Monitoring Drowsiness On-line Using a Single Encephalographic Channel

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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

Eye blink characterization from frontal EEG electrodes using source separation and pattern recognition algorithms

Due to its major safety applications, including safe driving, mental fatigue estimation is a rapidly growing research topic in the engineering field. Most current mental fatigue monitoring systems analyze brain activity through electroencephalography (EEG). Yet eye blink analysis can also be added to help characterize fatigue states. It usually requires the use of additional devices, such as EOG electrodes, uncomfortable to wear, or more expensive eye trackers. However, in this article, a method is proposed to evaluate eye blink parameters using frontal EEG electrodes only. EEG signals, which are generally corrupted by ocular artifacts, are decomposed into sources by means of a source separation algorithm. Sources are then automatically classified into ocular or non-ocular sources using temporal, spatial and frequency features. The selected ocular source is back propagated in the signal space and used to localize blinks by means of an adaptive threshold, and then to characterize detected blinks. The method, validated on 11 different subjects, does not require any prior tuning when applied to a new subject, which makes it subject-independent. The vertical EOG signal was recorded during an experiment lasting 90 min in which the participants’ mental fatigue increased. The blinks extracted from this signal were compared to those extracted using frontal EEG electrodes. Very good performances were obtained with a true detection rate of 89% and a false alarm rate of 3%. The correlation between the blink parameters extracted from both recording modalities was 0.81 in average

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

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

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

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

A comparative performance evaluation framework for power based wind turbine fault detection methods

International audienc

Sulfur sedimentary processes and fossilization

International audienc