Future developments in brain-machine interface research

Neuroprosthetic devices based on brain-machine interface technology hold promise for the restoration of body mobility in patients suffering from devastating motor deficits caused by brain injury, neurologic diseases and limb loss. During the last decade, considerable progress has been achieved in this multidisciplinary research, mainly in the brain-machine interface that enacts upper-limb functionality. However, a considerable number of problems need to be resolved before fully functional limb neuroprostheses can be built. To move towards developing neuroprosthetic devices for humans, brain-machine interface research has to address a number of issues related to improving the quality of neuronal recordings, achieving stable, long-term performance, and extending the brain-machine interface approach to a broad range of motor and sensory functions. Here, we review the future steps that are part of the strategic plan of the Duke University Center for Neuroengineering, and its partners, the Brazilian National Institute of Brain-Machine Interfaces and the École Polytechnique Fédérale de Lausanne (EPFL) Center for Neuroprosthetics, to bring this new technology to clinical fruition

Compensation of Laser Phase Noise Using DSP in Multichannel Fiber-Optic Communications

One of the main impairments that limit the throughput of fiber-optic communication systems is laser phase noise, where the phase of the laser output drifts with time. This impairment can be highly correlated across channels that share lasers in multichannel fiber-optic systems based on, e.g., wavelength-division multiplexing using frequency combs or space-division multiplexing. In this thesis, potential improvements in the system tolerance to laser phase noise that are obtained through the use of joint-channel digital signal processing are investigated. To accomplish this, a simple multichannel phase-noise model is proposed, in which the phase noise is arbitrarily correlated across the channels. Using this model, high-performance pilot-aided phase-noise compensation and data-detection algorithms are designed for multichannel fiber-optic systems using Bayesian-inference frameworks. Through Monte Carlo simulations of coded transmission in the presence of moderate laser phase noise, it is shown that joint-channel processing can yield close to a 1 dB improvement in power efficiency. It is further shown that the algorithms are highly dependent on the positions of pilots across time and channels. Hence, the problem of identifying effective pilot distributions is studied.The proposed phase-noise model and algorithms are validated using experimental data based on uncoded space-division multiplexed transmission through a weakly-coupled, homogeneous, single-mode, 3-core fiber. It is found that the performance improvements predicted by simulations based on the model are reasonably close to the experimental results. Moreover, joint-channel processing is found to increase the maximum tolerable transmission distance by up to 10% for practical pilot rates.Various phenomena decorrelate the laser phase noise between channels in multichannel transmission, reducing the potency of schemes that exploit this correlation. One such phenomenon is intercore skew, where the spatial channels experience different propagation velocities. The effect of intercore skew on the performance of joint-core phase-noise compensation is studied. Assuming that the channels are aligned in the receiver, joint-core processing is found to be beneficial in the presence of skew if the linewidth of the local oscillator is lower than the light-source laser linewidth.In the case that the laser phase noise is completely uncorrelated across channels in multichannel transmission, it is shown that the system performance can be improved by applying transmitter-side multidimensional signal rotations. This is found by numerically optimizing rotations of four-dimensional signals that are transmitted through two channels. Structured four-dimensional rotations based on Hadamard matrices are found to be near-optimal. Moreover, in the case of high signal-to-noise ratios and high signal dimensionalities, Hadamard-based rotations are found to increase the achievable information rate by up to 0.25 bits per complex symbol for transmission of higher-order modulations

Inertial Measurement Unit based Virtual Antenna Arrays - DoA Estimation and Positioning in Wireless Networks

Today we have different location based services available in a mobile phone or mobile station (MS). These services include: direction finding to nearby ATMs, locating favorite food restaurants, or finding any target destination. Similarly, we see different applications of the positioning and navigation systems in firefighting or other rescue operations. The common factor in almost all of the location based services is the system's ability to determine the user's current position, with reference to a floor plan or a navigation map. Current technologies are using sensor data measurements from one or more sensors, available to the positioning device, for positioning and navigation. Typical examples are radio based positioning such as global positioning system, inertial sensors based inertial navigation system, or camera based positioning systems. Different accuracy and availability conditions of the positioning and navigation solution can be obtained depending on the positioning algorithms and the available sensor information.Nowadays, the focus of research in positioning and navigation has been mostly on the use of existing hardware infrastructure and low-cost solutions, such that the proposed technique can be deployed with ease and without extra infrastructure requirements as well as without any expensive sensor equipment. In this work, we investigate a novel idea for positioning using existing wireless networks and low-cost inertial sensor measurements available at the MS. We propose to use received baseband radio signal along with inertial sensor data, such as accelerometer and rate gyroscope measurements, for direction of arrival (DoA) estimation and positioning. The DoA information from different base stations or access points can be used to estimate the MS position using triangulation technique. Furthermore, due to size and cost restrictions it is difficult to have real antenna arrays at the MS, the idea of DoA estimation and positioning is proposed to be used with single antenna devices by using the so-called virtual antenna arrays.We have presented our research results in three different papers. We provide measurement based results to perform a quantitative evaluation of DoA estimation using arbitrary virtual antenna arrays in 3-D; where a state-of-the-art high-resolution algorithm has been used for radio signal parameter estimation. Furthermore, we provide an extended Kalman filter framework to investigate the performance of unaided inertial navigation systems with 3-axis accelerometer and 3-axis rate gyroscope measurements, from a six-degrees-of-freedom inertial measurement unit. Using the extended Kalman filter framework, we provide results for position estimation error standard deviation with respect to integration time for an unaided inertial navigation system; where the effect of different stochastic errors noise sources in the inertial sensors measurements such as white Gaussian noise and bias instability noise is investigated. Also, we derive a closed form expression for Cramér-Rao lower bound to investigate DoA estimation accuracy for a far-field source using random antenna arrays in 3-D. The Cramér-Rao lower bound is obtained using known antenna coordinates as well as using estimated antenna coordinates, where the antenna coordinates are estimated with an uncertainty whose standard deviation is known. Furthermore, using Monte-Carlo simulations for random antenna arrays, we provide Cramér-Rao lower bound based performance evaluation of random 3-D antenna arrays for DoA estimation

Identification of audio evoked response potentials in ambulatory EEG data

Electroencephalography (EEG) is commonly used for observing brain function over a period of time. It employs a set of invasive electrodes on the scalp to measure the electrical activity of the brain. EEG is mainly used by researchers and clinicians to study the brain’s responses to a specific stimulus - the event-related potentials (ERPs). Different types of undesirable signals, which are known as artefacts, contaminate the EEG signal. EEG and ERP signals are very small (in the order of microvolts); they are often obscured by artefacts with much larger amplitudes in the order of millivolts. This greatly increases the difficulty of interpreting EEG and ERP signals.Typically, ERPs are observed by averaging EEG measurements made with many repetitions of the stimulus. The average may require many tens of repetitions before the ERP signal can be observed with any confidence. This greatly limits the study and useof ERPs. This project explores more sophisticated methods of ERP estimation from measured EEGs. An Optimal Weighted Mean (OWM) method is developed that forms a weighted average to maximise the signal to noise ratio in the mean. This is developedfurther into a Bayesian Optimal Combining (BOC) method where the information in repetitions of ERP measures is combined to provide a sequence of ERP estimations with monotonically decreasing uncertainty. A Principal Component Analysis (PCA) isperformed to identify the basis of signals that explains the greatest amount of ERP variation. Projecting measured EEG signals onto this basis greatly reduces the noise in measured ERPs. The PCA filtering can be followed by OWM or BOC. Finally, crosschannel information can be used. The ERP signal is measured on many electrodes simultaneously and an improved estimate can be formed by combining electrode measurements. A MAP estimate, phrased in terms of Kalman Filtering, is developed using all electrode measurements.The methods developed in this project have been evaluated using both synthetic and measured EEG data. A synthetic, multi-channel ERP simulator has been developed specifically for this project.Numerical experiments on synthetic ERP data showed that Bayesian Optimal Combining of trial data filtered using a combination of PCA projection and Kalman Filtering, yielded the best estimates of the underlying ERP signal. This method has been applied to subsets of real Ambulatory Electroencephalography (AEEG) data, recorded while participants performed a range of activities in different environments. From this analysis, the number of trials that need to be collected to observe the P300 amplitude and delay has been calculated for a range of scenarios

Parametric Modelling of EEG Data for the Identification of Mental Tasks

Electroencephalographic (EEG) data is widely used as a biosignal for the identification of different mental states in the human brain. EEG signals can be captured by relatively inexpensive equipment and acquisition procedures are non-invasive and not overly complicated. On the negative side, EEG signals are characterized by low signal-to-noise ratio and non-stationary characteristics, which makes the processing of such signals for the extraction of useful information a challenging task.peer-reviewe

Extraction and Detection of Fetal Electrocardiograms from Abdominal Recordings

The non-invasive fetal ECG (NIFECG), derived from abdominal surface electrodes, offers novel diagnostic possibilities for prenatal medicine. Despite its straightforward applicability, NIFECG signals are usually corrupted by many interfering sources. Most significantly, by the maternal ECG (MECG), whose amplitude usually exceeds that of the fetal ECG (FECG) by multiple times. The presence of additional noise sources (e.g. muscular/uterine noise, electrode motion, etc.) further affects the signal-to-noise ratio (SNR) of the FECG. These interfering sources, which typically show a strong non-stationary behavior, render the FECG extraction and fetal QRS (FQRS) detection demanding signal processing tasks. In this thesis, several of the challenges regarding NIFECG signal analysis were addressed. In order to improve NIFECG extraction, the dynamic model of a Kalman filter approach was extended, thus, providing a more adequate representation of the mixture of FECG, MECG, and noise. In addition, aiming at the FECG signal quality assessment, novel metrics were proposed and evaluated. Further, these quality metrics were applied in improving FQRS detection and fetal heart rate estimation based on an innovative evolutionary algorithm and Kalman filtering signal fusion, respectively. The elaborated methods were characterized in depth using both simulated and clinical data, produced throughout this thesis. To stress-test extraction algorithms under ideal circumstances, a comprehensive benchmark protocol was created and contributed to an extensively improved NIFECG simulation toolbox. The developed toolbox and a large simulated dataset were released under an open-source license, allowing researchers to compare results in a reproducible manner. Furthermore, to validate the developed approaches under more realistic and challenging situations, a clinical trial was performed in collaboration with the University Hospital of Leipzig. Aside from serving as a test set for the developed algorithms, the clinical trial enabled an exploratory research. This enables a better understanding about the pathophysiological variables and measurement setup configurations that lead to changes in the abdominal signal's SNR. With such broad scope, this dissertation addresses many of the current aspects of NIFECG analysis and provides future suggestions to establish NIFECG in clinical settings.:Abstract Acknowledgment Contents List of Figures List of Tables List of Abbreviations List of Symbols (1)Introduction 1.1)Background and Motivation 1.2)Aim of this Work 1.3)Dissertation Outline 1.4)Collaborators and Conflicts of Interest (2)Clinical Background 2.1)Physiology 2.1.1)Changes in the maternal circulatory system 2.1.2)Intrauterine structures and feto-maternal connection 2.1.3)Fetal growth and presentation 2.1.4)Fetal circulatory system 2.1.5)Fetal autonomic nervous system 2.1.6)Fetal heart activity and underlying factors 2.2)Pathology 2.2.1)Premature rupture of membrane 2.2.2)Intrauterine growth restriction 2.2.3)Fetal anemia 2.3)Interpretation of Fetal Heart Activity 2.3.1)Summary of clinical studies on FHR/FHRV 2.3.2)Summary of studies on heart conduction 2.4)Chapter Summary (3)Technical State of the Art 3.1)Prenatal Diagnostic and Measuring Technique 3.1.1)Fetal heart monitoring 3.1.2)Related metrics 3.2)Non-Invasive Fetal ECG Acquisition 3.2.1)Overview 3.2.2)Commercial equipment 3.2.3)Electrode configurations 3.2.4)Available NIFECG databases 3.2.5)Validity and usability of the non-invasive fetal ECG 3.3)Non-Invasive Fetal ECG Extraction Methods 3.3.1)Overview on the non-invasive fetal ECG extraction methods 3.3.2)Kalman filtering basics 3.3.3)Nonlinear Kalman filtering 3.3.4)Extended Kalman filter for FECG estimation 3.4)Fetal QRS Detection 3.4.1)Merging multichannel fetal QRS detections 3.4.2)Detection performance 3.5)Fetal Heart Rate Estimation 3.5.1)Preprocessing the fetal heart rate 3.5.2)Fetal heart rate statistics 3.6)Fetal ECG Morphological Analysis 3.7)Problem Description 3.8)Chapter Summary (4)Novel Approaches for Fetal ECG Analysis 4.1)Preliminary Considerations 4.2)Fetal ECG Extraction by means of Kalman Filtering 4.2.1)Optimized Gaussian approximation 4.2.2)Time-varying covariance matrices 4.2.3)Extended Kalman filter with unknown inputs 4.2.4)Filter calibration 4.3)Accurate Fetal QRS and Heart Rate Detection 4.3.1)Multichannel evolutionary QRS correction 4.3.2)Multichannel fetal heart rate estimation using Kalman filters 4.4)Chapter Summary (5)Data Material 5.1)Simulated Data 5.1.1)The FECG Synthetic Generator (FECGSYN) 5.1.2)The FECG Synthetic Database (FECGSYNDB) 5.2)Clinical Data 5.2.1)Clinical NIFECG recording 5.2.2)Scope and limitations of this study 5.2.3)Data annotation: signal quality and fetal amplitude 5.2.4)Data annotation: fetal QRS annotation 5.3)Chapter Summary (6)Results for Data Analysis 6.1)Simulated Data 6.1.1)Fetal QRS detection 6.1.2)Morphological analysis 6.2)Own Clinical Data 6.2.1)FQRS correction using the evolutionary algorithm 6.2.2)FHR correction by means of Kalman filtering (7)Discussion and Prospective 7.1)Data Availability 7.1.1)New measurement protocol 7.2)Signal Quality 7.3)Extraction Methods 7.4)FQRS and FHR Correction Algorithms (8)Conclusion References (A)Appendix A - Signal Quality Annotation (B)Appendix B - Fetal QRS Annotation (C)Appendix C - Data Recording GU

Motion Artifact Processing Techniques for Physiological Signals

The combination of reducing birth rate and increasing life expectancy continues to drive the demographic shift toward an ageing population and this is placing an ever-increasing burden on our healthcare systems. The urgent need to address this so called healthcare \time bomb" has led to a rapid growth in research into ubiquitous, pervasive and distributed healthcare technologies where recent advances in signal acquisition, data storage and communication are helping such systems become a reality. However, similar to recordings performed in the hospital environment, artifacts continue to be a major issue for these systems. The magnitude and frequency of artifacts can vary signicantly depending on the recording environment with one of the major contributions due to the motion of the subject or the recording transducer. As such, this thesis addresses the challenges of the removal of this motion artifact removal from various physiological signals. The preliminary investigations focus on artifact identication and the tagging of physiological signals streams with measures of signal quality. A new method for quantifying signal quality is developed based on the use of inexpensive accelerometers which facilitates the appropriate use of artifact processing methods as needed. These artifact processing methods are thoroughly examined as part of a comprehensive review of the most commonly applicable methods. This review forms the basis for the comparative studies subsequently presented. Then, a simple but novel experimental methodology for the comparison of artifact processing techniques is proposed, designed and tested for algorithm evaluation. The method is demonstrated to be highly eective for the type of artifact challenges common in a connected health setting, particularly those concerned with brain activity monitoring. This research primarily focuses on applying the techniques to functional near infrared spectroscopy (fNIRS) and electroencephalography (EEG) data due to their high susceptibility to contamination by subject motion related artifact. Using the novel experimental methodology, complemented with simulated data, a comprehensive comparison of a range of artifact processing methods is conducted, allowing the identication of the set of the best performing methods. A novel artifact removal technique is also developed, namely ensemble empirical mode decomposition with canonical correlation analysis (EEMD-CCA), which provides the best results when applied on fNIRS data under particular conditions. Four of the best performing techniques were then tested on real ambulatory EEG data contaminated with movement artifacts comparable to those observed during in-home monitoring. It was determined that when analysing EEG data, the Wiener lter is consistently the best performing artifact removal technique. However, when employing the fNIRS data, the best technique depends on a number of factors including: 1) the availability of a reference signal and 2) whether or not the form of the artifact is known. It is envisaged that the use of physiological signal monitoring for patient healthcare will grow signicantly over the next number of decades and it is hoped that this thesis will aid in the progression and development of artifact removal techniques capable of supporting this growth