Corpuscular description of the speed of light in a homogeneous medium

We are used to describe the detection of light in terms of particles and its propagation from the source to the detection, by waves. For instance, the slowing down of light in a transparent medium is always explained within the electromagnetic wave framework. We propose to approach that phenomenon through a purely corpuscular description. We find expression for the refractive indices which differ slightly from the usual Maxwell wave approach. We thus compare these expressions against experimental refractive indices and we show that both reproduce well the data. We show also how this corpuscular framework gives a very natural interpretation to the self focusing Kerr effect. Finally an experimental expectation of fluctuation of the speed of light is presented.Comment: 16 pages, 2 figure

Probing ECG-based mental state monitoring on short time segments

Electrocardiography is used to provide features for mental state monitoring systems. There is a need for quick mental state assessment in some applications such as attentive user interfaces. We analyzed how heart rate and heart rate variability features are influenced by working memory load (WKL) and time-on-task (TOT) on very short time segments (5s) with both statistical significance and classification performance results. It is shown that classification of such mental states can be performed on very short time segments and that heart rate is more predictive of TOT level than heart rate variability. However, both features are efficient for WKL level classification. What's more, interesting interaction effects are uncovered: TOT influences WKL level classification either favorably when based on HR, or adversely when based on HRV. Implications for mental state monitoring are discussed

Mapping and assessment of the capacity of ecosystems in French Guiana to supply ecosystem services. ECOSEO Project Report.

[no abstract available

Efficient Workload Classification based on Ignored Auditory Probes: A Proof of Concept

Mental workload is a mental state that is currently one of the main research focuses in neuroergonomics. It can notably be estimated using measurements in electroencephalography (EEG), a method that allows for direct mental state assessment. Auditory probes can be used to elicit event-related potentials (ERPs) that are modulated by workload. Although, some papers do report ERP modulations due to workload using attended or ignored probes, to our knowledge there is no literature regarding effective workload classification based on ignored auditory probes. In this paper, in order to efficiently estimate workload, we advocate for the use of such ignored auditory probes in a single-stimulus paradigm and a signal processing chain that includes a spatial filtering step. The effectiveness of this approach is demonstrated on data acquired from participants that performed the Multi-Attribute Task Battery – II. They carried out this task during two 10-min blocks. Each block corresponded to a workload condition that was pseudorandomly assigned. The easy condition consisted of two monitoring tasks performed in parallel, and the difficult one consisted of those two tasks with an additional plane driving task. Infrequent auditory probes were presented during the tasks and the participants were asked to ignore them. The EEG data were denoised and the probes’ ERPs were extracted and spatially filtered using a canonical correlation analysis. Next, binary classification was performed using a Fisher LDA and a fivefold cross-validation procedure. Our method allowed for a very high estimation performance with a classification accuracy above 80% for every participant, and minimal intrusiveness thanks to the use of a single-stimulus paradigm. Therefore, this study paves the way to the efficient use of ERPs for mental state monitoring in close to real-life settings and contributes toward the development of adaptive user interfaces

EEG index for control operators’ mental fatigue monitoring using interactions between brain regions

Mental fatigue is a gradual and cumulative phenomenon induced by the time spent on a tedious but mentally demanding task, which is associated with a decrease in vigilance. It may be dangerous for operators controlling air traffic or monitoring plants. An index that estimates this state on-line from EEG signals recorded in 6 brain regions is proposed. It makes use of the Frobenius distance between the EEG spatial covariance matrices of each of the 6 regions calculated on 20s epochs to a mean covariance matrix learned during an initial reference state. The index is automatically tuned from the learning set for each subject. Its performance is analyzed on data from a group of 15 subjects who performed for 90 min an experiment that modulates mental workload. It is shown that the index based on the alpha band is well correlated with an ocular index that measures external signs of mental fatigue and can accurately assess mental fatigue over long periods of time

Mental fatigue and working memory load estimation: Interaction and implications for EEG-based passive BCI

Current mental state monitoring systems, a.k.a. passive brain-computer interfaces (pBCI), allow one to perform a real-time assessment of an operator's cognitive state. In EEG-based systems, typical measurements for workload level assessment are band power estimates in several frequency bands. Mental fatigue, arising from growing time-on-task (TOT), can significantly affect the distribution of these band power features. However, the impact of mental fatigue on workload (WKL) assessment has not yet been evaluated. With this paper we intend to help fill in this lack of knowledge by analyzing the influence of WKL and TOT on EEG band power features, as well as their interaction and its impact on classification performance. Twenty participants underwent an experiment that modulated both their WKL (low/high) and time spent on the task (short/long). Statistical analyses were performed on the EEG signals, behavioral and subjective data. They revealed opposite changes in alpha power distribution between WKL and TOT conditions, as well as a decrease in WKL level discriminability with increasing TOT in both number of statistical differences in band power and classification performance. Implications for pBCI systems and experimental protocol design are discussed

Ten years of ecosystem services matrix: Review of a (r)evolution

With the Ecosystem Service (ES) concept's popularisation, the need for robust and practical methodologies for ES assessments has increased. The ES matrix approach, linking ecosystem types or other geospatial units with ES in easy-to-apply lookup tables, was first developed ten years ago and, since then, has been broadly used. Whereas detailed methodological guidelines can be found in literature, the ES matrix approach seems to be often used in a quick (and maybe even "quick and dirty”) way. Based on a reviewa of scientific publications, in which the ES matrix approach was used, we present the diversity of application contexts, highlight trends of uses and propose future recommendations for improved applications of the ES matrix. A total of 109 studies applying the ES matrix approach and one methodological study without concrete applications were considered for the review. Amongst the main patterns observed, the ES matrix approach allows the assessment of a higher number of ES than other ES assessment methods. ES can be jointly assessed with indicators for ecosystem condition and biodiversity in the ES matrix. Although the ES matrix allows us consider many data sources to achieve the assessment scores for the individual ES, in the reviewed studies, these were mainly used together with expert-based scoring (73%) and/or ES scores that were based on an already-published ES matrix or deduced by information found in related scientific publications (51%). We must acknowledge that 27% of the studies did not clearly explain their methodology. This points out a lack of method elucidation on how the data had been used and where the scores came from. Although some studies addressed the need to consider variabilities and uncertainties in ES assessments, only a minority of studies (15%) did so. Our review shows that, in 29% of the studies, an already-existing matrix was used as an initial matrix for the assessment (mainly the same matrix from one of the Burkhard et al. papers). In 16% of the reviewed studies, no other data were used for the matrix scores or no adaptation of the existing matrix used was made. However, the actual idea of the ES scores, included in the Burkhard et al.'s matrices published 10 years ago, was to provide some examples and give inspiration for one's own studies. Therefore, we recommend to use only scores assessed for a specific study or, if one wishes to use pre-existing scores from another study, to revise them in depth, taking into account the local context of the new assessment. We also recommend to systematically report and consider variabilities and uncertainties in each ES assessment. We emphasise the need for all scientific studies to describe clearly and extensively the whole methodology used to score or evaluate ES in order to be able to rate the quality of the scores obtained. In conclusion, the application of the ES matrix has to become more transparent and integrate more variability analyses. The increasing number of studies that use the ES matrix approach confirms its success, appropriability, flexibility and utility for decision-making, as well as its ability to increase awareness of ES

Efficient mental workload estimation using task-independent EEG features

Objective. Mental workload is frequently estimated by EEG-based mental state monitoring systems. Usually, these systems use spectral markers and event-related potentials ( ERPs ) . To our knowledge, no study has directly compared their performance for mental workload assessment, nor evaluated the stability in time of these markers and of the performance of the associated mental workload estimators. This study proposes a comparison of two processing chains, one based on the power in fi ve frequency bands, and one based on ERPs, both including a spatial filtering step ( respectively CSP and CCA ) , an FLDA classification and a 10-fold cross-validation. Approach. To get closer to a real life implementation, spectral markers were extracted from a short window ( i.e. towards reactive systems ) that did not include any motor activity and the analyzed ERPs were elicited by a task-independent probe that required a re fl ex-like answer ( i.e. close to the ones required by dead man ’ s vigilance devices ) . The data were acquired from 20 participants who performed a Sternberg memory task for 90 min ( i.e. 2 / 6 digits to memorize) inside which a simple detection task was inserted. The results were compared both when the testing was performed at the beginning and end of the session. Main results. Both chains performed significantly better than random; however the one based on the spectral markers had a low performance ( 60% ) and was not stable in time. Conversely, the ERP-based chain gave very high results ( 91% ) and was stable in time. Significance. This study demonstrates that an efficient and stable in time workload estimation can be achieved using task-independent spatially filtered ERPs elicited in a minimally intrusive manner

Mapping and assessing ecosystems and their services: a comparative approach to ecosystem service supply in Suriname and French Guiana

Current environmental resource management policies acknowledge the need to manage and conserve biodiversity. Sustaining good ecosystem conditions and ecosystem services (ES) is imperative at and across multiple spatial scales. The ES concept is a valuable tool to communicate the benefits that nature provides to people. In the Guiana Shield, neighbouring countries share landscapes and ecosystems, and therefore also the services they supply. This study presents the first spatial ES assessments at territorial level for Suriname and French Guiana. Expert-based ES supply matrices were used and analysed in combination with Land Use/Land Cover (LULC) data to compile ES capacity maps for the two territories. In comparison, both ES supply matrices showed a high degree of similarity–forest ecosystems scored the highest ES capacities, followed by aquatic and marine ecosystems. Agricultural and urban land cover showed weak to moderate capacities for ES supply. A statistical analysis revealed a 30% difference of the two matrix assessments. Expert scores given for ES in Suriname exceeded those in French Guiana, especially for urban LULC and planted forests. Sociodemographic factors such as age, gender and institutional environment were analysed to explain this difference. The diverging scores can also be attributed to the distribution and the degree of similarity of the different LULC types and, hence, ES capacities and different governance and institutional contexts of the assessments. Comparative evaluations are essential to understand the differences in perception of ES supply capacities and to underpin future knowledge-based bilateral conservation policies and funding decisions by governments and managers

Estimation of Working Memory Load using EEG Connectivity Measures

Working memory load can be estimated using features extracted from the electroencephalogram (EEG). Connectivity measures, that evaluate the interaction between signals, can be used to extract such features and therefore provide information about the interconnection of brain areas and electrode sites. To our knowledge, there is no literature regarding a direct comparison of the relevance of several connectivity measures for working memory load estimation. This study intends to overcome this lack of literature by proposing a direct comparison of four connectivity measures on data extracted from a working memory load experiment performed by 20 participants. These features are extracted using pattern-based or vector-based methods, and classified using an FLDA classifier and a 10-fold cross-validation procedure. The relevance of the connectivity measures was assessed by statistically comparing the obtained classification accuracy. Additional investigations were performed regarding the best set of electrodes and the best frequency band. The main results are that covariance seems to be the best connectivity measure to estimate working memory load from EEG signals, even more so with signals filtered in the beta band. point