Independent component approach to the analysis of EEG and MEG recordings

Multichannel recordings of the electromagnetic fields emerging from neural currents in the brain generate large amounts of data. Suitable feature extraction methods are, therefore, useful to facilitate the representation and interpretation of the data. Recently developed independent component analysis (ICA) has been shown to be an efficient tool for artifact identification and extraction from electroencephalographic (EEG) and magnetoen- cephalographic (MEG) recordings. In addition, ICA has been ap- plied to the analysis of brain signals evoked by sensory stimuli. This paper reviews our recent results in this field

Bioelectrical signal processing in cardiac and neurological applications and electromyography: physiology, engineering, and noninvasive applications

The present article reviews two recent books dealing with rather closely related subjects; in fact, they tend to complement and supplement reciprocally. Obviously, the electromyogram is a bioelectrical signal that often is mathematically manipulated in different ways to better extract its information. Moreover, its correlation with other bioelectric variables may become necessary

Recent Advances in Neural Recording Microsystems

The accelerating pace of research in neuroscience has created a considerable demand for neural interfacing microsystems capable of monitoring the activity of large groups of neurons. These emerging tools have revealed a tremendous potential for the advancement of knowledge in brain research and for the development of useful clinical applications. They can extract the relevant control signals directly from the brain enabling individuals with severe disabilities to communicate their intentions to other devices, like computers or various prostheses. Such microsystems are self-contained devices composed of a neural probe attached with an integrated circuit for extracting neural signals from multiple channels, and transferring the data outside the body. The greatest challenge facing development of such emerging devices into viable clinical systems involves addressing their small form factor and low-power consumption constraints, while providing superior resolution. In this paper, we survey the recent progress in the design and the implementation of multi-channel neural recording Microsystems, with particular emphasis on the design of recording and telemetry electronics. An overview of the numerous neural signal modalities is given and the existing microsystem topologies are covered. We present energy-efficient sensory circuits to retrieve weak signals from neural probes and we compare them. We cover data management and smart power scheduling approaches, and we review advances in low-power telemetry. Finally, we conclude by summarizing the remaining challenges and by highlighting the emerging trends in the field

Artifact Rejection Methodology Enables Continuous, Noninvasive Measurement of Gastric Myoelectric Activity in Ambulatory Subjects.

The increasing prevalence of functional and motility gastrointestinal (GI) disorders is at odds with bottlenecks in their diagnosis, treatment, and follow-up. Lack of noninvasive approaches means that only specialized centers can perform objective assessment procedures. Abnormal GI muscular activity, which is coordinated by electrical slow-waves, may play a key role in symptoms. As such, the electrogastrogram (EGG), a noninvasive means to continuously monitor gastric electrical activity, can be used to inform diagnoses over broader populations. However, it is seldom used due to technical issues: inconsistent results from single-channel measurements and signal artifacts that make interpretation difficult and limit prolonged monitoring. Here, we overcome these limitations with a wearable multi-channel system and artifact removal signal processing methods. Our approach yields an increase of 0.56 in the mean correlation coefficient between EGG and the clinical "gold standard", gastric manometry, across 11 subjects (p < 0.001). We also demonstrate this system's usage for ambulatory monitoring, which reveals myoelectric dynamics in response to meals akin to gastric emptying patterns and circadian-related oscillations. Our approach is noninvasive, easy to administer, and has promise to widen the scope of populations with GI disorders for which clinicians can screen patients, diagnose disorders, and refine treatments objectively

Blind Source Separation for the Processing of Contact-Less Biosignals

(Spatio-temporale) Blind Source Separation (BSS) eignet sich für die Verarbeitung von Multikanal-Messungen im Bereich der kontaktlosen Biosignalerfassung. Ziel der BSS ist dabei die Trennung von (z.B. kardialen) Nutzsignalen und Störsignalen typisch für die kontaktlosen Messtechniken. Das Potential der BSS kann praktisch nur ausgeschöpft werden, wenn (1) ein geeignetes BSS-Modell verwendet wird, welches der Komplexität der Multikanal-Messung gerecht wird und (2) die unbestimmte Permutation unter den BSS-Ausgangssignalen gelöst wird, d.h. das Nutzsignal praktisch automatisiert identifiziert werden kann. Die vorliegende Arbeit entwirft ein Framework, mit dessen Hilfe die Effizienz von BSS-Algorithmen im Kontext des kamera-basierten Photoplethysmogramms bewertet werden kann. Empfehlungen zur Auswahl bestimmter Algorithmen im Zusammenhang mit spezifischen Signal-Charakteristiken werden abgeleitet. Außerdem werden im Rahmen der Arbeit Konzepte für die automatisierte Kanalauswahl nach BSS im Bereich der kontaktlosen Messung des Elektrokardiogramms entwickelt und bewertet. Neuartige Algorithmen basierend auf Sparse Coding erwiesen sich dabei als besonders effizient im Vergleich zu Standard-Methoden.(Spatio-temporal) Blind Source Separation (BSS) provides a large potential to process distorted multichannel biosignal measurements in the context of novel contact-less recording techniques for separating distortions from the cardiac signal of interest. This potential can only be practically utilized (1) if a BSS model is applied that matches the complexity of the measurement, i.e. the signal mixture and (2) if permutation indeterminacy is solved among the BSS output components, i.e the component of interest can be practically selected. The present work, first, designs a framework to assess the efficacy of BSS algorithms in the context of the camera-based photoplethysmogram (cbPPG) and characterizes multiple BSS algorithms, accordingly. Algorithm selection recommendations for certain mixture characteristics are derived. Second, the present work develops and evaluates concepts to solve permutation indeterminacy for BSS outputs of contact-less electrocardiogram (ECG) recordings. The novel approach based on sparse coding is shown to outperform the existing concepts of higher order moments and frequency-domain features

Fetal electrocardiograms, direct and abdominal with reference heartbeat annotations

Monitoring fetal heart rate (FHR) variability plays a fundamental role in fetal state assessment. Reliable FHR signal can be obtained from an invasive direct fetal electrocardiogram (FECG), but this is limited to labour. Alternative abdominal (indirect) FECG signals can be recorded during pregnancy and labour. Quality, however, is much lower and the maternal heart and uterine contractions provide sources of interference. Here, we present ten twenty-minute pregnancy signals and 12 five-minute labour signals. Abdominal FECG and reference direct FECG were recorded simultaneously during labour. Reference pregnancy signal data came from an automated detector and were corrected by clinical experts. The resulting dataset exhibits a large variety of interferences and clinically significant FHR patterns. We thus provide the scientific community with access to bioelectrical fetal heart activity signals that may enable the development of new methods for FECG signals analysis, and may ultimately advance the use and accuracy of abdominal electrocardiography methods.Web of Science71art. no. 20

Versatile LCP surface microelectrodes for combining electrophysiology and in vivo two-photon imaging in the murine CNS

Neurons and astrocytes are highly interconnected and form a complex cellular network for signal processing in the brain. The electrical activity of neurons and astroglial Ca2+ signals are tightly coupled. Parallel recording of electrical activity and Ca2+ signals can help to identify the molecular mechanisms of neuron-glia communications. In this work, flexible liquid crystal polymer microelectrode arrays for electrical recordings and stimulations during two-photon laser-scanning microscopy (2P-LSM) were developed. The arrays were designed for standard craniotomies used for cortical 2P-LSM in vivo imaging. Being of low weight, thin and flexible, they can be easily positioned between the dura mater and the glass coverslip. Three different designs were constructed: arrays (1) with eight circular electrodes (arranged in a matrix of three by three elements, sparing the center), (2) with sixteen circular electrodes (four by four matrix) and (3) with eight rectangular electrodes (placed in four groups of 2 single sites). The initial contact sites of gold were coated with nanoporous platinum to decrease the impedance of the electrode tissue contacts and to increase the charge transfer capability. The biocompatibility of the electrodes was confirmed by immuno-histochemistry. Electrical recordings and Ca2+-imaging were performed in mice with neuronal or astroglial expression of the genetically encoded Ca2+-sensor GCaMP3. With the sixteen channel electrode arrays, an estimation of the spatially resolved electrical activity pattern within the cranial window could be described. The eight channel arrays were used in studies for simultaneous acquisition of Ca2+ (using 2P-LSM) and electrical signals. In addition, Ca2+ signals could be elicited by electrical stimulation. Using different stimulation intensities and depth of anesthesia, the change of brain activity during transition from anesthetized to awake state was investigated. In addition, the LCP technology was transferred from the cortical to a spinal cord application.Neurone und Astrozyten bilden ein komplexes interagierendes zellulares Netzwerk zur Signalverarbeitung im Gehirn. Dabei sind die elektrische Aktivitäten der Nervenzellen und die Ca2+ Signale der Astrozyten eng aneinander gekoppelt. Parallele Aufzeichnungen der elektrischen Aktivität und der Ca2+ Signale können helfen, die molekularen Mechanismen der Neuron-Glia-Kommunikation zu identifizieren. Innerhalb dieser Arbeit wurden flexible Flüssigkristall-Polymer-Mikroelektrodenarrays für elektrische Aufzeichnungen und Stimulationen für die Zwei-Photonen-Laserscan- Mikroskopie (2P-LSM) entwickelt. Die Elektrodenarrays wurden für Standard-Kraniotomien entwickelt, die für die kortikale in vivo 2P-LSM verwendet werden. Sie sind dünn, flexibel und von geringem Gewicht und können leicht auf der Dura positioniert werden. Drei verschiedene Designs wurden konstruiert: Arrays (1) mit acht runden Elektroden (angeordnet in einer drei mal drei Matrix, ohne die mittlere Elektrode), (2) mit sechzehn kreisförmigen Elektroden (vier mal vier Matrix) und (3) mit acht rechteckigen Elektroden (angeordnet in vier Gruppen von zwei einzelnen Standorten). Die ursprünglichen Elektrodenkontakte aus Gold wurden mit nanoporösem Platin beschichtet, um die Gewebekontaktimpedanz zu verringern und die Ladungsübertragungsfähigkeit zu erhöhen. Die Biokompatibilität der Elektroden immunhistochemisch getestet. Elektrische Aktivität und Ca2+ Signale wurden bei Mäusen mit neuronaler oder astroglialer Expression des Ca2+-Indikators GCaMP3 aufgezeichnet. Mit den sechzehn Kanal-Elektroden-Arrays konnten die elektrische Aktivität entlang der Kortexoberfläche innerhalb der Kraniotomie charakterisiert werden. Die achtkanaligen Arrays wurden zur gleichzeitigen Erfassung von Ca2+ (mit 2P-LSM) und elektrischen Signalen verwendet. Darüber hinaus konnten Ca2+ Signale durch elektrische Stimulation hervorgerufen werden. Mit verschiedenen Stimulationsintensitäten und der Tiefe der Anästhesie (Isofluran) wurde die Veränderung der Hirnaktivität beim Übergang von anästhesiert zu wach beobachtet. Zusätzlich konnte die Flüssigkristall-Polymer -Technologie von der kortikalen auf die spinale Anwendung übertragen werden.- European Union / EUGlia-PhD - European Union / Neurofibre

Biomedical signal filtering for noisy environments

 Luke\u27s work addresses issue of robustly attenuating multi-source noise from surface EEG signals using a novel Adaptive-Multiple-Reference Least-Means-Squares filter (AMR-LMS). In practice, the filter successfully removes electrical interference and muscle noise generated during movement which contaminates EEG, allowing subjects to maintain maximum mobility throughout signal acquisition and during the use of a Brain Computer Interface

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