Response-related potentials during semantic priming: the effect of a speeded button response task on ERPs

This study examines the influence of a button response task on the event-related potential (ERP) in a semantic priming experiment. Of particular interest is the N400 component. In many semantic priming studies, subjects are asked to respond to a stimulus as fast and accurately as possible by pressing a button. Response time (RT) is recorded in parallel with an electroencephalogram (EEG) for ERP analysis. In this case, the response occurs in the time window used for ERP analysis and response-related components may overlap with stimulus-locked ones such as the N400. This has led to a recommendation against such a design, although the issue has not been explored in depth. Since studies keep being published that disregard this issue, a more detailed examination of influence of response-related potentials on the ERP is needed. Two experiments were performed in which subjects pressed one of two buttons with their dominant hand in response to word-pairs with varying association strength (AS), indicating a personal judgement of association between the two words. In the first experiment, subjects were instructed to respond as fast and accurately as possible. In the second experiment, subjects delayed their button response to enforce a one second interval between the onset of the target word and the button response. Results show that in the first experiment a P3 component and motor-related potentials (MRPs) overlap with the N400 component, which can cause a misinterpretation of the latter. In order to study the N400 component, the button response should be delayed to avoid contamination of the ERP with response-related components

A Two-stage Flow-based Intrusion Detection Model ForNext-generation Networks

The next-generation network provides state-of-the-art access-independent services over converged mobile and fixed networks. Security in the converged network environment is a major challenge. Traditional packet and protocol-based intrusion detection techniques cannot be used in next-generation networks due to slow throughput, low accuracy and their inability to inspect encrypted payload. An alternative solution for protection of next-generation networks is to use network flow records for detection of malicious activity in the network traffic. The network flow records are independent of access networks and user applications. In this paper, we propose a two-stage flow-based intrusion detection system for next-generation networks. The first stage uses an enhanced unsupervised one-class support vector machine which separates malicious flows from normal network traffic. The second stage uses a self-organizing map which automatically groups malicious flows into different alert clusters. We validated the proposed approach on two flow-based datasets and obtained promising results

Discrimination of emotional states from scalp- and intracranial EEG using multiscale Renyi entropy

A data-adaptive, multiscale version of Rényi’s quadratic entropy (RQE) is introduced for emotional state discrimination from EEG recordings. The algorithm is applied to scalp EEG recordings of 30 participants watching 4 emotionally-charged video clips taken from a validated public database. Krippendorff’s inter-rater statistic reveals that multiscale RQE of the mid-frontal scalp electrodes best discriminates between five emotional states. Multiscale RQE is also applied to joint scalp EEG, amygdala- and occipital pole intracranial recordings of an implanted patient watching a neutral and an emotionally charged video clip. Unlike for the neutral video clip, the RQEs of the mid-frontal scalp electrodes and the amygdala-implanted electrodes are observed to coincide in the time range where the crux of the emotionally-charged video clip is revealed. In addition, also during this time range, phase synchrony between the amygdala and mid-frontal recordings is maximal, as well as our 30 participants’ inter-rater agreement on the same video clip. A source reconstruction exercise using intracranial recordings supports our assertion that amygdala could contribute to mid-frontal scalp EEG. On the contrary, no such contribution was observed for the occipital pole’s intracranial recordings. Our results suggest that emotional states discriminated from mid-frontal scalp EEG are likely to be mirrored by differences in amygdala activations in particular when recorded in response to emotionally-charged scenes

Discriminating multiple emotional states from EEG using a data-adaptive, multiscale information-theoretic approach

A multivariate sample entropy metric of signal complexity is applied to EEG data recorded when subjects were viewing four prior-labeled emotion-inducing video clips from a publically available, validated database. Besides emotion category labels, the video clips also came with arousal scores. Our subjects were also asked to provide their own emotion labels. In total 30 subjects with age range 19–70 years participated in our study. Rather than relying on predefined frequency bands, we estimate multivariate sample entropy over multiple data-driven scales using the multivariate empirical mode decomposition (MEMD) technique and show that in this way we can discriminate between five self-reported emotions (p<0.05p<0.05). These results could not be obtained by analyzing the relation between arousal scores and video clips, signal complexity and arousal scores, and self-reported emotions and traditional power spectral densities and their hemispheric asymmetries in the theta, alpha, beta, and gamma frequency bands. This shows that multivariate, multiscale sample entropy is a promising technique to discriminate multiple emotional states from EEG recordings

Current Status and Future Challenges of Sleep Monitoring Systems: Systematic Review

International audienceBackground:Sleep is essential for human health. Considerable effort has been made into academic and industrial research and development on wireless body area networks for sleep monitoring in terms of non-intrusiveness, portability and autonomy. Thanks to rapid advances in smart sensing and communication technologies, various sleep monitoring systems (SMS) have been developed with advantages such as low-cost, accessible, discreet, contactless, unmanned and suitable for long-term monitoring.Objective:The objective of this paper is to review current research in sleep monitoring to serve as a reference for researchers and to provide insights for future work. Specific selection criteria were chosen to include articles in which sleep monitoring systems or devices are covered.Methods:This review investigates the use of various common sensors in the hardware implementation of current SMS, as well as the types of parameters collected, the positions they are on the body, the possible description of sleep phases, and the advantages and drawbacks. In addition, the data processing algorithms and software used in different works about SMS and their results are presented. This review is not limited to the study of laboratory research, but also investigated the various popular commercial products available for sleep monitoring, presenting their characteristics, advantages and disadvantages. In particular, we categorized existing research on SMS based on how the sensor is used, including the number and type of sensors, and the preferred positions on the body. In addition to focusing on a specific system, issues concerning SMS such as privacy, economic and social impact are also included. Finally, we present an original SMS solution developed in our laboratory.Results:Through retrieving large number of articles and abstracts, we found that hotspot techniques such as big data, machine learning, artificial intelligence and data mining have not been widely applied to the sleep monitoring research area. Accelerometer is the most commonly used sensor in SMS. Most of commercial sleep monitoring products can’t provide performance evaluation based on gold standard PSG.Conclusions:The combination of hotspot techniques such as big data, machine learning, artificial intelligence and data mining with sleep monitoring may be a promising research direction and attracts more and more researchers in the future. How to balance user acceptance and monitoring performance is the biggest challenge in SMS research