Electroencephalography (EEG) signals capture the electrical activity of the brain and have traditionally been used in clinical settings. More recently, with the development of mobile, wearable EEG devices their use has been explored for other applications, including emotion recognition, quality of experience monitoring, or fatigue detection, just to name a few. EEG signals are known to have a 1/f-noise like structure and are very low amplitude, thus making them highly susceptible to artefacts, such as power line interference, muscle movement, and eye blinks. Moreover, prototyping and development of new devices to record EEG signals may introduce additional sources of artefacts generated by different instrumentation settings. As such, automated quantification of the quality of the EEG signals has become important and the focus of recent research. Here, we propose a new quality metric based on the 1/f-noise structure of the EEG signal. Experimental results show the proposed metric classifying clean versus noisy EEG segments in a subject-independent setting with an accuracy of 86.0% for the AF7 electrode location and 64.6% for AF8, two electrode locations known to be highly degraded by artefacts. Additionally, the proposed metrics are shown to generalize well to unseen electrode locations. For example, a quality model trained on AF7 noisy EEG data achieved an accuracy of 61.4% when tested on data collected from the AF8 location. EEG, quality, fooof, prototyping

The coronavirus disease 2019 (COVID-19) pandemic has drastically impacted life around the globe. As life returns to pre-pandemic routines, COVID-19 testing has become a key component, assuring that travellers and citizens are free from the disease. Conventional tests can be expensive, time-consuming (results can take up to 48h), and require laboratory testing. Rapid antigen testing, in turn, can generate results within 15-30 minutes and can be done at home, but research shows they achieve very poor sensitivity rates. In this paper, we propose an alternative test based on speech signals recorded at home with a portable device. It has been well-documented that the virus affects many of the speech production systems (e.g., lungs, larynx, and articulators). As such, we propose the use of new modulation spectral features and linear prediction analysis to characterize these changes and design a two-stage COVID-19 prediction system by fusing the proposed features. Experiments with three COVID-19 speech datasets (CSS, DiCOVA2, and Cambridge subset) show that the two-stage feature fusion system outperforms the benchmark systems of CSS and Cambridge datasets while maintaining lower complexity compared to DL-based systems. Furthermore, the two-stage system demonstrates higher generalizability to unseen conditions in a cross-dataset testing evaluation scheme. The generalizability and interpretability of our proposed system demonstrate the potential for accessible, low-cost, at-home COVID-19 testing.

Hospital workers are known to work long hours in a highly stressful environment. The COVID-19 pandemic has increased this burden multi-fold. Pre-COVID statistics already showed that one in every three nurses reported burnout, thus affecting patient satisfaction and the quality of their provided service. Real-time monitoring of burnout, and other underlying factors, such as stress, could provide feedback not only to the clinical staff, but also to hospital administrators, thus allowing for supportive measures to be taken early. In this paper, we present a context-aware speech-based system for stress detection. We consider data from 144 hospital workers who were monitored during their daily shifts over a 10-week period; subjective stress readings were collected daily. Wearable devices measured speech features and physiological readings, such as heart rate. Environment sensors, in turn, were used to track staff movement within the hospital. Here, we show the importance of context-awareness for stress level detection based on a bidirectional LSTM deep neural network. In particular, we show the importance of hospital location and circadian rhythm based contextual cues for stress prediction. Overall, we show improvements as high as 14% in F1 scores once context is incorporated, relative to using the speech features alone.

Argentine tango has been shown to help psychological and physical health by reducing perceived levels of depression and stress, similar and at times better than meditation. An often reported experience in Argentine tango is "flow", which is described as total involvement from the dancers. While this state has been self-reported by experienced dancers while dancing, it has yet to be quantified in real-time. However, with the emergence of portable and wearable devices for the acquisition of physiological signals such as electroencephalography (EEG) and electrocardiography (ECG), and recent innovations in EEG artifact removal algorithms, this quantification may now be possible. In this work presents a case study where we aim to first validate the potential of recording usable EEG and ECG data from dancers while dancing, in an unobtrusive manner, as well as investigate the existence of neurophysiological correlates of Argentine tango flow.

Mental workload assessment is of great importance for safety critical applications, especially in situations that involve physical demands, such as with first responders (e.g., paramedics, firefighters, or police officers). Advancements in physiological signal monitoring with wearable sensors have made way for real-time mental workload assessment using physiological signals. However, these models have typically been conducted in controlled laboratory settings and rely on a single physiological modality. As a result, such models often experience a drop in performance due to movement artifacts introduced in real-life conditions. In this paper, we demonstrate that a multi-modal mental workload model not only improves measurement accuracy, but can also increase robustness against physical activity artifacts. To this end, an experiment was conducted where mental workload and physical activity levels were modulated simultaneously while physiological data was collected from 48 participants using off-the-shelf wearable devices. Results show improved mental workload assessment with multi-modal fusion under varying physical activity conditions.

Assessment of mental workload in real world conditions is key to ensure the performance of workers executing tasks which demand sustained attention. Previous literature has employed electroencephalography (EEG) to this end. However, EEG correlates of mental workload vary across subjects and physical strain, thus making it difficult to devise models capable of simultaneously presenting reliable performance across users. The field of domain adaptation (DA) aims at developing methods that allow for generalization across different domains by learning domain-invariant representations. Such DA methods, however, rely on the so-called covariate shift assumption, which typically does not hold for EEG-based applications. As such, in this paper we propose a way to measure the statistical (marginal and conditional) shift observed on data obtained from different users and use this measure to quantitatively assess the effectiveness of different adaptation strategies. In particular, we use EEG data collected from individuals performing a mental task while running in a treadmill and explore the effects of different normalization strategies commonly used to mitigate cross-subject variability. We show the effects that different normalization schemes have on statistical shifts and their relationship with the accuracy of mental workload prediction as assessed on unseen participants at train time.

Stress is well known to have negative effects on health and workplace performance. Physiological sensing using wearables shows in turn great potential for realtime stress monitoring. While some off-the-shelf consumer products (e.g. smartwatches) already feature stress detection, there is still a pressing need to improve the robustness of these models in ecological settings where physical activity can hamper detection accuracy. In this paper, we show that using a multimodal physiological stress model can not only improve model accuracy, but can increase robustness to physical activity inference. To do so, we propose a video game based method to elicit emotional responses. More specifically, 48 participants played video games in which psychological stress and physical activity were jointly modulated. Physiological features showing robustness to stress are analyzed in order to guide further research.

Assessment of mental workload is crucial in safety-critical applications. Often, such applications require the user to be ambulant, such as first responders (e.g., paramedics, firefighters, or police officers). Typically, mental workload models have relied on electroencephalography (EEG) signals. EEGs, however, are known to be highly sensitive to movement artefacts, thus limited applications exist for ambulant users and studies have mostly occurred in controlled laboratory settings. In this paper, we explore the robustness of new non-linear features against movement artefacts and test their effectiveness in monitoring mental workload for ambulant users with the end goal of developing mitigation measures based on mental state of operators. To this end, an EEG experiment was conducted where mental workload and physical activity levels were modulated simultaneously and data was collected from 48 participants. Classical EEG features used for workload assessment, such as spectral power and amplitude/phase coherence, were used as benchmarks and compared against the proposed non-linear multi-scale permutation entropy features. Experimental results show the proposed features consistently outperforming the benchmark ones, thus high-lighting their robustness to movement artefacts.

Assessment of mental workload is crucial for applications which require constant attention and where conditions such as mental fatigue and drowsiness must be avoided. As such, electroencephalography (EEG) based mental workload models have been developed in the past. The majority of these models, however, have assumed individuals are not ambulant, thus bypassing the issue of movement-related EEG artefacts. While such models may be useful for a number of applications (e.g., operators are sitting), they may not apply in situations in which operators are performing their task under different physical activity levels. Representative examples can include first responders, such as paramedics, firefighters, or police officers. In this work, we take the first steps towards overcoming this limitation and present results of an experiment simultaneously eliciting increasing mental workload states at varying physical activity levels. EEG data from forty-seven participants was collected while they performed the NASA Revised Multi-Attribute Task Battery II (MATB-II) under three different activity level conditions (no, medium, high). In this study, we report the effects of activity on the noise-robustness and distribution of several spectral, amplitude/phase coherence, and amplitude modulation features, with the ultimate goal of deriving a feature set tailored towards automated workload assessment during physical activity. Preliminary results show spectral features acquired from the frontal area of the cortex as the most promising and that activity aware mental workload models should be developed.