A programmable reaction-diffusion system for spatiotemporal cell signaling circuit design.

Cells self-organize molecules in space and time to generate complex behaviors, but we lack synthetic strategies for engineering spatiotemporal signaling. We present a programmable reaction-diffusion platform for designing protein oscillations, patterns, and circuits in mammalian cells using two bacterial proteins, MinD and MinE (MinDE). MinDE circuits act like "single-cell radios," emitting frequency-barcoded fluorescence signals that can be spectrally isolated and analyzed using digital signal processing tools. We define how to genetically program these signals and connect their spatiotemporal dynamics to cell biology using engineerable protein-protein interactions. This enabled us to construct sensitive reporter circuits that broadcast endogenous cell signaling dynamics on a frequency-barcoded imaging channel and to build control signal circuits that synthetically pattern activities in the cell, such as protein condensate assembly and actin filamentation. Our work establishes a paradigm for visualizing, probing, and engineering cellular activities at length and timescales critical for biological function.

Early T cell activation: integrating biochemical, structural, and biophysical cues.

T cells carry out the formidable task of identifying small numbers of foreign antigenic peptides rapidly and specifically against a very noisy environmental background of endogenous self-peptides. Early steps in T cell activation have thus fascinated biologists and are among the best-studied models of cell stimulation. This remarkable process, critical in adaptive immune responses, approaches and even seems to exceed the limitations set by the physical laws ruling molecular behavior. Despite the enormous amount of information concerning the nature of molecules involved in the T cell antigen receptor (TCR) signal transduction network, and the description of the nanoscale organization and real-time analysis of T cell responses, the general principles of information gathering and processing remain incompletely understood. Here we review currently accepted key data on TCR function, discuss the limitations of current research strategies, and suggest a novel model of TCR triggering and a few promising ways of going further into the integration of available data.

Combinatorial Signal Perception in the BMP Pathway.

The bone morphogenetic protein (BMP) signaling pathway comprises multiple ligands and receptors that interact promiscuously with one another and typically appear in combinations. This feature is often explained in terms of redundancy and regulatory flexibility, but it has remained unclear what signal-processing capabilities it provides. Here, we show that the BMP pathway processes multi-ligand inputs using a specific repertoire of computations, including ratiometric sensing, balance detection, and imbalance detection. These computations operate on the relative levels of different ligands and can arise directly from competitive receptor-ligand interactions. Furthermore, cells can select different computations to perform on the same ligand combination through expression of alternative sets of receptor variants. These results provide a direct signal-processing role for promiscuous receptor-ligand interactions and establish operational principles for quantitatively controlling cells with BMP ligands. Similar principles could apply to other promiscuous signaling pathways.

Tracing Information Flow from Erk to Target Gene Induction Reveals Mechanisms of Dynamic and Combinatorial Control.

Cell signaling networks coordinate specific patterns of protein expression in response to external cues, yet the logic by which signaling pathway activity determines the eventual abundance of target proteins is complex and poorly understood. Here, we describe an approach for simultaneously controlling the Ras/Erk pathway and monitoring a target gene's transcription and protein accumulation in single live cells. We apply our approach to dissect how Erk activity is decoded by immediate early genes (IEGs). We find that IEG transcription decodes Erk dynamics through a shared band-pass filtering circuit; repeated Erk pulses transcribe IEGs more efficiently than sustained Erk inputs. However, despite highly similar transcriptional responses, each IEG exhibits dramatically different protein-level accumulation, demonstrating a high degree of post-transcriptional regulation by combinations of multiple pathways. Our results demonstrate that the Ras/Erk pathway is decoded by both dynamic filters and logic gates to shape target gene responses in a context-specific manner.

CoverageMaster: comprehensive CNV detection and visualization from NGS short reads for genetic medicine applications.

CoverageMaster (CoM) is a copy number variation (CNV) calling algorithm based on depth-of-coverage maps designed to detect CNVs of any size in exome [whole exome sequencing (WES)] and genome [whole genome sequencing (WGS)] data. The core of the algorithm is the compression of sequencing coverage data in a multiscale Wavelet space and the analysis through an iterative Hidden Markov Model. CoM processes WES and WGS data at nucleotide scale resolution and accurately detects and visualizes full size range CNVs, including single or partial exon deletions and duplications. The results obtained with this approach support the possibility for coverage-based CNV callers to replace probe-based methods such as array comparative genomic hybridization and multiplex ligation-dependent probe amplification in the near future.

A real-time signal processing software package for the electrophysiology laboratory.

BACKGROUND: Real-time signal processing has to date been difficult to implement in the clinical electrophysiology laboratory. To date, no open access software solutions are available in electrophysiology (EP) laboratories to facilitate real-time intraprocedural signal analysis. We aimed to develop an open access, scalable Python plug-in to allow real-time signal processing during human EP procedures. METHODS AND RESULTS: A Python-based plug in for the widely available EnsiteX mapping system was developed. This plug-in utilized the LiveSync feature of the system to allow real-time signal analysis. An open access library was developed to allow end-users to implement real-time signal analysis using this platform, implemented in the Python programming language https://github.com/anand9176/WaveWatch5000Public. CONCLUSION: We have developed and demonstrated the feasibility of a readily scalable and open-access Python-based plug in to an electroanatomic mapping system (EnSiteX) to allow real-time processing and display of electrogram (EGM) based information for the procedural electrophysiologist to view intraprocedurally in the electrophysiology laboratory. The availability, to the clinician, of traditional and novel EGM-based metrics at the time of intervention, such as atrial fibrillation ablation, allows for key mechanistic insights into critical unresolved questions regarding arrhythmia mechanism.

Peak frequency mapping of atypical atrial flutter.

INTRODUCTION: Peak frequency (PF) mapping is a novel method that may identify critical portions of myocardial substrate supporting reentry. The aim of this study was to describe and evaluate PF mapping combined with omnipolar voltage mapping in the identification of critical isthmuses of left atrial (LA) atypical flutters. METHODS AND RESULTS: LA omnipolar voltage and PF maps were generated in flutter using the Advisor HD-Grid catheter (Abbott) and EnSite Precision Mapping System (Abbott) in 12 patients. Normal voltage was defined as ≥0.5 mV, low-voltage as 0.1-0.5 mV, and scar as <0.1 mV. PF distributions were compared with ANOVA and post hoc Tukey analyses. The 1 cm radius from arrhythmia termination was compared to global myocardium with unpaired t-testing. The mean age was 65.8 ± 9.7 years and 50% of patients were female. Overall, 34 312 points were analyzed. Atypical flutters most frequently involved the mitral isthmus (58%) or anterior wall (25%). Mean PF varied significantly by myocardial voltage: normal (335.5 ± 115.0 Hz), low (274.6 ± 144.0 Hz), and scar (71.6 ± 140.5 Hz) (p < .0001 for all pairwise comparisons). All termination sites resided in low-voltage regions containing intermediate or high PF. Overall, mean voltage in the 1 cm radius from termination was significantly lower than the remaining myocardium (0.58 vs. 0.95 mV, p < .0001) and PF was significantly higher (326.4 vs. 245.1 Hz, p < .0001). CONCLUSION: Low-voltage, high-PF areas may be critical targets during catheter ablation of atypical atrial flutter.

Nonictal electroencephalographic measures for the diagnosis of functional seizures.

OBJECTIVE: Functional seizures (FS) look like epileptic seizures but are characterized by a lack of epileptic activity in the brain. Approximately one in five referrals to epilepsy clinics are diagnosed with this condition. FS are diagnosed by recording a seizure using video-electroencephalography (EEG), from which an expert inspects the semiology and the EEG. However, this method can be expensive and inaccessible and can present significant patient burden. No single biomarker has been found to diagnose FS. However, the current limitations in FS diagnosis could be improved with machine learning to classify signal features extracted from EEG, thus providing a potentially very useful aid to clinicians. METHODS: The current study has investigated the use of seizure-free EEG signals with machine learning to identify subjects with FS from those with epilepsy. The dataset included interictal and preictal EEG recordings from 48 subjects with FS (mean age = 34.76 ± 10.55 years, 14 males) and 29 subjects with epilepsy (mean age = 38.95 ± 13.93 years, 18 males) from which various statistical, temporal, and spectral features from the five EEG frequency bands were extracted then analyzed with threshold accuracy, five machine learning classifiers, and two feature importance approaches. RESULTS: The highest classification accuracy reported from thresholding was 60.67%. However, the temporal features were the best performing, with the highest balanced accuracy reported by the machine learning models: 95.71% with all frequency bands combined and a support vector machine classifier. SIGNIFICANCE: Machine learning was much more effective than using individual features and could be a powerful aid in FS diagnosis. Furthermore, combining the frequency bands improved the accuracy of the classifiers in most cases, and the lowest performing EEG bands were consistently delta and gamma.

Detection of focal to bilateral tonic-clonic seizures using a connected shirt.

OBJECTIVE: This study was undertaken to develop and evaluate a machine learning-based algorithm for the detection of focal to bilateral tonic-clonic seizures (FBTCS) using a novel multimodal connected shirt. METHODS: We prospectively recruited patients with epilepsy admitted to our epilepsy monitoring unit and asked them to wear the connected shirt while under simultaneous video-electroencephalographic monitoring. Electrocardiographic (ECG) and accelerometric (ACC) signals recorded with the connected shirt were used for the development of the seizure detection algorithm. First, we used a sliding window to extract linear and nonlinear features from both ECG and ACC signals. Then, we trained an extreme gradient boosting algorithm (XGBoost) to detect FBTCS according to seizure onset and offset annotated by three board-certified epileptologists. Finally, we applied a postprocessing step to regularize the classification output. A patientwise nested cross-validation was implemented to evaluate the performances in terms of sensitivity, false alarm rate (FAR), time in false warning (TiW), detection latency, and receiver operating characteristic area under the curve (ROC-AUC). RESULTS: We recorded 66 FBTCS from 42 patients who wore the connected shirt for a total of 8067 continuous hours. The XGBoost algorithm reached a sensitivity of 84.8% (56/66 seizures), with a median FAR of .55/24 h and a median TiW of 10 s/alarm. ROC-AUC was .90 (95% confidence interval = .88-.91). Median detection latency from the time of progression to the bilateral tonic-clonic phase was 25.5 s. SIGNIFICANCE: The novel connected shirt allowed accurate detection of FBTCS with a low false alarm rate in a hospital setting. Prospective studies in a residential setting with a real-time and online seizure detection algorithm are required to validate the performance and usability of this device.

A sleep stage estimation algorithm based on cardiorespiratory signals derived from a suprasternal pressure sensor.

Automatic estimation of sleep structure is an important aspect in moving sleep monitoring from clinical laboratories to people's homes. However, the transition to more portable systems should not happen at the expense of important physiological signals, such as respiration. Here, we propose the use of cardiorespiratory signals obtained by a suprasternal pressure (SSP) sensor to estimate sleep stages. The sensor is already used for diagnosis of sleep-disordered breathing (SDB) conditions, but besides respiratory effort it can detect cardiac vibrations transmitted through the trachea. We collected the SSP sensor signal in 100 adults (57 male) undergoing clinical polysomnography for suspected sleep disorders, including sleep apnea syndrome, insomnia, and movement disorders. Here, we separate respiratory effort and cardiac activity related signals, then input these into a neural network trained to estimate sleep stages. Using the original mixed signal the results show a moderate agreement with manual scoring, with a Cohen's kappa of 0.53 in Wake/N1-N2/N3/rapid eye movement sleep discrimination and 0.62 in Wake/Sleep. We demonstrate that decoupling the two signals and using the cardiac signal to estimate the instantaneous heart rate improves the process considerably, reaching an agreement of 0.63 and 0.71. Our proposed method achieves high accuracy, specificity, and sensitivity across different sleep staging tasks. We also compare the total sleep time calculated with our method against manual scoring, with an average error of -1.83 min but a relatively large confidence interval of ±55 min. Compact systems that employ the SSP sensor information-rich signal may enable new ways of clinical assessments, such as night-to-night variability in obstructive sleep apnea and other sleep disorders.

Blind Polarization Demultiplexing of Shaped QAM Signals Assisted by Temporal Correlations.

While probabilistic constellation shaping (PCS) enables rate and reach adaption with finer granularity [1], it imposes signal processing challenges at the receiver. Since the distribution of PCS-quadrature amplitude modulation (QAM) signals tends to be Gaussian, conventional blind polarization demultiplexing algorithms are not suitable for them [2]. It is known that independently and identically distributed (iid) Gaussian signals, when mixed, cannot be recovered/separated from their mixture. For PCS-QAM signals, there are algorithms such as [3], [4] which are designed by extending conventional blind algorithms used for uniform QAM signals. In these algorithms, an initialization point is obtained by processing only a part of the mixed signal, which have non-Gaussian statistics. In this paper, we propose an alternative method wherein we add temporal correlations at the transmitter, which are subsequently exploited at the receiver in order to separate the polarizations. We will refer to the proposed method as frequency domain (FD) joint diagonalization (JD) probability aware-multi modulus algorithm (pr-MMA), and it is suited to channels with moderate polarization mode dispersion (PMD) effects. Furthermore, we extend our previously proposed JD-MMA [5] by replacing the standard MMA with a pr-MMA, improving its performance. Both FDJD-pr-MMA and JD-pr-MMA are evaluated for a diverse range of PCS (entropy 𝓗) over a first-order PMD channel that is simulated in a proof-of-concept setup. A MMA initialized with a memoryless constant modulus algorithm (CMA) is used as a benchmark. We show that at a differential group delay (DGD) of 10% of symbol period Tsymb and 18 dB SNR/pol., JD-pr-MMA successfully demultiplexes the PCS signals, while CMA-MMA fails drastically. Furthermore, we demonstrate that the newly proposed FDJD-pr-MMA is robust against moderate PMD effects by evaluating it over a DGD of up to 40% of Tsymb. Our results show that the proposed FDJD-pr-MMA successfully equalizes PMD channels with a DGD up to 20% of Tsymb.

Terahertz communication: detection and signal processing.

The development of 6 G networks has promoted related research based on terahertz communication. As submillimeter radiation, signal transportation via terahertz waves has several superior properties, including non-ionizing and easy penetration of non-metallic materials. This paper provides an overview of different terahertz detectors based on various mechanisms. Additionally, the detailed fabrication process, structural design, and the improvement strategies are summarized. Following that, it is essential and necessary to prevent the practical signal from noise, and methods such as wavelet transform, UM-MIMO and decoding have been introduced. This paper highlights the detection process of the terahertz wave system and signal processing after the collection of signal data.

Microstate Analysis Reflects Maturation of the Preterm Brain.

Preterm neonates are at risk of long-term neurodevelopmental impairments due to disruption of natural brain development. Electroencephalography (EEG) analysis can provide insights into brain development of preterm neonates. This study aims to explore the use of microstate (MS) analysis to evaluate global brain dynamics changes during maturation in preterm neonates with normal neurodevelopmental outcome.The dataset included 135 EEGs obtained from 48 neonates at varying postmenstrual ages (26.4 to 47.7 weeks), divided into four age groups. For each recording we extracted a 5-minute epoch during quiet sleep (QS) and during non-quiet sleep (NQS), resulting in eight groups (4 age group x 2 sleep states). We compared MS maps and corresponding (map-specific) MS metrics across groups using group-level maps. Additionally, we investigated individual map metrics.Four group-level MS maps accounted for approximately 70% of the global variance and showed non-random syntax. MS topographies and transitions changed significantly when neonates reached 37 weeks. For both sleep states and all MS maps, MS duration decreased and occurrence increased with age. The same relationships were found using individual maps, showing strong correlations (Pearson coefficients up to 0.74) between individual map metrics and post-menstrual age. Moreover, the Hurst exponent of the individual MS sequence decreased with age.The observed changes in MS metrics with age might reflect the development of the preterm brain, which is characterized by formation of neural networks. Therefore, MS analysis is a promising tool for monitoring preterm neonatal brain maturation, while our study can serve as a valuable reference for investigating EEGs of neonates with abnormal neurodevelopmental outcomes.

Measures of overnight oxygen saturation to characterize sleep apnea severity and predict postoperative respiratory depression.

BACKGROUND: Sleep apnea syndrome, characterized by recurrent cessation (apnea) or reduction (hypopnea) of breathing during sleep, is a major risk factor for postoperative respiratory depression. Challenges in sleep apnea assessment have led to the proposal of alternative metrics derived from oxyhemoglobin saturation (SpO2), such as oxygen desaturation index (ODI) and percentage of cumulative sleep time spent with SpO2 below 90% (CT90), as predictors of postoperative respiratory depression. However, their performance has been limited with area under the curve of 0.60 for ODI and 0.59 for CT90. Our objective was to propose novel features from preoperative overnight SpO2 which are correlated with sleep apnea severity and predictive of postoperative respiratory depression. METHODS: Preoperative SpO2 signals from 235 surgical patients were retrospectively analyzed to derive seven features to characterize the sleep apnea severity. The features included entropy and standard deviation of SpO2 signal; below average burden characterizing the area under the average SpO2; average, standard deviation, and entropy of desaturation burdens; and overall nocturnal desaturation burden. The association between the extracted features and sleep apnea severity was assessed using Pearson correlation analysis. Logistic regression was employed to evaluate the predictive performance of the features in identifying postoperative respiratory depression. RESULTS: Our findings indicated a similar performance of the proposed features to the conventional apnea-hypopnea index (AHI) for assessing sleep apnea severity, with average area under the curve ranging from 0.77 to 0.81. Notably, entropy and standard deviation of overnight SpO2 signal and below average burden showed comparable predictive capability to AHI but with minimal computational requirements and individuals' burden, making them promising for screening purposes. Our sex-based analysis revealed that compared to entropy and standard deviation, below average burden exhibited higher sensitivity in detecting respiratory depression in women than men. CONCLUSION: This study underscores the potential of preoperative SpO2 features as alternative metrics to AHI in predicting postoperative respiratory.

Portable multi-focal visual evoked potential diagnostics for multiple sclerosis/optic neuritis patients.

PURPOSE: Multiple sclerosis (MS) is a neuro-inflammatory disease affecting the central nervous system (CNS), where the immune system targets and damages the protective myelin sheath surrounding nerve fibers, inhibiting axonal signal transmission. Demyelinating optic neuritis (ON), a common MS symptom, involves optic nerve damage. We've developed NeuroVEP, a portable, wireless diagnostic system that delivers visual stimuli through a smartphone in a headset and measures evoked potentials at the visual cortex from the scalp using custom electroencephalography electrodes. METHODS: Subject vision is evaluated using a short 2.5-min full-field visual evoked potentials (ffVEP) test, followed by a 12.5-min multifocal VEP (mfVEP) test. The ffVEP evaluates the integrity of the visual pathway by analyzing the P100 component from each eye, while the mfVEP evaluates 36 individual regions of the visual field for abnormalities. Extensive signal processing, feature extraction methods, and machine learning algorithms were explored for analyzing the mfVEPs. Key metrics from patients' ffVEP results were statistically evaluated against data collected from a group of subjects with normal vision. Custom visual stimuli with simulated defects were used to validate the mfVEP results which yielded 91% accuracy of classification. RESULTS: 20 subjects, 10 controls and 10 with MS and/or ON were tested with the NeuroVEP device and a standard-of-care (SOC) VEP testing device which delivers only ffVEP stimuli. In 91% of the cases, the ffVEP results agreed between NeuroVEP and SOC device. Where available, the NeuroVEP mfVEP results were in good agreement with Humphrey Automated Perimetry visual field analysis. The lesion locations deduced from the mfVEP data were consistent with Magnetic Resonance Imaging and Optical Coherence Tomography findings. CONCLUSION: This pilot study indicates that NeuroVEP has the potential to be a reliable, portable, and objective diagnostic device for electrophysiology and visual field analysis for neuro-visual disorders.

Neural network interpretation using descrambler groups.

The lack of interpretability and trust is a much-criticized feature of deep neural networks. In fully connected nets, the signaling between inner layers is scrambled because backpropagation training does not require perceptrons to be arranged in any particular order. The result is a black box; this problem is particularly severe in scientific computing and digital signal processing (DSP), where neural nets perform abstract mathematical transformations that do not reduce to features or concepts. We present here a group-theoretical procedure that attempts to bring inner-layer signaling into a human-readable form, the assumption being that this form exists and has identifiable and quantifiable features-for example, smoothness or locality. We applied the proposed method to DEERNet (a DSP network used in electron spin resonance) and managed to descramble it. We found considerable internal sophistication: the network spontaneously invents a bandpass filter, a notch filter, a frequency axis rescaling transformation, frequency-division multiplexing, group embedding, spectral filtering regularization, and a map from harmonic functions into Chebyshev polynomials-in 10 min of unattended training from a random initial guess.

Enhancing ECG readability in LVAD patients: A comparative analysis of Denoising techniques with an emphasis on discrete wavelet transform.

BACKGROUND: Electrocardiograms (ECGs) are vital for diagnosing cardiac conditions but obtaining clean signals in Left Ventricular Assist Device (LVAD) patients is hindered by electromagnetic interference (EMI). Traditional filters have limited efficacy. There is a current need for an easy and effective method. METHODS: Raw ECG data obtained from 5 patients with LVADs. LVAD types included HeartMate II, III at multiple impeller speeds, and a case with HeartMate III and a ProtekDuo. ECG spectral profiles were examined ensuring the presence of diverse types of EMI in the study. ECGs were then processed with four denoising techniques: Moving Average Filter, Finite Impulse Response Filter, Fast Fourier Transform, and Discrete Wavelet Transform. RESULTS: Discrete Wavelet Transform proved as the most promising method. It offered a one solution fits all, enabling automatic processing with minimal user input while preserving crucial high-frequency components and reducing LVAD EMI artifacts. CONCLUSION: Our study demonstrates the practicality and efficiency of Discrete Wavelet Transform in obtaining high-fidelity ECGs in LVAD patients. This method could enhance clinical diagnosis and monitoring.

Exploring a new frontier in cardiac diagnosis: ECG analysis enhanced by machine learning and parametric quartic spline modeling.

The heart's study holds paramount importance in human physiology, driving valuable research in cardiovascular health. However, assessing Electrocardiogram (ECG) analysis techniques poses challenges due to noise and artifacts in authentic recordings. The advent of machine learning systems for automated diagnosis has heightened the demand for extensive data, yet accessing medical data is hindered by privacy concerns. Consequently, generating artificial ECG signals faithful to real ones is a formidable task in biomedical signal processing. This paper introduces a method for ECG signal modeling using parametric quartic splines and generating a new dataset based on the modeled signals. Additionally, it explores ECG classification using three machine learning techniques facilitated by Orange software, addressing both normal and abnormal sinus rhythms. The classification enables early detection and prediction of heart-related ailments, facilitating timely clinical interventions and improving patient outcomes. The assessment of synthetic signal quality is conducted through power spectrum analysis and cross-correlation analysis, power spectrum analysis of both real and synthetic ECG waves provides a quantitative assessment of their frequency content, aiding in the validation and evaluation of synthetic ECG signal generation techniques. Cross-correlation analysis revealing a robust correlation coefficient of 0.974 and precise alignment with a negligible time lag of 0.000 s between the synthetic and real ECG signals. Overall, the adoption of quartic spline interpolation in ECG modeling enhances the precision, smoothness, and fidelity of signal representation, thereby improving the effectiveness of diagnostic and analytical tasks in cardiology. Three prominent machine learning algorithms, namely Decision Tree, Logistic Regression, and Gradient Boosting, effectively classify the modeled ECG signals with classification accuracies of 0.98620, 0.98965, and 0.99137, respectively. Notably, all models exhibit robust performance, characterized by high AUC values and classification accuracy. While Gradient Boosting and Logistic Regression demonstrate marginally superior performance compared to the Decision Tree model across most metrics, all models showcase commendable efficacy in ECG signal classification. The study underscores the significance of accurate ECG modeling in health sciences and biomedical technology, offering enhanced accuracy and flexibility for improved cardiovascular health understanding and diagnostic tools.

Similar performance in sound localisation with unsynchronised and synchronised automatic gain controls in bilateral cochlear implant recipients.

OBJECTIVE: One proposed method to improve sound localisation for bilateral cochlear implant (BiCI) users is to synchronise the automatic gain control (AGC) of both audio processors. In this study we tested whether AGC synchronisation in a dual-loop front-end processing scheme with a 3:1 compression ratio improves sound localisation acuity. DESIGN: Source identification in the frontal hemifield was tested in in an anechoic chamber as a function of (roving) presentation level. Three different methods of AGC synchronisation were compared to the standard unsynchronised approach. Both root mean square error (RMSE) and signed bias were calculated to evaluate sound localisation in the horizontal plane. STUDY SAMPLE: Six BiCI users. RESULTS: None of the three AGC synchronisation methods yielded significant improvements in either localisation error or bias, neither across presentation levels nor for individual presentation levels. For synchronised AGC, the pooled mean (standard deviation) localisation error of the three synchronisation methods was 24.7 (5.8) degrees RMSE, for unsynchronised AGC it was 27.4 (7.5) degrees. The localisation bias was 5.1 (5.5) degrees for synchronised AGC and 5.0 (3.8) for unsynchronised. CONCLUSIONS: These findings do not support the hypothesis that the tested AGC synchronisation configurations improves localisation acuity in bilateral users of MED-EL cochlear implants.

Exploring Convolutional Neural Network Architectures for EEG Feature Extraction.

The main purpose of this paper is to provide information on how to create a convolutional neural network (CNN) for extracting features from EEG signals. Our task was to understand the primary aspects of creating and fine-tuning CNNs for various application scenarios. We considered the characteristics of EEG signals, coupled with an exploration of various signal processing and data preparation techniques. These techniques include noise reduction, filtering, encoding, decoding, and dimension reduction, among others. In addition, we conduct an in-depth analysis of well-known CNN architectures, categorizing them into four distinct groups: standard implementation, recurrent convolutional, decoder architecture, and combined architecture. This paper further offers a comprehensive evaluation of these architectures, covering accuracy metrics, hyperparameters, and an appendix that contains a table outlining the parameters of commonly used CNN architectures for feature extraction from EEG signals.