (BMI) is a particular class of human-machine interface (HMI). BMIs have so far been studied mostly as a communication means for people who have little or no voluntary control of muscle activity. For able-bodied... more
A brain-machine interface (BMI) is a particular class of human-machine interface (HMI). BMIs have so far been studied mostly as a communication means for people who have little or no voluntary control of muscle activity. For able-bodied users, such as astronauts, a BMI would only be practical if conceived as an augmenting interface. A method is presented for pointing out effective combinations of HMIs and applications of robotics and automation to space. Latency and throughput are selected as performance measures for a hybrid bionic system (HBS), that is, the combination of a user, a device, and a HMI. We classify and briefly describe HMIs and space applications and then compare the performance of classes of interfaces with the requirements of classes of applications, both in terms of latency and throughput. Regions of overlap correspond to effective combinations. Devices requiring simpler control, such as a rover, a robotic camera, or environmental controls are suitable to be driven by means of BMI technology. Free flyers and other devices with six degrees of freedom can be controlled, but only at low-interactivity levels. More demanding applications require conventional interfaces, although they could be controlled by BMIs once the same levels of performance as currently recorded in animal experiments are attained. Robotic arms and manipulators could be the next frontier for noninvasive BMIs. Integrating smart controllers in HBSs could improve interactivity and boost the use of BMI technology in space applications.
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Research Interests:
In this paper we use genetic programming - an evolutionary program-induction technology - to evolve algorithms that accurately approximate the behaviour of two standard detectors of ocular movement based on Electro-oculogram (EOG). The... more
In this paper we use genetic programming - an evolutionary program-induction technology - to evolve algorithms that accurately approximate the behaviour of two standard detectors of ocular movement based on Electro-oculogram (EOG). The prediction is based entirely on EEG signals, i.e., without using EOG, making it possible to detect eye movements even in data recorded without EOG or eye tracking. Experimental results with this approach are very encouraging.
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The development of hybrid neuroprosthetic systems (HBSs) linking the human nervous system with artificial devices is an important area of research that is currently addressed by several groups to restore sensorimotor function in people... more
The development of hybrid neuroprosthetic systems (HBSs) linking the human nervous system with artificial devices is an important area of research that is currently addressed by several groups to restore sensorimotor function in people affected by different disabilities. It is particularly important to establish a fast, intuitive, bidirectional flow of information between the nervous system of the user and the smart robotic device. Among the possible solutions to achieve this goal, interfaces with the peripheral nervous system and in particular intraneural electrodes can represent an interesting choice. In the present study, thin-film longitudinal intra-fascicular electrodes were implanted in the median and ulnar nerves of an amputee. The possibility of restoring the bidirectional link between the subject and the external world was investigated during a 4 week trial. The result showed that both the extraction of motor information and the restoration of sensory function are possible.
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Abstract Robust and automated classification and correction of ECG-derived heart beats are a necessary prerequisite for an accurate real-time estimation of measures of heart rate variability and cardiovascular control. In particular, the... more
Abstract Robust and automated classification and correction of ECG-derived heart beats are a necessary prerequisite for an accurate real-time estimation of measures of heart rate variability and cardiovascular control. In particular, the low quality of the signal, as well as the presence of recurring arrhythmic events, may significantly affect estimation accuracy. We here present a novel point process based method for a real time RR interval error detection and correction.
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Purpose Measures of heart rate variability (HRV) and respiratory sinus arrhythmia (RSA), which are the healthy fluctuations in heart rate that reflect autonomic influences on cardiac activity, have been proposed as indicators of physical... more
Purpose Measures of heart rate variability (HRV) and respiratory sinus arrhythmia (RSA), which are the healthy fluctuations in heart rate that reflect autonomic influences on cardiac activity, have been proposed as indicators of physical and psychological health. Previous studies suggest that HRV increases during some meditative states, but it is not clear how autonomic tone is affected either in the short term during meditative states, nor as a long-term result of meditation practice.
Abstract Point-process models have been recognized as a distinguished tool for the instantaneous assessment of heartbeat dynamics. Although not thoroughly linked to the physiology, nonlinear models also yield a more accurate... more
Abstract Point-process models have been recognized as a distinguished tool for the instantaneous assessment of heartbeat dynamics. Although not thoroughly linked to the physiology, nonlinear models also yield a more accurate quantification of cardiovascular control dynamics.
Abstract We present a novel methodology for instantaneous estimation of quantitative correlates of depth of Anesthesia from noninvasive electrocardiographic recordings. The analysis is based on a point process model of heartbeat dynamics... more
Abstract We present a novel methodology for instantaneous estimation of quantitative correlates of depth of Anesthesia from noninvasive electrocardiographic recordings. The analysis is based on a point process model of heartbeat dynamics that allows for continuous tracking of linear and nonlinear HRV indices, including a novel instantaneous assessment of the Lyapunov Spectrum by using a cubic autoregressive formulation.
The measurement of indoor climate in heritage buildings can provide valuable sources of data needed for an optimal use and management of the works of art exhibited inside. It is a fundamental technique to evaluate environmental damage and... more
The measurement of indoor climate in heritage buildings can provide valuable sources of data needed for an optimal use and management of the works of art exhibited inside. It is a fundamental technique to evaluate environmental damage and degradation processes, to support the protection, conservation and preservation of works of art, improving the quality of museum environments. This is particularly important when the old buildings have special architectural and historical value.
Abstract In this paper we use genetic programming-an evolutionary program-induction technology-to evolve algorithms that accurately approximate the behaviour of two standard detectors of ocular movement based on Electro-oculogram (EOG).... more
Abstract In this paper we use genetic programming-an evolutionary program-induction technology-to evolve algorithms that accurately approximate the behaviour of two standard detectors of ocular movement based on Electro-oculogram (EOG). The prediction is based entirely on EEG signals, ie, without using EOG, making it possible to detect eye movements even in data recorded without EOG or eye tracking. Experimental results with this approach are very encouraging.
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BACKGROUND: Interfacing an amputee's upper-extremity stump nerves to control a robotic hand requires training of the individual and algorithms to process interactions between cortical and peripheral signals. OBJECTIVE: To evaluate for the... more
BACKGROUND: Interfacing an amputee's upper-extremity stump nerves to control a robotic hand requires training of the individual and algorithms to process interactions between cortical and peripheral signals. OBJECTIVE: To evaluate for the first time whether EEG-driven analysis of peripheral neural signals as an amputee practices could improve the classification of motor commands. METHODS: Four thin-film longitudinal intrafascicular electrodes (tf-LIFEs-4) were implanted in the median and ulnar nerves of the stump in the distal upper arm for 4 weeks. Artificial intelligence classifiers were implemented to analyze LIFE signals recorded while the participant tried to perform 3 different hand and finger movements as pictures representing these tasks were randomly presented on a screen. In the final week, the participant was trained to perform the same movements with a robotic hand prosthesis through modulation of tf-LIFE-4 signals. To improve the classification performance, an event-related desynchronization/synchronization (ERD/ERS) procedure was applied to EEG data to identify the exact timing of each motor command. RESULTS: Real-time control of neural (motor) output was achieved by the participant. By focusing electroneurographic (ENG) signal analysis in an EEG-driven time window, movement classification performance improved. After training, the participant regained normal modulation of background rhythms for movement preparation (α/βband desynchronization) in the sensorimotor area contralateral to the missing limb. Moreover, coherence analysis found a restored αband synchronization of Rolandic area with frontal and parietal ipsilateral regions, similar to that observed in the opposite hemisphere for movement of the intact hand. Of note, phantom limb pain (PLP) resolved for several months. CONCLUSIONS: Combining information from both cortical (EEG) and stump nerve (ENG) signals improved the classification performance compared with tf-LIFE signals processing alone; training led to cortical reorganization and mitigation of PLP.
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We present a novel statistical paradigm for modeling and analysis of gait variability which captures the natural point process structure of gait intervals and allows for definition of new measures instantaneous mean and standard... more
We present a novel statistical paradigm for modeling and analysis of gait variability which captures the natural point process structure of gait intervals and allows for definition of new measures instantaneous mean and standard deviation. We validate our model using two existing data sets from physionet.org. Results show an excellent model fit and yield insights into the underlying statistical structure behind human gait. Statistical analyses further corroborate previous findings of increased variability in gait at different speeds, both self-paced and metronome-paced, and reveal a significant increase in gait variability in Parkinson's subjects, as compared to young and elderly healthy subjects. These results indicate the validity of a point process approach to the analysis of gait, and the potential utility of incorporating instantaneous measures of gait into diagnostic or patient monitoring applications.
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We present a novel, automatic point-process approach that is able to provide continuous, instantaneous estimates of heart rate variability (HRV) and respiratory sinus arrhythmia (RSA) in long duration data recordings such as those during... more
We present a novel, automatic point-process approach that is able to provide continuous, instantaneous estimates of heart rate variability (HRV) and respiratory sinus arrhythmia (RSA) in long duration data recordings such as those during an entire night of sleep. We analyze subjects with and without sleep apnea who underwent diagnostic polysomnography. The proposed algorithm is able to quantify multi-scale high time resolution autonomic signatures of sleep fragmentation, such as arousals and stage transitions, throughout an entire night. Results demonstrate the ability of our methods to track fast dynamic transitions from sleep to wake and between REM sleep and other sleep stages, providing resolution details not available in sleep scoring summaries. An automatic threshold-based procedure is further able to detect brief arousals, with the instantaneous indices characterizing specific arousal dynamic signatures.
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We present a comprehensive probabilistic point process framework to estimate and monitor the instantaneous heartbeat dynamics as related to specific cardiovascular control mechanisms and hemodynamics. Assessment of the model's statistics... more
We present a comprehensive probabilistic point process framework to estimate and monitor the instantaneous heartbeat dynamics as related to specific cardiovascular control mechanisms and hemodynamics. Assessment of the model's statistics is established through the Wiener-Volterra theory and a multivariate autoregressive (AR) structure. A variety of instantaneous cardiovascular metrics, such as heart rate (HR), heart rate variability (HRV), respiratory sinus arrhythmia (RSA), and baroreceptor-cardiac reflex (BRS), can be rigorously derived within a parametric framework and instantaneously updated with an adaptive algorithm. Instantaneous metrics of nonlinearity, such as the bispectrum of heartbeat intervals, can also be derived. We have applied the proposed point process framework to experimental recordings from healthy subjects in order to monitor cardiovascular regulation under propofol anesthesia. Results reveal interesting dynamic trends across different pharmacological interventions, confirming the ability of the algorithm to track important changes in cardiorespiratory elicited interactions, and pointing at our mathematical approach as a promising monitoring tool for an accurate, noninvasive assessment of general anesthesia.
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Among the possible interfaces with the peripheral nervous system (PNS), intraneural electrodes represent an interesting solution for their potential advantages such as the possibility of extracting spikes from electroneurographic (ENG)... more
Among the possible interfaces with the peripheral nervous system (PNS), intraneural electrodes represent an interesting solution for their potential advantages such as the possibility of extracting spikes from electroneurographic (ENG) signals. Their use could increase the precision and the amount of information which can be detected with respect to other processing methods. In this study, in order to verify this assumption, thin-film longitudinal intrafascicular electrodes (tfLIFE) were implanted in the sciatic nerve of rabbits. Various sensory stimuli were applied to the hind limb of the animal and the elicited ENG signals were recorded using the tfLIFEs. These signals were processed to determine whether the different types of information can be decoded. Signals were wavelet denoised and spike sorted. Support vector machines were trained to use the spike waveforms found to infer the stimulus applied to the rabbit. This approach was also compared with previously used ENG-processing methods. The results indicate that the combination of wavelet denoising and spike sorting techniques can increase the amount of information extractable from ENG signals recorded with intraneural electrodes. This strategy could allow the development of more effective closed-loop neuroprostheses and hybrid bionic systems connecting the human nervous system with artificial devices.
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In this paper we propose a brain-computer interface (BCI) mouse based on P300 waves in electroencephalogram (EEG) signals. The system is analogue in that at no point a binary decision is made as to whether or not a P300 was actually... more
In this paper we propose a brain-computer interface (BCI) mouse based on P300 waves in electroencephalogram (EEG) signals. The system is analogue in that at no point a binary decision is made as to whether or not a P300 was actually produced in response to the stimuli. Instead, the 2-D motion of the pointer on the screen, using a novel BCI paradigm, is controlled by directly combining the amplitudes of the output produced by a filter in the presence of different stimuli. This filter and the features to be combined within it are optimised by an evolutionary algorithm.
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The dream of controlling and guiding computer-based systems using human brain signals has slowly but steadily become a reality. The available technology allows real-time implementation of systems that measure neuronal activity, convert... more
The dream of controlling and guiding computer-based systems using human brain signals has slowly but steadily become a reality. The available technology allows real-time implementation of systems that measure neuronal activity, convert their signals, and translate their output for the purpose of controlling mechanical and electronic systems. This paper describes the state of the art of non-invasive brain-machine interfaces (BMIs) and critically investigates both the current technological limits and the future potential that BMIs have for space applications. We present an assessment of the advantages that BMIs can provide and justify the preferred candidate concepts for space applications together with a vision of future directions for their implementation.
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The possibility of controlling dexterous hand prostheses by using a direct connection with the nervous system is particularly interesting for the significant improvement of the quality of life of patients, which can derive from this... more
The possibility of controlling dexterous hand prostheses by using a direct connection with the nervous system is particularly interesting for the significant improvement of the quality of life of patients, which can derive from this achievement. Among the various approaches, peripheral nerve based intrafascicular electrodes are excellent neural interface candidates, representing an excellent compromise between high selectivity and relatively low invasiveness. Moreover, this approach has undergone preliminary testing in human volunteers and has shown promise. In this paper, we investigate whether the use of intrafascicular electrodes can be used to decode multiple sensory and motor information channels with the aim to develop a finite state algorithm that may be employed to control neuroprostheses and neurocontrolled hand prostheses. The results achieved both in animal and human experiments show that the combination of multiple sites recordings and advanced signal processing techniques (such as wavelet denoising and spike sorting algorithms) can be used to identify both sensory stimuli (in animal models) and motor commands (in a human volunteer). These findings have interesting implications, which should be investigated in future experiments.
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Significant strides have been recently made to develop highly sensorized cybernetic prostheses aimed at restoring sensorimotor limb functions to those who have lost them because of a traumatic event (amputation). In these cases, one of... more
Significant strides have been recently made to develop highly sensorized cybernetic prostheses aimed at restoring sensorimotor limb functions to those who have lost them because of a traumatic event (amputation). In these cases, one of the main goals is to create a bidirectional link between the artificial devices (e.g., robotic hands, arms, or legs) and the nervous system. Several human-machine interfaces (HMIs) are currently used to this aim. Among them, interfaces with the peripheral nervous system and in particular longitudinal intrafascicular electrodes can be a promising solution able to improve the current situation. In this paper, the potentials and limits of the use of this interface to control robotic devices are presented. Specific information is provided on: 1) the neurophysiological bases for the use peripheral nerve interfaces; 2) a comparison of the potentials of the different peripheral neural interfaces; 3) the possibility of extracting and appropriately interpreting the neural code for motor commands and of delivering sensory feedback by stimulating afferent fibers by using longitudinal intrafascicular electrodes; 4) a preliminary comparative analysis of the performance of this approach with the ones of others HMIs; 5) the open issues which have to be addressed for a chronic usability of this approach.
Research Interests: Robotics, Biomedical Engineering, Cybernetics, Humans, Peripheral Nervous System, and 13 moreComparative Analysis, Human Machine Interface, Electromyography, Amputees, Sensory Feedback, Peripheral nerves, Hand, Peripheral Nerve, Nervous System, Equipment Design, Equipment Failure Analysis, Electrical And Electronic Engineering, and neural code
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Research Interests: Neuroscience, Cognitive Science, Animal Studies, Electroencephalography, Brain Computer Interface, and 16 moreBrain, Humans, Computer Simulation, Brain machine interface, Information Transfer, Reaction Time, Feedback, Human Machine Interface, Robot Arm, Computer User Interface Design, Humanoid robot, Statistical Methods for Neuroscience, Neurosciences, Muscle Activity, Real-world Application, and Man–machine Systems
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Stimulus-locked, response-locked, and ERP-locked averaging are effective methods for reducing artifacts in ERP analysis. However, they suffer from a magnifying-glass effect: they increase the resolution of specific ERPs at the cost of... more
Stimulus-locked, response-locked, and ERP-locked averaging are effective methods for reducing artifacts in ERP analysis. However, they suffer from a magnifying-glass effect: they increase the resolution of specific ERPs at the cost of blurring other ERPs. Here we propose an extremely simple technique - binning trials based on response times and then averaging - which can significantly alleviate the problems of other averaging methods. We have empirically evaluated the technique in an experiment where the task requires detecting a target in the presence of distractors. We have also studied the signal-to-noise ratio and the resolving power of averages with and without binning. Results indicate that the method produces clearer representations of ERPs than either stimulus-locked and response-locked averaging, revealing finer details of ERPs and helping in the evaluation of the amplitude and latency of ERP waves. The method is applicable to within-subject and between-subject averages.
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Research Interests: Engineering, Robotics, Artificial Intelligence, Transcranial Magnetic Stimulation, Motor Control, and 18 moreHumans, Phantom limb, Sensation, Movement, Computer Systems, Clinical, Male, Neuronal Plasticity, Neural plasticity, Amputees, Clinical Neurophysiology, Adult, Hand, Upper Limb, Artificial Limbs, Cortical Reorganization, Median Nerve, and Ulnar Nerve
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We examine neural signals from Longitudinally implanted Intra-Fascicular Electrodes (LIFE) in a chronic, rabbit model. Translation-invariant wavelet de-noising methods are used to improve S%R. Then traditional template-based spike sorting... more
We examine neural signals from Longitudinally implanted Intra-Fascicular Electrodes (LIFE) in a chronic, rabbit model. Translation-invariant wavelet de-noising methods are used to improve S%R. Then traditional template-based spike sorting is applied to discriminate single units. We investigate the effect of discriminating between identified units on Brain Machine Interface (BMI) decoding performance. We infer the stability of LIFE based on decoding performance with and without current BMI methods to counter-act electrode neural signal degradation.
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The P300 is an endogenous component of EEG event related potentials which is elicited by rare and significant stimuli. P300s are used increasingly frequently in Brain Computer Interfaces (BCI) because, being naturally elicited in response... more
The P300 is an endogenous component of EEG event related potentials which is elicited by rare and significant stimuli. P300s are used increasingly frequently in Brain Computer Interfaces (BCI) because, being naturally elicited in response to external stimuli, users do not need special training. However, P300 waves are hard to detect and, therefore, multiple stimulus presentations are needed before an interface can make a reliable decision. While significant improvements have been made in the detection of P300s, no particular attention has been paid to the variability in shape and timing of P300 waves and its exploitation in BCI. In this paper we start filling this gap, by first documenting and then exploiting a modulation in amplitude of P300 caused by target-to-target interval (TTI) differences. We demonstrate this within the context of the Donchin's speller, which is perhaps the best known example of a BCI system relying on the detection P300 waves, where target-to-target interval variations are induced by stimuli randomisation. In particular we show that by specialising detectors to work with P300s elicited with each TTI, we can further improve the performance of the best known Donchin's speller with minimal changes.
Research Interests:
A brain-machine interface (BMI) is a particular class of human-machine interface (HMI). BMIs have so far been studied mostly as a communication means for people who have little or no voluntary control of muscle activity. For able-bodied... more
A brain-machine interface (BMI) is a particular class of human-machine interface (HMI). BMIs have so far been studied mostly as a communication means for people who have little or no voluntary control of muscle activity. For able-bodied users, such as astronauts, a BMI would only be practical if conceived as an augmenting interface. A method is presented for pointing out effective combinations of HMIs and applications of robotics and automation to space. Latency and throughput are selected as performance measures for a hybrid bionic system (HBS), that is, the combination of a user, a device, and a HMI. We classify and briefly describe HMIs and space applications and then compare the performance of classes of interfaces with the requirements of classes of applications, both in terms of latency and throughput. Regions of overlap correspond to effective combinations. Devices requiring simpler control, such as a rover, a robotic camera, or environmental controls are suitable to be driven by means of BMI technology. Free flyers and other devices with six degrees of freedom can be controlled, but only at low-interactivity levels. More demanding applications require conventional interfaces, although they could be controlled by BMIs once the same levels of performance as currently recorded in animal experiments are attained. Robotic arms and manipulators could be the next frontier for noninvasive BMIs. Integrating smart controllers in HBSs could improve interactivity and boost the use of BMI technology in space applications.
Research Interests:
To investigate the potential relationships between Heart rate variability (HRV) and objective performance-subjective alertness measures during sleep deprivation, a novel point process algorithm was applied to ECG data from healthy young... more
To investigate the potential relationships between Heart rate variability (HRV) and objective performance-subjective alertness measures during sleep deprivation, a novel point process algorithm was applied to ECG data from healthy young subjects in a 52-hour Constant Routine protocol, which includes sleep deprivation. Our algorithm is able to estimate the time-varying behavior of the HRV spectral indexes in an on-line instantaneous fashion. Results demonstrate the ability of our framework to provide high time-resolution sympatho-vagal dynamics as measured by spectral low frequency (LF) and high frequency (HF) power. Correlation analysis on individual subjects reveals a relevant correspondence between LF/HF and subjective alertness during the initial hours of sleep deprivation. At longer times awake, high correlation levels between LF/HF and objective performance indicate an increasing sympathetic drive as performance measures worsen. These results suggest that our point-process based HRV assessment could aid in real-time prediction of performance-alertness.
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Note: OCR errors may be found in this Reference List extracted from the full text article. ACM has opted to expose the complete List rather than only correct and linked references. ... P. Buckle and J. Devereux, "The nature of... more
Note: OCR errors may be found in this Reference List extracted from the full text article. ACM has opted to expose the complete List rather than only correct and linked references. ... P. Buckle and J. Devereux, "The nature of work-related neck and upper limb musculoskeletal ...