Artifact Rejection Research Articles

Measuring Saccade Latency Using Smartphone Cameras.

Accurate quantification of neurodegenerative disease progression is an ongoing challenge that complicates efforts to understand and treat these conditions. Clinical studies have shown that eye movement features may serve as objective biomarkers to support diagnosis and tracking of disease progression. Here, we demonstrate that saccade latency-an eye movement measure of reaction time-can be measured robustly outside of the clinical environment with a smartphone camera. To enable tracking of saccade latency in large cohorts of patients and control subjects, we combined a deep convolutional neural network for gaze estimation with a model-based approach for saccade onset determination that provides automated signal-quality quantification and artifact rejection. Simultaneous recordings with a smartphone and a high-speed camera resulted in negligible differences in saccade latency distributions. Furthermore, we demonstrated that the constraint of chinrest support can be removed when recording healthy subjects. Repeat smartphone-based measurements of saccade latency in 11 self-reported healthy subjects resulted in an intraclass correlation coefficient of 0.76, showing our approach has good to excellent test-retest reliability. Additionally, we conducted more than 19 000 saccade latency measurements in 29 self-reported healthy subjects and observed significant intra- and inter-subject variability, which highlights the importance of individualized tracking. Lastly, we showed that with around 65 measurements we can estimate mean saccade latency to within less-than-10-ms precision, which takes within 4 min with our setup. By enabling repeat measurements of saccade latency and its distribution in individual subjects, our framework opens the possibility of quantifying patient state on a finer timescale in a broader population than previously possible.

Fuzzy Clustering for Exploratory Analysis of EEG Event-Related Potentials

We introduce an analysis method for electroencephalography (EEG) data, focused on event-related potentials (ERPs). Our approach is unsupervised and makes use of a fuzzy clustering algorithm based on the possibilistic framework and includes a data-driven noise and artifact rejection phase. Our contribution provides a general analysis tool, applicable to any ERP dataset, which can uncover the dataset's internal structure. The fuzzy clustering algorithm is the core of our method, since its fine-grained membership grades how much a sample belongs to a given cluster, making the method applicable even when groups have a certain overlap. Prior to the clustering step, we apply weights to the feature vectors, optimizing them in order to enhance the variance within the dataset, and we extract time-window-interval-based features inspired by interval arithmetic. We apply the data processing workflow to the analysis of a set of ERPs recorded during an emotional Go/NoGo task. We evaluate the performance of the unsupervised analysis by computing a measure based on the clusterization rate of trials in different experimental conditions. The results of the studied dataset show that the proposed method obtains a difference of clusterization rate of 69% in Go versus NoGo trials, when weights and interval features are applied to the data, improving previous work not including weights and interval features, which had a rate of 31%. Furthermore, when compared with the standard fuzzy c-means, our proposed possibilistic clustering algorithm outperforms it in terms of the clusterization rate. We also examine the effect of preprocessing the data with independent component analysis and removing noise-related components and observe that this does not improve significantly the obtained results. These findings demonstrate that our proposed method provides a valuable data processing workflow robust to EEG artifacts and able to produce a clustering that is coherent with the experimental conditions represented in the ERP dataset.

Intraoperative monitoring of olfactory function: a feasibility study.

Intraoperative neuromonitoring of the chemical senses (smell and taste) has never been performed. The objective of this study was to determine if olfactory-evoked potentials could be obtained intraoperatively under general anesthesia. A standard olfactometer was used in the surgical theater with hydrogen sulfide (4 ppm, 200 msec). Olfactory-evoked potentials were recorded in 8 patients who underwent neurosurgery for resection of cerebral lesions. These patients underwent routine target-controlled propofol and sufentanil general anesthesia. Frontal, temporal, and parietal scalp subdermal electrodes were recorded ipsilaterally and contralaterally at the site of the surgery. Evoked potentials were computed if at least 70 epochs (0.5-100 Hz) satisfying the artifact rejection criterion (threshold 45 μV) could be extracted from signals of electrodes. Contributive recordings were obtained for 5 of 8 patients (3 patients had fewer than 70 epochs with an amplitude < 45 μV). Olfactory-evoked potentials showed N1 responses (mean 442.8 ± 40.0 msec), most readily observed in the patient who underwent midline anterior fossa neurosurgery. No component of later latencies could be recorded consistently. The study confirms that olfactory-evoked potentials can be measured in response to olfactory stimuli under general anesthesia. This demonstrates the feasibility of recording olfactory function intraoperatively and opens the potential for neuromonitoring of olfactory function during neurosurgery.

Abstract 76: Motor and Sensory Control of Robotic Hands Through Fascicular Targeting

PURPOSE: 18-25,000 upper limb amputations occur in the US annually. We have developed fascicular targeting (FAST) as a surgical approach for implanting electrode interfaces within the individual component fascicular groups of the nerves in the residual limb of upper extremity amputees. Our hypothesis is that FAST, when combined with custom-developed technologies including longitudinal intrafascicular electrode (LIFE) interfaces, nerve stimulation and recording electronics, sensory stimulation patterns, and artificial intelligence (AI) motor decoding algorithms, will enable the restoration of naturalistic hand function in robotic hand protheses used by upper extremity amputees. METHODS: 5 upper extremity amputees were implanted with LIFE + cuff interfaces for durations ranging from 3 months up to 1 year. 3 subjects were partial-hand amputees with 2 FAST interfaces (2 ulnar nerve fascicles). 2 subjects were transradial amputees with 4 FAST interfaces (2 ulnar nerve and 2 median nerve fascicles). Weekly experimental sessions were performed: to assess stimulation parameters for sensory feedback, to record motor signals for robotic hand control, and to train and measure functional performance following restoration of naturalistic sensory feedback and motor control. Implants were removed at the completion of the trial. RESULTS: All implants were well-tolerated by subjects, with little to no functional morbidity caused by their participation in the trial. Sensory stimulation thresholds remained within a usable range for the duration of the trial. Stimulation patterns were used to elicit sensations perceived as either natural or non-natural, according to subjects’ preferences. Sensory discrimination and anatomic localization were also assessed. Motor signals were recorded using a novel microchip design with built-in artifact rejection circuitry. Acquisition of single-unit motor data permitted the use of AI-based signal decoding algorithms to establish independent, free-will control of all 5 digits of the robotic hand. Single-session decodes continued to work for over 3–4 weeks post-training. Functional performance using closed-loop sensorimotor control, with anatomically relevant sensory feedback and user controlled activation of individual digits of the robotic hand, will be discussed in detail. CONCLUSION: Fascicular targeting, when combined with specialized sensory stimulation and motor recording strategies, can enable true dexterity for upper extremity amputees using robotic hands. This holds promise for the development of full clinical systems to restore the hands of upper extremity amputees.

Assessment of Statistically Significant Command-Following in Pediatric Patients with Disorders of Consciousness, Based on Visual, Auditory and Tactile Event-Related Potentials

Disorders of consciousness (DOC) are among the major challenges of contemporary medicine, mostly due to the high rates of misdiagnoses in clinical assessment, based on behavioral scales. This turns our attention to potentially objective neuroimaging methods. Paradigms based on electroencephalography (EEG) are most suited for bedside applications, but sensitive to artifacts. These problems are especially pronounced in pediatric patients. We present the first study on the assessment of pediatric DOC patients by means of command-following procedures and involving long-latency cognitive event-related potentials. To deal with the above mentioned challenges, we construct a specialized signal processing scheme including artifact correction and rejection, parametrization, classification and final assessment of the statistical significance. To compensate for the possible bias of the tests involved in the final diagnosis, we propose the Monte Carlo evaluation of the processing pipeline. To compensate for possible sensory impairments of DOC patients, for each subject we check command-following responses to the stimuli in the major modalities: visual, tactile, and audio (words and sounds). We test the scheme on 20 healthy volunteers and present results for 15 patients from a hospital for children with severe brain damage, in relation to their behavioral diagnosis on the Coma Recovery Scale-Revised (CRS-R).

The Effect of Kalman-Weighted Averaging and Artifact Rejection on Residual Noise During Auditory Brainstem Response Testing.

Purpose Low residual noise (RN) levels are critically important when obtaining electrophysiological recordings of threshold auditory brainstem responses. In this study, we examine the effectiveness and efficiency of Kalman-weighted averaging (KWA) implemented on the Vivosonic Integrity System and artifact rejection (AR) implemented on the Intelligent Hearing Systems SmartEP system for obtaining low RN levels. Method Sixteen adults participated. Electrophysiological measures were obtained using simultaneous recordings by the Vivosonic and Intelligent Hearing Systems for subjects in 2 relaxed conditions and 4 active motor conditions. Three averaging times were used for the relaxed states (1, 1.5, and 3 min) and for the active states (1.5, 3, and 6 min). Repeated-measures analyses of variance were used to examine RN levels as a function of noise reduction strategy (i.e., KWA, AR) and averaging time. Results Lower RN levels were obtained using KWA than AR in both the relaxed and active motor states. Thus, KWA was more effective than was AR under the conditions examined in this study. Using KWA, approximately 3 min of averaging was needed in the relaxed condition to obtain an average RN level of 0.025 μV. In contrast, in the active motor conditions, approximately 6 min of averaging was required using KWA. Mean RN levels of 0.025 μV were not attained using AR. Conclusions When patients are not physiologically quiet, low RN levels are more likely to be obtained and more efficiently obtained using KWA than AR. However, even when using KWA, in active motor states, 6 min of averaging or more may be required to obtain threshold responses. Averaging time needed and whether a low RN level can be attained will depend on the level of motor activity exhibited by the patient.

A Modular, Smart, and Wearable System for High Density sEMG Detection.

The use of linear or bi-dimensional electrode arrays for surface EMG detection (HD-sEMG) is gaining attention as it increases the amount and reliability of information extracted from the surface EMG. However, the complexity of the setup and the encumbrance of HD-sEMG hardware currently limits its use in dynamic conditions. The aim of this paper was to develop a miniaturized, wireless, and modular HD-sEMG acquisition system for applications requiring high portability and robustness to movement artifacts. A system with modular architecture was designed. Its core is a miniaturized 32-channel amplifier (Sensor Unit - SU) sampling at 2048sps/ch with 16bit resolution and wirelessly transmitting data to a PC or a mobile device. Each SU is a node of a Body Sensor Network for the synchronous signal acquisition from different muscles. A prototype with two SUs was developed and tested. Each SU is small (3.4cm × 3cm × 1.5cm), light (16.7g), and can be connected directly to the electrodes; thus, avoiding the need for customary, wired setup. It allows to detect HD-sEMG signals with an average noise of 1.8μVRMS and high performance in terms of rejection of power-line interference and motion artefacts. Tests performed on two SUs showed no data loss in a 22m range and a ±500μs maximum synchronization delay. Data collected in a wide spectrum of experimental conditions confirmed the functionality of the designed architecture and the quality of the acquired signals. By simplifying the experimental setup, reducing the hardware encumbrance, and improving signal quality during dynamic contractions, the developed system opens new perspectives in the use of HD-sEMG in applied and clinical settings.

Automatic analysis of pre-ejection period during sleep using impedance cardiogram.

The pre-ejection period (PEP) is a valid index of myocardial contractility and beta-adrenergic sympathetic control of the heart defined as the time between electrical systole (ECG Q wave) to the initial opening of the aortic valve, estimated as the B point on the impedance cardiogram (ICG). B-point detection accuracy can be severely impacted if ICG cardiac cycles corrupted by motion artifact, noise, or electrode displacement are included in the analyses. Here, we developed new algorithms to detect and exclude corrupted ICG cycles by analyzing their level of activity. PEP was then estimated and analyzed on ensemble-averaged clean ICG cycles using an automatic algorithm previously developed by the authors for the detection of B point in awake individuals. We investigated the algorithms' performance relative to expert visual scoring on long-duration data collected from 20 participants during overnight recordings, where the quality of ICG could be highly affected by movement artifacts and electrode displacements and the signal could also vary according to sleep stage and time of night. The artifact rejection algorithm achieved a high accuracy of 87% in detection of expert-identified corrupted ICG cycles, including those with normal amplitude as well as out-of-range values, and was robust to different types and levels of artifact. Intraclass correlations for concurrent validity of the B-point detection algorithm in different sleep stages and in-bed wakefulness exceeded 0.98, indicating excellent agreement with the expert. The algorithms show promise toward sleep applications requiring accurate and reliable automatic measurement of cardiac hemodynamic parameters.

Development of a Closed-Loop Stimulator for Laryngeal Reanimation: Part 2. Device Testing in the Canine Model of Laryngeal Paralysis.

Laryngeal paralysis of central or peripheral origin can potentially be treated using functional electrical stimulation (FES) of laryngeal muscles. Experiments in canines (dogs) were performed using implant prototypes capable of closed-loop FES to refine engineering designs and specifications, test surgical approaches for implantation, and better understand the in vivo effects of laryngeal muscle stimulation on short- and long-term glottic function. Prospective, laboratory. We designed and tested a series of microprocessor-based implantable devices that can stimulate glottic opening or closing based on input from physiological control signals (real-time processing of electromyographic [EMG] signals). After acute device testing experiments, 2 dogs were implanted for 8 and 24 months, with periodic testing of closed-loop laryngeal muscle stimulation triggered from EMG signals. In total, 5 dogs were tested for the effects of laryngeal muscle stimulation on vocal fold (VF) posturing in larynges with nerve supplies that were intact (7 VFs), synkinetically reinnervated (2 VFs), or chronically denervated (1 VF). In 3 cases, the stimulation was combined with airflow-driven phonation to study the consequent modulation of phonatory parameters. Initial device prototypes used inductive coupling for power and communication, while later iterations used battery power and infrared light communication (detailed descriptions are provided in the Part 1 companion paper). Two animals were successfully implanted with the inductively powered units, which operated until removed at 8 months in 1 animal or for more than 16 months in the second animal. Surgically, the encapsulated implants were well tolerated, and procedures for placing, attaching, and connecting the devices were developed. To simulate EMG control signals in anesthetized animals, we created 2 types of nerve/muscle signal sources. In one approach, a neck muscle had a cuff electrode placed on its motor nerve that was connected to transdermal electrical connection ports for periodic testing. In the second approach, the recurrent laryngeal nerve on one side of the larynx was stimulated to generate a VF EMG signal, which was then used to trigger FES of the paralyzed contralateral side (eg, restoring VF movement symmetry). Implant testing identified effective stimulation parameters and closed-loop stimulation artifact rejection techniques for FES of both healthy and paralyzed VFs. Stimulation levels effective for VF adduction did not cause signs of discomfort during awake testing. Our inductive and battery-powered prototypes performed effectively during in vivo testing, and the 2 units that were implanted for long-term evaluation held up well. As a proof of concept, we demonstrated that elicited neck strap muscle or laryngeal EMG potentials could be used as a control signal for closed-loop stimulation of laryngeal adduction and vocal pitch modulation, depending on electrode positioning, and that VFs were stimulable in the presence of synkinetic reinnervation or chronic denervation.

M110 - ELECTROPHYSIOLOGIC DIFFERENCES TO ALCOHOL CHALLENGE IN INDIVIDUALS POLYMORPHIC AT ADH1B LOCI

Background The ADH Class 1 is the major group for metabolizing alcohol in humans. Subunits which constitute class 1 ADH are encoded by the three genes ADH1A, ADH1B, and ADH1C, giving rise to A, B, and C subunits. Genetic polymorphisms, occur at ADH1B to give rise to ADH1B1, ADH1B2, and ADH1B3 subunits. Concerning the ADH1B gene, individuals with the ADH1B3 genotype appear to be more resistant to the stimulating effects of alcohol, possibly due to a faster-metabolizing capacity of this variant compared to the wild-type (ADH1B1). The purpose of this study was to examine the effect of ADH1B polymorphisms on electroencephalography, using the alcohol clamp technique. We hypothesize that individuals heterozygous for the ADH1B3 allele will have lower alpha power in frontoparietal electrodes, suggesting less relaxation effect during alcohol ingestion compared to those homozygous for ADH1B1. Methods A pilot sample of individuals (N=28) genotyped at the ADH1B locus received continuous EEG recording during infusion, using the alcohol clamp technique. The collected EEG data was analyzed using EEGLAB, an open source electrophysiological data analysis software application. The data was preprocessed using FASTER, an EEGLAB plugin for Automated EEG artifact Rejection. The FASTER process applies independent component analysis and statistical thresholds for artifact rejection. The accepted EEG epochs were then submitted to the pwelch FFT implementation using the inbuilt functions of EEGLAB, which computed the power of the EEG in 0.24 Hz steps over a total range of 1.5 – 40 Hz. The power spectra were averaged over epochs and averaged amplitude peaks extracted. The estimates of power within the delta (1.5–3.2 Hz), theta (3.8– 7.02 Hz), alpha (7.8–12.5 Hz), and slow (13.2–18.75 Hz) and fast (19.5–39.8 Hz) beta frequency bands were computed as the area under the PSD curve. Results Subjects with ADH1B1/1 have increased alpha power in CZ, FZ and PZ leads during baseline (i.e. pre-alcohol exposure) eye-closed resting EEG, with the greatest difference (between ADH1B1/1 and ADH1B1/3 observed in the PZ leads (P Discussion Based on rich evidence from neurophysiological studies, increased alpha power is indicative of hypoarousal; therefore, our data subjects more evidence for cortical hypoarousal in anticipation of alcohol challenge among individuals with ADH1B1/1 polymorphism.

Protocol for Rapid, Accurate, Electrophysiologic, Auditory Assessment of Infants and Toddlers

Background: Audiologists often lack confidence in results produced by current protocols for diagnosticelectrophysiologic testing of infants. This leads to repeat testing appointments and slow protocols whichextend the time needed to complete the testing and consequently delay fitting of amplification. A recentpublication (Sininger et al, 2018) has shown how new technologies can be applied to electrophysiologictesting systems to improve confidence in results and allow faster test protocols. Average test times forcomplete audiogram predictions when using new technologies and protocols were found to be just over32 minutes using auditory brainstem response (ABR) and just under 20 minutes using auditory steadystateresponse (ASSR) technology.&lt;br /&gt;Purpose: The purpose of this manuscript is to provide details of expedited test protocols for infant andtoddler diagnostic electrophysiologic testing.&lt;br /&gt;Summary: Several new technologies and their role in test speed and confidence are described includingCE-Chirp stimuli, automated detection of ABRs using a technique called FMP, Bayesian weighting which isan alternative to standard artifact rejection and Next-Generation ASSR with improved response detectionand chirp stimuli. The test protocol has the following features: (1) preliminary testing includes impedancemeasures and otoacoustic emissions, (2) starting test levels are based on Broad-Band CE-Chirp thresholdsin each ear, (3) ABRs or ASSRs are considered present based on automated detection rather thanon replication of responses, (4) number of test levels is minimized, (5) ASSR generally evaluates fourfrequencies in each ear simultaneously with flexibility to change all test levels independently.&lt;br /&gt;Conclusions: Combining new technologies with common-sense strategies has been shown to substantiallyreduce test times for predicting audiometric thresholds in infants and toddlers (Sininger et al, 2018).Details and rationales for changing test strategies and protocols are given and case examples are used toillustrate.

Quantitative EEG After Subarachnoid Hemorrhage Predicts Long-Term Functional Outcome.

Delayed cerebral ischemia is a major complication after subarachnoid hemorrhage. Our previous study showed that alpha power reduction in continuous quantitative EEG predicts delayed cerebral ischemia. In this prospective cohort, we aimed to determine the prognostic value of alpha power in quantitative EEG for the long-term outcome of patients with subarachnoid hemorrhage. Adult patients with nontraumatic subarachnoid hemorrhage were included if admitted early enough for EEG to start within 72 hours after symptom onset. Continuous six-channel EEG was applied. Unselected EEG signals underwent automated artifact rejection, power spectral analysis, and detrending. Alpha power decline of ≥40% for ≥5 hours was defined as critical EEG event based on previous findings. Six-month outcome was obtained using the modified Rankin scale. Twenty-two patients were included (14 male; mean age, 59 years; Hunt and Hess grade I-IV; duration of EEG monitoring, median 14 days). Poor outcome (modified Rankin scale, 2-5) was noted in 11 of 16 patients (69%) with critical EEG events. All six patients (100%) without EEG events achieved an excellent outcome (modified Rankin scale 0, 1) (P = 0.0062; sensitivity 100%, specificity 54.5%). Vasospasm detected with transcranial Doppler/Duplex sonography appeared 1.5 days after EEG events and showed weaker association with outcome (P = 0.035; sensitivity 100%, specificity 45.5%). There was no significant association between EEG events and ischemic lesions on imaging (P = 0.1). Also, no association between ischemic lesions and outcome was seen (P = 0.64). Stable alpha power in quantitative EEG reflects successful therapy and predicts good functional outcome after subarachnoid hemorrhage. Critical alpha power reduction indicates an increased risk of poor functional outcome.

Artifact-free recordings in human bidirectional brain–computer interfaces

Objective. Intracortical microstimulation has shown promise as a means of evoking somatosensory percepts as part of a bidirectional brain–computer interface (BCI). However, microstimulation generates large electrical artifacts that dominate the recordings necessary for BCI control. These artifacts must be eliminated from the signal in real-time to allow for uninterrupted BCI decoding. Approach. We present a simple, robust modification to an existing clinical BCI system to allow for simultaneous recording and stimulation using a combination of signal blanking and digital filtering, without needing to explicitly account for varying parameters such as electrode locations or amplitudes. We validated our artifact rejection scheme by recording from microelectrodes in primary motor cortex (M1) while stimulating in somatosensory cortex of a person with a spinal cord injury. Main results. M1 recordings were digitally blanked using a sample-and-hold circuit triggered just prior to stimulus onset and a first-order 750 Hz high-pass Butterworth filter was used to reduce distortion of the remaining artifact. This scheme enabled spike detection in M1 to resume as soon as 740 µs after each stimulus pulse. We demonstrated the effectiveness of the complete bidirectional BCI system by comparing functional performance during a 5 degree of freedom robotic arm control task, with and without stimulation. When stimulation was delivered without this artifact rejection scheme, the number of objects the subject was able to move across a table in 2 min under BCI control declined significantly compared to trials without stimulation (p < 0.01). When artifact rejection was implemented, performance was no different than in trials that did not include stimulation (p = 0.621). Significance. The proposed technique uses simple changes in filtering and digital signal blanking with FDA-cleared hardware and enables artifact-free recordings during bidirectional BCI control.

Automated Detection of High Frequency Oscillations in Human Scalp Electroencephalogram.

High frequency oscillations (HFOs) > 80 Hz are a promising biomarker of epileptic tissue. Recent evidence has shown that spontaneous HFOs can be recorded from the scalp, but detection of these electrographic events remains a challenge. Here, we modified a simple automatic detector, used originally for intracranial EEG (iEEG) recordings, to detect ripples and fast ripples in scalp EEG. We analyzed scalp EEG recordings of seven subjects and validated our detector and artifact rejection algorithm via visual review. Of the candidate events marked by the detector, 40% and 60% were confirmed to be ripples and fast ripples, respectively, by human visual review, making this algorithm suitable for supervised detection. Detected HFOs occurred at a rate of <1/min in most channels, and the average duration was 47 and 24 ms for ripples and fast ripples, respectively. The simplicity of the algorithm, with only a single parameter, enables the consistent application of automatic detection across recording modalities, and it could therefore be a tool for the assessment and localization of epileptic activity.

Automatic Independent Component Scalp Map Analysis of Electroencephalogram During Motor Preparation.

This work presents a method for automatic independent component (IC) scalp map analysis of electroencephalogram during motor preparation in visuomotor tasks. The strength of this approach is the analysis of the IC scalp maps based on the apriori given mask. This uses an image processing approach, comparable to visual classification used by experts, to automate the selection of relevant ICs in visuomotor tasks. Thirty iterations of the Infomax ICA algorithm were used to test the reliability of the ICs. ICs above 95% quality index were used for IC scalp topography image analysis. Here, we used a linkage-clustering algorithm for IC clustering and gap statistic to estimate the number of clusters. After classifying the components with our approach, the labels were compared to those from well-known MARA ("Multiple Artifact Rejection Algorithm") - an open-source EEGLAB plug-in. It was found that 334 of the 568 labels were in-agreement. MARA labeled 81 out of the 177 source-related components, and 238 out of the 319 non-source-related components, as artifacts. Here, the strength of our approach lies in using an image-processing algorithm to identify the task-specific ICs whereas MARA focuses on the automatic classification of the artifactual ICs by combining stereotyped artifact-specific spatial and temporal features that depend on the electrode montage. After "artefactual" ICs are removed, task-specific ICs still remains to be identified from the remaining "good" ICs where our scalp topography image analysis approach can be applied. Our IC scalp topography image analysis is focused on task-specific IC selection based on an apriori mask, which is not limited to specific EEG features and/or electrode configurations for high-density EEG.

Assessment of tobacco smoke effects on neonatal cardiorespiratory control using a semi-automated processing approach.

A semi-automated processing approach was developed to assess the effects of early postnatal environmental tobacco smoke (ETS) on the cardiorespiratory control of newborn lambs. The system consists of several steps beginning with artifact rejection, followed by the selection of stationary segments, and ending with feature extraction. This approach was used in six lambs exposed to 20 cigarettes/day for the first 15days of life, while another six control lambs were exposed to room air. On postnatal day 16, electrocardiograph and respiratory signals were obtained from a 6-h polysomnographic recording. The effects of postnatal ETS exposure on heart rate variability, respiratory rate variability, and cardiorespiratory interrelations were explored. The unique results suggest that early postnatal ETS exposure increases respiratory rate variability and decreases the coupling between cardiac and respiratory systems. Potentially harmful consequences in early life include unstable breathing and decreased adaptability of cardiorespiratory function, particularly during early life challenges, such as prematurity or viral infection. Graphical abstract ᅟ.

S129. An open source platform for concurrent EEG/ECoG comparisons

Concurrent EEG/ECoG recordings are crucial in providing a gold standard to develop EEG models, especially using primate models. These could be crucial for selecting methods for determining valid brain connectivity measures. Two essential points are to eliminate the unavoidable artifacts that contaminate such types of recordings as well as to provide sufficiently detailed biophysical models, such as lead fields, for both EEG and ECoG data that take into consideration intracranial electrode materials. We present a Matlab open source platform for EEG/ECoG comparisons, based upon the Multidimensional Recording in Nagasaka et al. (2011) data set shared at http://neurotycho.org. EEG/ECoG registration, artifact rejection, and preprocessing routines are illustrated with data obtained from awake and anesthetized monkeys. This platform has already been used to produce the results described in Papadopuoulus et al. (2014) and Todaro et al. (2017). The platform uses BrainSuite2 for T1 MRI pre-processing (intensity bias correction and segmentation). The white/gray matter interface surface was chosen as the source space model for both EEG/ECoG. The specific head compartments considered were (1) brain (enclosed by the pial surface), (2) brain plus surrounding cerebrospinal fluid, (3) skull and (4) skin. Final manually correction was carried out by an expert. Our volume conduction model comprises compartments (2), (3) and (4). Lead Field Calculation: Tetrahedral meshes were created from the surfaces of the head model using Tetgen 2.0. Both EEG and ECoG lead fields were calculated by NeuroFEM using Finite Element Method from SimBio. Care was taken to model the plastic strips supporting ECoG electrodes. Model-Based Artifact Removal: The raw EEG data suffered from mainly 2 types of artifacts: Low frequency drifting, and Sparse spikes. We introduce the Transient Artifact Reduction Algorithm. We formulate the artifact removal process as minimizing the residual, and penalizing sparsity of both the trend and spikes. More details can be found in Selesnick et al. (2014). Time Frequency Analysis: Spectrogram (squared magnitude of the Short-Time Fourier Transform (STFT)) is useful in analyzing neurological signals, but with drawback of leakage effect. To overcome this, we use the multi-tapper method and the implementation from Chronux (Bokil et al., 2006) and we also calculate the cross-spectrum. Spectrogram: EEG and ECoG show slower oscillation in anesthesia state than the awake state (<5 Hz), but ECoG can detect obvious beta rhythm (13–30 Hz) in the awake state while EEG cannot; Cross-Spectrum: Averagely(0.3–45 Hz), EEG is stronger in the frontal area while ECoG is stronger in the occipital and temporal areas in both awake and anesthesia states. The proposed open source platform and allows co-registration, artifact removal and biophysical modeling of use in both sensor and source level analyses. We believe that this platform will be helpful for EEG/ECoG multimodal comparisons and integration.

An automatic pre-processing pipeline for EEG analysis (APP) based on robust statistics

ObjectiveWith the advent of high-density EEG and studies of large numbers of participants, yielding increasingly greater amounts of data, supervised methods for artifact rejection have become excessively time consuming. Here, we propose a novel automatic pipeline (APP) for pre-processing and artifact rejection of EEG data, which innovates relative to existing methods by not only following state-of-the-art guidelines but also further employing robust statistics. MethodsAPP was tested on event-related potential (ERP) data from healthy participants and schizophrenia patients, and resting-state (RS) data from healthy participants. Its performance was compared with that of existing automatic methods (FASTER for ERP data, TAPEEG and Prep pipeline for RS data) and supervised pre-processing by experts. ResultsAPP rejected fewer bad channels and bad epochs than the other methods. In the ERP study, it produced significantly higher amplitudes than FASTER, which were consistent with the supervised scheme. In the RS study, it produced spectral measures that correlated well with the automatic alternatives and the supervised scheme. ConclusionAPP effectively removed EEG artifacts, performing similarly to the supervised scheme and outperforming existing automatic alternatives. SignificanceThe proposed automatic pipeline provides a reliable and efficient tool for pre-processing large datasets of both evoked and resting-state EEG.

A Nonlinear Beamformer Based on p-th Root Compression—Application to Plane Wave Ultrasound Imaging

Ultrafast medical ultrasound imaging is necessary for 3D and 4D ultrasound imaging, and it can also achieve high temporal resolution (thousands of frames per second) for monitoring of transient biological phenomena. However, reaching such frame rates involves reduction of image quality compared with that obtained with conventional ultrasound imaging, since the latter requires each image line to be reconstructed separately with a thin ultrasonic focused beam. There are many techniques to simultaneously acquire several image lines, although at the expense of resolution and contrast, due to interference from echoes from the whole medium. In this paper, a nonlinear beamformer is applied to plane wave imaging to improve resolution and contrast of ultrasound images. The method consists of the introduction of nonlinear operations in the conventional delay-and-sum (DAS) beamforming algorithm. To recover the value of each pixel, the raw radiofrequency signals are first dynamically focused and summed on the plane wave dimension. Then, their amplitudes are compressed using the signed p t h root. After summing on the element dimension, the signed p-power is applied to restore the original dimensionality in volts. Finally, a band-pass filter is used to remove artificial harmonics introduced by these nonlinear operations. The proposed method is referred to as p-DAS, and it has been tested here on numerical and experimental data from the open access platform of the Plane wave Imaging Challenge in Medical UltraSound (PICMUS). This study demonstrates that p-DAS achieves better resolution and artifact rejection than the conventional DAS (for p = 2 with eleven plane wave imaging on experimental phantoms, the lateral resolution is improved by 21 % , and contrast ratio (CR) by 59 % ). However, like many coherence-based beamformers, it tends to distort the conventional speckle structure (contrast-to-noise-ratio (CNR) decreased by 45 % ). It is demonstrated that p-DAS, for p = 2 , is very similar to the nonlinear filtered-delay-multiply-and-sum (FDMAS) beamforming, but also that its impact on image quality can be tuned changing the value of p.

Multi Line Transmit Beamforming Combined With Adaptive Apodization.

Increased frame rate is of high importance to cardiac diagnostic imaging as it enables examination of fast events during the cardiac cycle and improved quantitative analysis, such as speckle tracking. Multi-line transmission (MLT) is one of the methods proposed for this purpose. In contrast to the single-line transmission (SLT), where one focused beam is sent in each direction, MLT beams are simultaneously transmitted and focused in several (2,4,6..) directions improving the framerate accordingly. The simultaneous transmission is known to cause cross-talk artifacts due to the interference between the main-lobes and the side-lobes of the transmitted and received beams. Usually, the artifacts are attenuated using a Tukey window apodization, but the lateral resolution is degraded. Several other methods, such as minimum variance beamforming and filtered delay multiply and sum beamforming were proposed to deal with these artifacts.The assumption examined in this study is that a receive apodization can be chosen adaptively from a number of apodization windows in order to provide better artifact rejection and to increase the spatial resolution. The entire study was performed on experimental MLT dataset including wire and tissue mimicking phantoms, as well as in vivo cardiac data. The results demonstrate that application of a predefined apodization bank outperforms Tukey windowing alone, in terms of both resolution and receive crosstalk artifact rejection. Moreover, the achieved spatial resolution is superior to the non-apodized SLT, as measured from wire phantoms. The proposed method can also be combined with wider transmit beams, suitable for multi line acquisition.