Aurélie Campagne

Correlation between driving errors and vigilance level: influence of the driver's age.

During long and monotonous driving at night, most drivers progressively show signs of visual fatigue and loss of vigilance. Their capacity to maintain adequate driving performance usually is affected and varies with the age of the driver. The main question is to know, on one hand, if occurrence of fatigue and drowsiness is accompanied by a modification in the driving performance of the driver and, on the other hand, if this relationship partially depends on the drivers age. Forty-six male drivers, divided into three age categories: 20-30, 40-50, and 60-70 years, performed a 350-km motorway driving session at night on a driving simulator. Driving errors were measured in terms of number of running-off-the-road incidents (RORI) and large speed deviations. The evolution of physiological vigilance level was evaluated using electroencephalography (EEG) recording. In older drivers, in comparison with young and middle-aged drivers, the degradation of driving performance was correlated to the evolution of lower frequency waking EEG (i.e., theta). Contrary to young and middle-aged drivers, the deterioration of the vigilance level attested by EEG correlated with the increase in gravity of all studied driving errors in older drivers. Thus, depending on the age category considered, only part of the driving errors would constitute a relevant indication as for the occurrence of a state of low arousal.

Functional Segregation of Cortical Language Areas by Sentence Repetition

The functional organization of the perisylvian language network was examined using a functional MRI (fMRI) adaptation paradigm with spoken sentences. In Experiment 1 , a given sentence was presented every 14.4 s and repeated two, three, or four times in a row. The study of the temporal properties of the BOLD response revealed a temporal gradient along the dorsal–ventral and rostral–caudal directions: From Heschls gyrus, where the fastest responses were recorded, responses became increasingly slower toward the posterior part of the superior temporal gyrus and toward the temporal poles and the left inferior frontal gyrus, where the slowest responses were observed. Repetition induced a decrease in amplitude and a speeding up of the BOLD response in the superior temporal sulcus (STS), while the most superior temporal regions were not affected. In Experiment 2 , small blocks of six sentences were presented in which either the speaker voice or the linguistic content of the sentence, or both, were repeated. Data analyses revealed a clear asymmetry: While two clusters in the left superior temporal sulcus showed identical repetition suppression whether the sentences were produced by the same speaker or different speakers, the homologous right regions were sensitive to sentence repetition only when the speaker voice remained constant. Thus, hemispheric left regions encode linguistic content while homologous right regions encode more details about extralinguistic features like speaker voice. The results demonstrate the feasibility of using sentence‐level adaptation to probe the functional organization of cortical language areas. Hum Brain Mapp, 2006.

Selection of the Most Relevant Physiological Features for Classifying Emotion

With the development of wearable physiological sensors, emotion estimation becomes a hot topic in the literature. Databases of physiological signals recorded during emotional stimulation are acquired and machine learning algorithms are used. Yet, which are the most relevant signals to detect emotions is still a question to be answered. In order to better understand the contribution of each signal, and thus sensor, to the emotion estimation problem, several feature selection algorithms were implemented on two databases freely available to the research community (DEAP and MANHOB-HCI). Both databases manipulate emotions by showing participants short videos (video clips or part of movies respectively). Features extracted from Galvanic Skin response were found to be relevant for arousal estimation in both databases. Other relevant features were eye closing rate for arousal, variance of zygomatic EMG for valence (those features being only available for DEAP). The hearth rate variability power in three frequency bands also appeared to be very relevant, but only for MANHOB-HCI database where heat rate was measured using ECG (whereas DEAP used PPG). This suggests that PPG is not accurate enough to estimate HRV precisely. Finally we showed on DEAP database that emotion classifiers need just a few well selected features to obtain similar performances to literature classifiers using more features.

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 operators 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.

Oculomotor Changes Due to Road Events During Prolonged Monotonous Simulated Driving

The possible influence of occuring external events on driver attention and vigilance level was assessed during a prolonged simulated driving task. Special attention was given to the duration of the task, as well as to the influence of time of day and of individual factors. Thirty six subjects drove for two hours. Blinking activity and eye movements associated with glances to the speedometer were recorded during the entire driving task and particularly during specific road events. During significant events, blinking and ocular activity decreased, attesting a higher attention of the driver. With increased duration of driving, the reduction in blinking and ocular activity was progressively smaller for the less significant events, indicating a reduction in attention. During driving, women blinked more frequently than men. With increased duration of driving, drivers adopted different behavioural strategies depending on their age and sex to reach a safe and adapted method of driving.

Microsaccades are modulated by both attentional demands of a visual discrimination task and background noise

Microsaccades are miniature saccades occurring once or twice per second during visual fixation. While microsaccades and saccades share similarities at the oculomotor level, the functional roles of microsaccades are still debated. In this study, we examined the hypothesis that the microsaccadic activity is affected by the type of noisy background during the execution of a particular discrimination task. Human subjects had to judge the orientation of a tilted stimulus embedded in static or dynamic backgrounds in a forced choice-task paradigm, as adapted from Rucci, Iovin, Poletti, and Santini (2007). Static backgrounds induced more microsaccades than dynamic ones only during the execution of the discrimination task. A directional bias of microsaccades, dictated by the stimulus orientation, was temporally coupled with this period of increased activity. Both microsaccade rates and orientations were comparable across background types after the response time although subjects maintained fixation until the end of the trial. This represents a background-specific modulation of the microsaccadic activity driven by attentional demands. The visual influence of microsaccades on discrimination performances was modeled at the retinal level for both types of backgrounds. A higher simulated microsaccadic activity was necessary for static backgrounds in order to achieve discrimination performance scores comparable to that of dynamic ones. Taken together, our experimental and theoretical findings further support the idea that microsaccades are under attentional control and represent an efficient sampling strategy allowing spatial information acquisition.

A comparison of ERP spatial filtering methods for optimal mental workload estimation

Mental workload estimation is of crucial interest for user adaptive interfaces and neuroergonomics. Its estimation can be performed using event-related potentials (ERPs) extracted from electroencephalographic recordings (EEG). Several ERP spatial filtering methods have been designed to enhance relevant EEG activity for active brain-computer interfaces. However, to our knowledge, they have not yet been used and compared for mental state monitoring purposes. This paper presents a thorough comparison of three ERP spatial filtering methods: principal component analysis (PCA), canonical correlation analysis (CCA) and the xDAWN algorithm. Those methods are compared in their performance to allow for an accurate classification of mental workload when applied in an otherwise similar processing chain. The data of 20 healthy participants that performed a memory task for 10 minutes each was used for classification. Two levels of mental workload were considered depending on the number of digits participants had to memorize (2/6). The highest performances were obtained using the CCA filtering and the xDAWN algorithm respectively with 98% and 97% of correct classification. Their performances were significantly higher than that obtained using the PCA filtering (88%).

Brain Processing of Emotional Scenes in Aging: Effect of Arousal and Affective Context

Research on emotion showed an increase, with age, in prevalence of positive information relative to negative ones. This effect is called positivity effect. From the cerebral analysis of the Late Positive Potential (LPP), sensitive to attention, our study investigated to which extent the arousal level of negative scenes is differently processed between young and older adults and, to which extent the arousal level of negative scenes, depending on its value, may contextually modulate the cerebral processing of positive (and neutral) scenes and favor the observation of a positivity effect with age. With this aim, two negative scene groups characterized by two distinct arousal levels (high and low) were displayed into two separate experimental blocks in which were included positive and neutral pictures. The two blocks only differed by their negative pictures across participants, as to create two negative global contexts for the processing of the positive and neutral pictures. The results show that the relative processing of different arousal levels of negative stimuli, reflected by LPP, appears similar between the two age groups. However, a lower activity for negative stimuli is observed with the older group for both tested arousal levels. The processing of positive information seems to be preserved with age and is also not contextually impacted by negative stimuli in both younger and older adults. For neutral stimuli, a significantly reduced activity is observed for older adults in the contextual block of low-arousal negative stimuli. Globally, our study reveals that the positivity effect is mainly due to a modulation, with age, in processing of negative stimuli, regardless of their arousal level. It also suggests that processing of neutral stimuli may be modulated with age, depending on negative context in which they are presented to. These age-related effects could contribute to justify the differences in emotional preference with age.

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.

Behavioral assessment of emotional and motivational appraisal during visual processing of emotional scenes depending on spatial frequencies.

Previous studies performed on visual processing of emotional stimuli have revealed preference for a specific type of visual spatial frequencies (high spatial frequency, HSF; low spatial frequency, LSF) according to task demands. The majority of studies used a face and focused on the appraisal of the emotional state of others. The present behavioral study investigates the relative role of spatial frequencies on processing emotional natural scenes during two explicit cognitive appraisal tasks, one emotional, based on the self-emotional experience and one motivational, based on the tendency to action. Our results suggest that HSF information was the most relevant to rapidly identify the self-emotional experience (unpleasant, pleasant, and neutral) while LSF was required to rapidly identify the tendency to action (avoidance, approach, and no action). The tendency to action based on LSF analysis showed a priority for unpleasant stimuli whereas the identification of emotional experience based on HSF analysis showed a priority for pleasant stimuli. The present study confirms the interest of considering both emotional and motivational characteristics of visual stimuli.