The photoplethysmographic (PPG) signal of the finger is being used to create embedded devices that estimate physiological variables. This project outlines an innovative method for developing a synthetic PPG generator that produces both actual reference digital signals and their equivalent analog signals using open-source technology. A series of PPG profiles is synthesized using three variant Gaussian functions. A low-frequency trend induced by respiratory frequency and background noise are then added. To generate a diverse range of continuously variable PPG profiles within specified boundaries and customizable levels of interference, all parameters undergo random fluctuations on a cycle-by-cycle basis, as per user-defined constraints. The generated signal is then converted into its equivalent analog form through the use of an RC filter that low-frequency filters a Pulse-Width Modulation square wave that is modulated directly by the generated signal. The software returns different PPG profiles and allows the signal comparison before vs after the addition of different-intensity modulated respiratory trends and background noise. The digital signal is faithfully converted into an equivalent analog voltage signal capable of reproducing not only the waveform profile but also the respiratory trend and various levels of noise.

Background: In cochlear implant (CI) treatment, there is a large variability in outcome. The aim of our study was to identify the independent audiometric measures that are most directly relevant for describing this variability in outcome characteristics of CI recipients. An extended audiometric test battery was used with selected adult patients in order to characterize the full range of CI outcomes. Methods: CI users were recruited for this study on the basis of their postoperative results and divided into three groups: low (1st quartile), moderate (medium decentile), and high hearing performance (4th quartile). Speech recognition was measured in quiet by using (i) monosyllabic words (40-80 dB SPL), (ii) speech reception threshold (SRT) for numbers, and (iii) the German matrix test in noise. In order to reconstruct demanding everyday listening situations in the clinic, the temporal characteristics of the background noise and the spatial arrangements of the signal sources were varied for tests in noise. In addition, a survey was conducted using the Speech, Spatial, and Qualities (SSQ) questionnaire and the Listening Effort (LE) questionnaire. Results: Fifteen subjects per group were examined (total N = 45), who did not differ significantly in terms of age, time after CI surgery, or CI use behavior. The groups differed mainly in the results of speech audiometry. For speech recognition, significant differences were found between the three groups for the monosyllabic tests in quiet and for the sentences in stationary (S0°N0°) and fluctuating (S0°NCI) noise. Word comprehension and sentence comprehension in quiet were both strongly correlated with the SRT in noise. This observation was also confirmed by a factor analysis. No significant differences were found between the three groups for the SSQ questionnaire and the LE questionnaire results. The results of the factor analysis indicate that speech recognition in noise provides information highly comparable to information from speech intelligibility in quiet. Conclusions: The factor analysis highlighted three components describing the postoperative outcome of CI patients. These were (i) the audiometrically measured supra-threshold speech recognition and (ii) near-threshold audibility, as well as (iii) the subjective assessment of the relationship to real life as determined by the questionnaires. These parameters appear well suited to setting up a framework for a test battery to assess CI outcomes.

Determining visual attention during cognitive tasks using activation MRI remains challenging. This study aimed to develop a new eye-tracking (ET) post-processing platform to enhance data accuracy, validate the feasibility of subsequent ET-fMRI applications, and provide tool support. Sixteen volunteers aged 18 to 20 were exposed to a visual temporal paradigm with changing images of objects and faces in various locations while their eye movements were recorded using an MRI-compatible ET system. The results indicate that the accuracy of the data significantly improved after post-processing. Participants generally maintained their visual attention on the screen, with mean gaze positions ranging from 89.1% to 99.9%. In cognitive tasks, the gaze positions showed adherence to instructions, with means ranging from 46.2% to 50%. Temporal consistency assessments indicated prolonged visual tasks can lead to decreased attention during certain tasks. The proposed methodology effectively identified and quantified visual artifacts and losses, providing a precise measure of visual attention. This study offers a robust framework for future work integrating filtered eye-tracking data with fMRI analyses, supporting cognitive neuroscience research.

Condition monitoring (CM) is the basis of prognostics and health management (PHM), which is gaining more and more importance in the industrial world. CM, which refers to the tracking of industrial equipment\'s state of health during operations, plays, in fact, a significant role in the reliability, safety, and efficiency of industrial operations. This paper proposes a data-driven CM approach based on the autoregressive (AR) modeling of the acquired sensor data and their analysis within frequency subbands. The number and size of the bands are determined with negligible human intervention, analyzing only the time-frequency representation of the signal of interest under normal system operating conditions. In particular, the approach exploits the synchrosqueezing transform to improve the signal energy distribution in the time-frequency plane, defining a multidimensional health indicator built on the basis of the AR power spectral density and the symmetric Itakura-Saito spectral distance. The described health indicator proved capable of detecting changes in the signal spectrum due to the occurrence of faults. After the initial definition of the bands and the calculation of the characteristics of the nominal AR spectrum, the procedure requires no further intervention and can be used for online condition monitoring and fault diagnosis. Since it is based on the comparison of spectra under different operating conditions, its applicability depends neither on the nature of the acquired signal nor on a specific system to be monitored. As an example, the effectiveness of the proposed method was favorably tested using real data available in the Case Western Reserve University (CWRU) Bearing Data Center, a widely known and used benchmark.

The dataset presented in this article is an update of the dataset provided by K. Edanami and G. Sun entitled \"Medical Radar Signal Dataset for Non-Contact Respiration and Heart Rate Measurement\" [1]. The new dataset includes radar signals and reference laser measurements from experiments conducted on anesthetized rats. The rats were placed in a prone position, and isoflurane was administered in varying concentrations to maintain anesthesia. A 24 GHz radar and laser sensor were positioned above the rats to capture the necessary data. The dataset contains time-stamped radar I and Q channel signals as well as laser measurements. Additionally, MATLAB code for signal visualization and FFT (fast Fourier transform)-based respiration rate estimation is provided. This comprehensive dataset and accompanying MATLAB code facilitate the advancement of non-invasive respiration measurement techniques in small animals, with potential applications in biomedical research.

Conventional signal processing implemented on clinical cochlear implant (CI) sound processors is based on envelope signals extracted from overlapping frequency regions. Conventional strategies do not encode temporal envelope or temporal fine-structure cues with high fidelity. In contrast, several research strategies have been developed recently to enhance the encoding of temporal envelope and fine-structure cues. The present study examines the salience of temporal envelope cues when encoded into vocoder representations of CI signal processing. Normal-hearing listeners were evaluated on measures of speech reception, speech quality ratings, and spatial hearing when listening to vocoder representations of CI signal processing. Conventional vocoder techniques using envelope signals with noise- or tone-excited reconstruction were evaluated in comparison to a novel approach based on impulse-response reconstruction. A variation of this impulse-response approach was based on a research strategy, the Fundamentally Asynchronous Stimulus Timing (FAST) algorithm, designed to improve temporal precision of envelope cues. The results indicate that the introduced impulse-response approach, combined with the FAST algorithm, produces similar results on speech reception measures as the conventional vocoder approaches, while providing significantly better sound quality and spatial hearing outcomes. This novel approach for stimulating how temporal envelope cues are encoded into CI stimulation has potential for examining diverse aspects of hearing, particularly in aspects of musical pitch perception and spatial hearing.

Exposure of polytetrafluoroethylene to argon plasma results in chemical modification of the polymer near the surface. Interestingly, PTFE modification can be induced by the sub-band gap ultraviolet UV irradiation. In the latter case, the changes in the chemical structure are very subtle, and they are practically invisible to conventional experimental techniques. Raman spectra of irradiated and raw samples show practically identical peaks. However, the baseline that is commonly considered as an unwanted spectral component, contains an important information that reflects the minor structural changes. With the proper data analysis, this allows us to visualize the effects of the argon plasma and sub-band gap UV irradiation on the polymer.

Brain activity implies the orchestrated functioning of interconnected brain regions. Typical in vitro models aim to mimic the brain using single human pluripotent stem cell-derived neuronal networks. However, the field is constantly evolving to model brain functions more accurately through the use of new paradigms, e.g., brain-on-a-chip models with compartmentalized structures and integrated sensors. These methods create novel data requiring more complex analysis approaches. The previously introduced circular tripartite network concept models the connectivity between spatially diverse neuronal structures. The model consists of a microfluidic device allowing axonal connectivity between separated neuronal networks with an embedded microelectrode array to record both local and global electrophysiological activity patterns in the closed circuitry. The existing tools are suboptimal for the analysis of the data produced with this model. Here, we introduce advanced tools for synchronization and functional connectivity assessment. We used our custom-designed analysis to assess the interrelations between the kainic acid (KA)-exposed proximal compartment and its nonexposed distal neighbors before and after KA. Novel multilevel circuitry bursting patterns were detected and analyzed in parallel with the inter- and intracompartmental functional connectivity. The effect of KA on the proximal compartment was captured, and the spread of this effect to the nonexposed distal compartments was revealed. KA induced divergent changes in bursting behaviors, which may be explained by distinct baseline activity and varied intra- and intercompartmental connectivity strengths. The circular tripartite network concept combined with our developed analysis advances importantly both face and construct validity in modeling human epilepsy in vitro.

UNASSIGNED: 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.
UNASSIGNED: 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.
UNASSIGNED: Six BiCI users.
UNASSIGNED: 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.
UNASSIGNED: These findings do not support the hypothesis that the tested AGC synchronisation configurations improves localisation acuity in bilateral users of MED-EL cochlear implants.

Chronic spinal pain (CSP) is a prevalent condition, and prolonged sitting at work can contribute to it. Ergonomic factors like this can cause changes in motor variability. Variability analysis is a useful method to measure changes in motor performance over time. When performing the same task multiple times, different performance patterns can be observed. This variability is intrinsic to all biological systems and is noticeable in human movement. This study aims to examine whether changes in movement variability and complexity during real-time office work are influenced by CSP. The hypothesis is that individuals with and without pain will have different responses to office work tasks. Six office workers without pain and ten with CSP participated in this study. Participant\'s trunk movements were recorded during work for an entire week. Linear and nonlinear measures of trunk kinematic displacement were used to assess movement variability and complexity. A mixed ANOVA was utilized to compare changes in movement variability and complexity between the two groups. The effects indicate that pain-free participants showed more complex and less predictable trunk movements with a lower degree of structure and variability when compared to the participants suffering from CSP. The differences were particularly noticeable in fine movements.