Mobile EEG as a valid alternative to high-resolution laboratory EEG measures.

In this paper, we present how mobile electroencephalography, or mobile EEG, is becoming a relevant tool of urban studies, including among others, spatial cognition, architecture, urban design and planning. Mobile EEG is a research methodology that requires tightly controlled experiments and complicated analytical tools, but it is increasingly used beyond the clinical and research context to monitor brain function and cognition in real world. It is used to unravel our understanding of the neural processes that enable spatial perception and cognition, while is also applied to the study of psychological transactions between people and the environment, for example to gauge the effects of urban or natural environments on the emotional state of pedestrians. In the near future, mobile EEG could be integrated in the research on cities as a tool to understand the cognitive foundations of urban movement, assess the psychological impact of environments on individuals, or target interventions to improve the quality of the urban environment. In this paper we review relevant research with mobile EEG, as well as the background and methodological issues arising in such projects.

This focus issue of Physiological Measurement follows the 38th Annual International Computing in Cardiology (CinC) Conference, hosted in Hangzhou, China in September 2011 by Zhejiang University. Each year, the NIH-sponsored PhysioNet resource (http://physionet.org/) runs an open competition lasting several months, aimed at encouraging the development of solutions to an unsolved or poorly solved problem in biomedicine, in most cases making use of relevant clinical and experimental data provided freely by PhysioNet. Participants in these annual challenges discuss their diverse approaches to the Challenge problems during dedicated scientific sessions at CinC. The topics of these PhysioNet/CinC Challenges range from physiologic signal processing and analysis to forecasting and modelling clinically important events and processes.

Independent effects of working memory loads and facial expressions on event-related potential (ERP) responses: Evidence from mass univariate analysis.

In demanding work situations (e.g., during a surgery), the processing of complex soundscapes varies over time and can be a burden for medical personnel. Here we study, using mobile electroencephalography (EEG), how humans process workplace-related soundscapes while performing a complex audio-visual-motor task (3D Tetris). Specifically, we wanted to know how the attentional focus changes the processing of the soundscape as a whole. Participants played a game of 3D Tetris in which they had to use both hands to control falling blocks. At the same time, participants listened to a complex soundscape, similar to what is found in an operating room (i.e., the sound of machinery, people talking in the background, alarm sounds, and instructions). In this within-subject design, participants had to react to instructions (e.g., "place the next block in the upper left corner") and to sounds depending on the experimental condition, either to a specific alarm sound originating from a fixed location or to a beep sound that originated from varying locations. Attention to the alarm reflected a narrow attentional focus, as it was easy to detect and most of the soundscape could be ignored. Attention to the beep reflected a wide attentional focus, as it required the participants to monitor multiple different sound streams. Results show the robustness of the N1 and P3 event related potential response during this dynamic task with a complex auditory soundscape. Furthermore, we used temporal response functions to study auditory processing to the whole soundscape. This work is a step toward studying workplace-related sound processing in the operating room using mobile EEG.

Detachment (self-focused) and positive reinterpretation (situation-focused) are two important forms of cognitive reappraisal during emotion regulation. Previous research shows situation-focused reappraisal to be more effective than self-focused reappraisal for intentional emotion regulation. How the two differ in emotional consequences as components of automatic emotion regulation is however unclear. In the current study, event-related potentials were recorded to clarify this problem, while participants passively viewed disgusting or neutral scenes or formed implementation intentions based on self-focused or situation-focused reappraisal. Behavioural results showed fewer negative emotions during self-focused reappraisal than during either situation-focused reappraisal or free viewing (which had similar emotion ratings). In addition, self-reported cognitive cost was not enhanced during the two forms of reappraisal compared to passive viewing. Late positive potential (LPP) amplitudes for disgusting stimuli were larger than those elicited for neutral stimuli, at both frontal and posterior-parietal regions. This amplitude enhancement effect, irrespective of whether frontal or parietal LPP were involved, was found to be weaker during self-focused reappraisal than when participants were engaged in situation-focused reappraisal or passive viewing. The latter two conditions showed similar amplitude enhancement. These findings suggest that automatic self-focused reappraisal by implementation intention produces more favourable emotion regulation than situation-focused reappraisal, without enhancing cognitive cost.

Remote photoplethysmography (rPPG) is a recent technique for estimating heart rate by analyzing the pulsatility of skin hue using regular cameras. To determine the quality of the measurement, many existing methods are based on the signal-to-noise ratio (SNR) calculated in the frequency domain. However, the Fast Fourier Transform (FFT) operation is performed with a minimal complexity of O(n log n). Therefore, the use of this quality metric in an unsupervised rPPG framework in which this metric is estimated a large number of times will tend to greatly increase the complexity of the solution. In this paper, we propose a new probabilistic formulation of a cardiac signal quality index, with lower complexity, based on the Bayesian information criterion (BIC) that encapsulates the characteristic shape of the rPPG signal. The results of this study, obtained on a public database, have demonstrated that the proposed probabilistic metric outperforms the regular SNR metric with a lower computation complexity.

Parent-infant interaction is known to be influenced bidirectionally by parent and infant characteristics. However, it is unclear whether infant temperament affects parents' neural responses to infant stimuli. 85 infants (6-12months) were filmed in distress-eliciting tasks, which were coded for infants' negative affect. Mothers' reported infant affect was obtained from the Infant Behavior Questionnaire Very Short Form-Revised. Mothers' EEG activity was recorded while passively viewing photos of own, familiarized, and unfamiliar infants. Multiple regression indicated that mothers who reported greater infant negative affect showed a smaller difference in the late positive potential (LPP) response to own infant versus familiarized infant, controlling for researcher-coded infant negative affect. The findings suggest that parents' perceptions of their infant's temperament, but not independent measures of infant temperament, are related to electrocortical indices of emotional attention.

Estimates of surface currents over the continental shelf are now regularly made using high-frequency radar (HFR) systems along much of the U.S. coastline. The recently deployed HFR system at the Martha’s Vineyard Coastal Observatory (MVCO) is a unique addition to these systems, focusing on high spatial resolution over a relatively small coastal ocean domain with high accuracy. However, initial results from the system showed sizable errors and biased estimates of M2 tidal currents, prompting an examination of new methods to improve the quality of radar-based velocity data. The analysis described here utilizes the radial metric output of CODAR Ocean Systems’ version 7 release of the SeaSonde Radial Site Software Suite to examine both the characteristics of the received signal and the output of the direction-finding algorithm to provide data quality controls on the estimated radial currents that are independent of the estimated velocity. Additionally, the effect of weighting spatial averages of radials falling within the same range and azimuthal bin is examined to account for differences in signal quality. Applied to two month-long datasets from the MVCO high-resolution system, these new methods are found to improve the rms difference comparisons with in situ current measurements by up to 2 cm s−1, as well as reduce or eliminate observed biases of tidal ellipses estimated using standard methods.

We present a signal and thermal integrity (SI/TI) cooptimization framework for multi-chiplet placement and routing using generative adversarial imitation learning (GAIL). Design of modern 2.5-D integrated circuits face the critical challenge simultaneously optimizing TI and SI. GAIL goes beyond simply mimicking expert strategies by learning policies that generate consistent optimal behaviors across diverse state distributions, making it particularly well-suited for chiplet placement problems requiring simultaneous consideration of conflicting TI and SI objectives. Our approach leverages GAIL to learn placement strategies that effectively balance these competing objectives, incorporating both eye diagram-based signal quality metrics with turn penalties and power-aware thermal modeling. Experimental results demonstrate that our framework successfully achieves balanced optimization between thermal and SI constraints, enabling adaptive placement decisions for heterogeneous chiplet systems without requiring objective-specific retraining.

In a WCDMA system the downlink signal has typically a high peak to average ratio (PAR). In order to achieve a good efficiency in the power amplifier the PAR must be reduced i.e. the signal must be clipped. A mathematical model for signal quality metrics error vector magnitude and peak code domain error as a function of the clipping ratio is derived and tested through simulations in the case of baseband clipping. The model can be used for adjusting the clipping level in a WCDMA base station so that the downlink signal still fulfils the specifications.

The Global Navigation Satellite System (GNSS) is an important tool that is vulnerable to both intentional and unintentional radio frequency interference (RFI). This work seeks to develop a low-cost GNSS RFI monitor to detect and classify interference in an area. We present multiple signal combination-based jamming and spoofing experiments and provide an analysis of the results. Several simulated short duration jamming tests are applied to both L1 and L2 signals with four types of jamming interference, including continuous wave, narrowband jamming; broadband, additive white Gaussian noise jamming; chirp interference; and pulsed-chirp interference. A lift-off spoofing experiment combines real-time GNSS signals using two receive-only antennas and a programmable attenuator. The signals are processed using a low-cost u-blox F9P receiver and various power and signal quality metrics are analyzed. Specifically, the carrier-to-noise ratio (C/N0), the automatic gain control (AGC), and spectral analyses are useful for both identifying interference and classifying types of interference. Differences in power metric responses are observed depending on the characteristics and waveform of the jamming interference. Analyzing the position solution in addition to power metric and signal quality monitoring is found to be especially insightful in detecting the presented spoofing scenario. Our results characterize how the u-blox F9P receiver measures the several types of RFI and show the advantages of monitoring multiple metrics when monitoring for interference.

Remote photoplethysmography (rPPG) has emerged as a promising non-contact sensing technology for continuous heart rate monitoring in dynamic environments. While conventional rPPG algorithms are prone to motion artifacts in unconstrained scenarios, centralized deep learning methods face limitations from insufficient data diversity and privacy concerns. Federated learning provides a privacy-preserving approach for decentralized model training, offering potential solutions to these limitations. However, federated training suffers a significant performance drop when data from individual camera nodes is contaminated by noise, especially motion artifacts. This paper proposes FedQ<sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sup>, a federated learning framework that integrates signal quality-aware aggregation and adaptive data weighting to address motion interference in distributed rPPG systems. Specifically, FedQ<sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sup> introduces three innovations: 1) Proposing a model-aware signal quality module for real-time evaluation of data quality; 2) A dual-weight aggregation mechanism that jointly optimizes model contributions from edge devices based on both data quantity and signal quality metrics. 3) Building a federated learning evaluation system for the first time on different public datasets to validate the effectiveness of the FedQ<sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sup>. Experimental results demonstrate that while baseline federated averaging (FedAvg) achieves competitive performance in intra- and inter-dataset evaluations, its accuracy degrades significantly when motion-corrupted data from individual nodes participates in training. The proposed FedQ<sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sup> effectively mitigates these interference effects through its quality-driven aggregation strategy, ensuring robust performance across varying motion conditions.

One primary detractor in an alignment system’s performance is the corruption of alignment target integrity by thin film processes, metal grain structure, and contamination. While most aligners will acquire even very low contrast signals with sufficient precision, the measured align position is often inaccurate due to process induced asymmetries or dislocation of edges in the targets. If marks are sufficiently corrupted by lithographic processes, all alignment systems will suffer a performance degradation albeit to different degrees depending on the design. This article will present a general methodology and algorithm for the real-time detection and correction of erroneous alignment signal data in an image forming alignment system. An alignment signal figure of merit, based on image symmetry and internal consistency [A. Stankov, A. Lupata, and C. Progler, Proceedings of the KTI Microelectronics Seminar, City, Data 1991 (unpublished)], forms the basis for judging the integrity of alignment data. These two alignment signal quality metrics are computed and checked during each alignment cycle. Based on the signal metrics, the algorithm permits special signal filtering to obtain more robust alignment performance. Moreover, the algorithm has the ability to quantify the ‘‘quality’’ of alignment targets prior to exposure, thus, highlighting potential problem layers, mask errors, and wafer trouble spots due to processing. A description of the algorithm logic and implementation is described along with test bed and exposure-based data demonstrating the many benefits of the algorithm. These include improved overlay, faster alignment convergence, fewer alignment fails, and rapid assessment of alignment target integrity saving both time and money in the early stages of process development. In addition, an extension of the concept to a broader range of alignment system designs is briefly described.

Nascent technologies show promise in collecting 500 ppi fingerprints in a contactless fashion while yielding various improvements in the collection process over legacy contact collection methods currently in use throughout the world. While it has been shown that these new devices show promise in producing imagery that may be interoperable with legacy contact collected images [25][31], the contactless devices may be prone to capturing superfluous data (e.g., fingernail, finger background, or other imaging artifacts) pushing existing friction ridge algorithms meant for contact collection beyond those images for which they were originally designed. Image compression algorithms are highly sensitive to information content and complexity of the image being processed [32]. Currently there are two compression algorithms specified for fingerprints: WSQ for 500 ppi and JPEG 2000 for the more information-rich 1000 ppi fingerprint imagery [27].It was hypothesized that the more information-rich contactless-collected imagery, while being captured at the same sampling rate as legacy 500 ppi contact collected fingerprint imagery, may be better suited for the JPEG 2000 specification meant for the more information rich 1000 ppi imagery.Analysis of automated comparison scores as well as signal quality metrics showed that there was no significant difference found between the two algorithms when processing contactless imagery. Further examination of signal level metrics showed that WSQ remains better able to preserve spectral power of the image in regions of the frequency spectrum typically associated with level 2 and level 3 fingerprint features and therefore WSQ appears to be better suited for contactless fingerprint imagery.

Robustness against noise is critical for modulation recognition (MR) approaches deployed in real-world communication systems. In MR systems, a corrupted signal is normally enhanced using low-level signal enhancement (SE) before signal classification (SC). Many existing approaches address signal distortion problems by compartmentalizing SE from SC. While those approaches allow for efficient development, they also dictate compartmentalized performance metrics, without feedback from the SC module. For example, SE modules are designed using perceptual signal quality metrics but not with SC in mind. To improve the effectiveness of SE on MR, this paper proposes a joint learning framework consisting of three cascaded modules: dual-channel spectrum fusion, SE, and SC. Instead of separately processing SE and SC, these three modules are integrated into one framework and jointly trained with a single recognition loss. In contrast to estimating clean signals, the SE module in the proposed joint learning framework is trained to predict a ratio mask and find important time-frequency bins for the SC module. We integrate a multistage attention mechanism into the framework to further increase the robustness. The multistage attention mechanism is deployed to strengthen the recognition-related features learned from context information in channel, time, and frequency domains. We evaluate the recognition performance of the proposed framework and its modules on two benchmark datasets: RadioML2016.10a and RadioML2016.10b. The experiment results show that the proposed joint learning framework outperforms the separate learning framework. Moreover, comparisons are performed with several existing learning-based MR methods in the literature. The proposed joint learning framework leads to significant performance improvement, especially for modulated signals corrupted by channel noise.