Signal Processing of Electroencephalogram for the Detection of Attentiveness towards Short Training Videos

This research has developed a novel method which uses an easy to deploy single dry electrode wireless electroencephalogram (EEG) collection device as an input to an automated system that measures indicators of a participant’s attentiveness while they are watching a short training video. The results are promising, including 85% or better accuracy in identifying whether a participant is watching a segment of video from a boring scene or lecture, versus a segment of video from an attentiveness inducing active lesson or memory quiz. In addition, the final system produces an ensemble average of attentiveness across many participants, pinpointing areas in the training videos that induce peak attentiveness. Qualitative analysis of the results of this research is also very promising. The system produces attentiveness graphs for individual participants and these triangulate well with the thoughts and feelings those participants had during different parts of the videos, as described in their own words. As distance learning and computer based training become more popular, it is of great interest to measure if students are attentive to recorded lessons and short training videos. This research was motivated by this interest, as well as recent advances in electronic and computer engineering’s use of biometric signal analysis for the detection of affective (emotional) response. Signal processing of EEG has proven useful in measuring alertness, emotional state, and even towards very specific applications such as whether or not participants will recall television commercials days after they have seen them. This research extended these advances by creating an automated system which measures attentiveness towards short training videos. The bulk of the research was focused on electrical and computer engineering, specifically the optimization of signal processing algorithms for this particular application. A review of existing methods of EEG signal processing and feature extraction methods shows that there is a common subdivision of the steps that are used in different EEG applications. These steps include hardware sensing filtering and digitizing, noise removal, chopping the continuous EEG data into windows for processing, normalization, transformation to extract frequency or scale information, treatment of phase or shift information, and additional post-transformation noise reduction techniques. A large degree of variation exists in most of these steps within the currently documented state of the art. This research connected these varied methods into a single holistic model that allows for comparison and selection of optimal algorithms for this application. The research described herein provided for such a structured and orderly comparison of individual signal analysis and feature extraction methods. This study created a concise algorithmic approach in examining all the aforementioned steps. In doing so, the study provided the framework for a systematic approach which followed a rigorous participant cross validation so that options could be tested, compared and optimized. Novel signal analysis methods were also developed, using new techniques to choose parameters, which greatly improved performance. The research also utilizes machine learning to automatically categorize extracted features into measures of attentiveness. The research improved existing machine learning with novel methods, including a method of using per-participant baselines with kNN machine learning. This provided an optimal solution to extend current EEG signal analysis methods that were used in other applications, and refined them for use in the measurement of attentiveness towards short training videos. These algorithms are proven to be best via selection of optimal signal analysis and optimal machine learning steps identified through both n-fold and participant cross validation. The creation of this new system which uses signal processing of EEG for the detection of attentiveness towards short training videos has created a significant advance in the field of attentiveness measuring towards short training videos

Microseismicity Induced in the Opalinus Clay by a Gallery Excavation in the Mont Terri Underground Rock Laboratory

International audienc

Inflight Resources Recycling and Pollution Mitigation Impacts Through the WolfSat-1 CubeSat Mission

Mission: A CubeSat to Monitor Enzyme Activity of Ideonella Sakaiensis in the Microgravity Environmen

Seismotectonic analysis around the Mont Terri rock laboratory (Switzerland): a pilot study

For this pilot study we used recorded seismic events from the SED permanent network and data from a dedicated SNS network to improve the seismotectonic understanding of very weak seismicity in the vicinity of the Mont Terri underground laboratory. We combined field data on faults with microseismic events and modelling of stress and focal mechanisms. Eighty-six events with very low magnitudes (ML ≈ −2.0 to 2.0) recorded between July 2014 and August 2015 were located within a radius of 10 km of the underground laboratory and used for modelling. We compiled 234 fault/striation data from laboratory tunnels and regional geology, and also from seismic/borehole data on basement faults. With this database we defined seven groups of main faults in the cover and four groups in the basement. For each of these groups we computed a synthetic focal mechanism that was subsequently used to determine a synthetic P-phase waveform. The synthetic waveforms were then correlated with the microseismic events of the cover and the basement respectively. Of these, 78 events yielded satisfactorily correlation coefficients that we used for a regional seismotectonic interpretation. The synthetic focal mechanism can be linked to the main regional structural features: the NNE–SSW-oriented reactivated faults associated with the Rhine Graben development, and the NE–SW-oriented reverse faults related to the thrust development of major folds such as the Mont Terri anticline. The results for this pilot study confirm that our affirmative method can be used to augment local and regional seismotectonic interpretations with very weak-intensity earthquake data

Expansion of the Parkinson disease-associated SNCA-Rep1 allele upregulates human alpha-synuclein in transgenic mouse brain.

Alpha-synuclein (SNCA) gene has been implicated in the development of rare forms of familial Parkinson disease (PD). Recently, it was shown that an increase in SNCA copy numbers leads to elevated levels of wild-type SNCA-mRNA and protein and is sufficient to cause early-onset, familial PD. A critical question concerning the molecular pathogenesis of PD is what contributory role, if any, is played by the SNCA gene in sporadic PD. The expansion of SNCA-Rep1, an upstream, polymorphic microsatellite of the SNCA gene, is associated with elevated risk for sporadic PD. However, whether SNCA-Rep1 is the causal variant and the underlying mechanism with which its effect is mediated by remained elusive. We report here the effects of three distinct SNCA-Rep1 variants in the brains of 72 mice transgenic for the entire human SNCA locus. Human SNCA-mRNA and protein levels were increased 1.7- and 1.25-fold, respectively, in homozygotes for the expanded, PD risk-conferring allele compared with homozygotes for the shorter, protective allele. When adjusting for the total SNCA-protein concentration (endogenous mouse and transgenic human) expressed in each brain, the expanded risk allele contributed 2.6-fold more to the SNCA steady-state than the shorter allele. Furthermore, targeted deletion of Rep1 resulted in the lowest human SNCA-mRNA and protein concentrations in murine brain. In contrast, the Rep1 effect was not observed in blood lysates from the same mice. These results demonstrate that Rep1 regulates human SNCA expression by enhancing its transcription in the adult nervous system and suggest that homozygosity for the expanded Rep1 allele may mimic locus multiplication, thereby elevating PD risk

Operational Energy--Essential Knowledge for Military Officers

Energy Academic Group (EAG) New

The design thinking approaches of three different groups of designers based on self-reports

This paper compares the design thinking approaches of three groups of student-designers: industrial design and architecture undergraduates, and design PhD candidates. Participants responded to an open-ended design brief, working individually. Upon submission of their designs they were debriefed about their design processes. We compare the groups based on their submissions and self-reported design activities, especially the sequence of their design activities and the time allotted to them. There were some commonalities and differences between the two undergraduate groups but the main differences were between the two undergraduates and the PhD students. On the basis of the findings we pose questions regarding design methods in the era of 'design thinking' wherein designers are required to adopt an entrepreneurial frame of mind