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Waveforms of eye-blink-evoked potentials evoked at anterior, parietal, and occipital leads. ERPs are presented for standing (dotted blue), walking on meadow (black solid), and mastering the obstacle course (red solid). Data from the first block (bold) and the second block (thin) are superposed. This demonstrates that there was hardly any difference in the eye-blink-related activity across blocks. By and large, waveshapes and morphologies resemble onset-evoked visual ERPs.
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Blinking is a natural user-induced response which paces visual information processing. This study investigates whether blinks are viable for segmenting continuous electroencephalog-raphy (EEG) activity, for inferring cognitive demands in ecologically valid work environments. We report the blink-related EEG measures of participants who performed aud...
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Context 1
... common sequence of visual ERP components could be observed in the grand averages across all participants (see Figure 2). ERPs are presented for standing (dotted blue), walking on meadow (black solid), and mastering the obstacle course (red solid). ...
Context 2
... waveshapes were substantially different between walking conditions but fairly similar across blocks (see Figure 2). Most of the parameters neither revealed a main effect of experimental block nor an interaction between walking conditions and experimental block, with all F-values being around 1 and below. ...
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Citations
... In recent studies, eye blinks have emerged as a promising tool for detecting cognitive load in real-life situations involving sustained and uninterrupted task performance [11][12][13][14] . Participants naturally generate eye blinks, and their detection through EEG requires no additional equipment, providing a non-intrusive and uncomplicated approach to identifying noteworthy events. ...
... Studies on blink event-related potentials (bERPs) have revealed their responsiveness to a range of factors, including the experimental context and the specific nature of the ongoing task 14,17 . A recent study conducted in our laboratory by Wascher et al. 11 employed blink patterns to partition continuous EEG data and deduce cognitive loads during walking tasks. The study found that blink-associated EEG signals differentiate various degrees of cognitive demand while walking. ...
... Furthermore, it is assumed that the blink-related posterior P3 and anterior N2 components could be valid markers of resource allocation, task demand, and cognitive control during cognitive processing 11,18 . These components provide valuable insights into the dynamic nature of cognitive processes, with the increase in P3 amplitude in parieto-occipital regions being specifically associated with higher cognitive load 11,18 . ...
Assessing drivers’ cognitive load is crucial for driving safety in challenging situations. This research employed the occurrence of drivers’ natural eye blinks as cues in continuously recorded EEG data to assess the cognitive workload while reactive or proactive driving. Twenty-eight participants performed either a lane-keeping task with varying levels of crosswind (reactive) or curve road (proactive). The blink event-related potentials (bERPs) and spectral perturbations (bERSPs) were analyzed to assess cognitive load variations. The study found that task load during reactive driving did not significantly impact bERPs or bERSPs, possibly due to enduring alertness for vehicle control. The proactive driving revealed significant differences in the occipital N1 component with task load, indicating the necessity to adapt the attentional resources allocation based on road demands. Also, increased steering complexity led to decreased frontal N2, parietal P3, occipital P2 amplitudes, and alpha power, requiring more cognitive resources for processing relevant information. Interestingly, the proactive and reactive driving scenarios demonstrated a significant interaction at the parietal P2 and occipital N1 for three difficulty levels. The study reveals that EEG measures related to natural eye blink behavior provide insights into the effect of cognitive load on different driving tasks, with implications for driver safety.
... However, adding event markers based on stimulus presentation can be challenging in naturalistic settings, where visual inputs to participants continuously change. Recently, eye blinks have been proposed as valuable event markers to indicate cognitive load in real-life situations where a task is performed consistently without interruption [8], [9], [10], [11]. People generate these blinks spontaneously, and such blinks can be easily detected with EEG without any extra devices, offering a non-invasive and straightforward approach to identifying notable events. ...
... Research exploring the brain activity associated with eye blinking is essential to authenticate using eye blinks as event markers. Several studies have demonstrated that spontaneous eye blinks indicate cognitive load, often when processing intricate visual scenes [3], [8]. After blinking, the brain experiences an upsurge of visual information when the individuals open their eyes, leading to visual processing-related brain activity. ...
... However, previous research linking eye blinks to cognitive load has primarily concentrated on blink features such as eye movements and blink rate variability [12], [13], [14], with less emphasis on brain activity associated with eye blinks. To the best of our knowledge, only a handful of studies have investigated the neuronal processes connected to eye blink events [3], [8], [15], and none have yet investigated the neural and blink patterns involved in decoding cognitive states that precede and follow eye blinks. ...
Accurately evaluating cognitive load during work-related tasks in complex real-world environments is challenging, leading researchers to investigate the use of eye blinking as a fundamental pacing mechanism for segmenting EEG data and understanding the neural mechanisms associated with cognitive workload. Yet, little is known about the temporal dynamics of eye blinks and related visual processing in relation to the representation of task-specific information. Therefore, we analyzed EEG responses from two experiments involving simulated driving (re-active and pro-active) with three levels of task load for each, as well as operating a steam engine (active vs. passive), to decode the temporal dynamics of eye blink activity and the subsequent neural activity that follows blinking. As a result, we successfully decoded the binary representation of difficulty levels for pro-active driving using multivariate pattern analysis. However, the decoding level varied for different re-active driving conditions, which could be attributed to the required level of alertness. Furthermore, our study revealed that it was possible to decode both driving types as well as steam engine operating conditions, with the most significant decoding activity observed approximately 200 ms after a blink. Additionally, our findings suggest that eye blinks have considerable potential for decoding various cognitive states that may not be discernible through neural activity, particularly near the peak of the blink. The findings demonstrate the potential of blink-related measures alongside EEG data to decode cognitive states during complex tasks, with implications for improving evaluations of cognitive and behavioral states during tasks, such as driving and operating machinery.
... Here, eye activity such as blinks and/or saccades was found to be very useful. By analyzing blink-evoked event-related potentials (bERPs) and blink-evoked event-related spectral perturbations (bERSPs) during natural tasks like walking, insights into visual processing, and visual and cognitive demands of natural behavior were provided without experimental manipulation of the (visual) scene [6][7][8]. This technique could also be applied to driving scenarios, although car driving may be more complex than walking in terms of eye behavior. ...
... Firstly, bERPs have a similar waveform as regular event-related potentials (ERPs) following visual stimuli; positive and negative deflections after the event can be interpreted in terms of event-related changes in cognitive processing. This was shown in the laboratory [6] as well as in outside environments [7,9]. Changes in ERP components (namely P1, N1, P2, etc.) indicate modulations in sensory stimulus processing, attentional allocation as well as in the availability of mental resources [10]. ...
... Based on ICA decomposition, eye blinks were identified by gaussian fits [7]. Saccades were detected by searching for maximal velocity in the IC identified to reflect horizontal eye movements. ...
Driving safety strongly depends on the driver's mental states and attention to the driving situation. Previous studies demonstrate a clear relationship between EEG measures and mental states, such as alertness and drowsiness, but often only map their mental state for a longer period of time. In this driving simulation study, we exploit the high temporal resolution of the EEG to capture fine-grained modulations in cognitive processes occurring before and after eye activity in the form of saccades, fixations, and eye blinks. A total of 15 subjects drove through an approximately 50-km course consisting of highway, country road, and urban passages. Based on the ratio of brain oscillatory alpha and theta activity, the total distance was classified into 10-m-long sections with low, medium, and high task loads. Blink-evoked and fixation-evoked event-related potentials, spectral perturbations, and lateralizations were analyzed as neuro-cognitive correlates of cognition and attention. Depending on EEG-based estimation of task load, these measures showed distinct patterns associated with driving behavior parameters such as speed and steering acceleration and represent a temporally highly resolved image of specific cognitive processes during driving. In future applications, combinations of these EEG measures could form the basis for driver warning systems which increase overall driving safety by considering rapid fluctuations in driver's attention and mental states.
... Similar changes have previously been detected in the electroencephalography (EEG) power between straight and curve driving, 42 and the eyeblink behavior and task-related EEG power changes may be closely related. 43 It is important to note that eyeblink signatures are purely behavioral but provide a clue to cognitive state changes with a comparable time and spatial resolution to EEG. ...
How do humans blink while driving a vehicle? Although gaze control patterns have been previously reported in relation to successful steering, eyeblinks that disrupt vision are believed to be randomly distributed during driving or are ignored. Herein, we demonstrate that eyeblink timing shows reproducible patterns during real formula car racing driving and is related to car control. We studied three top-level racing drivers. Their eyeblinks and driving behavior were acquired during practice sessions. The results revealed that the drivers blinked at surprisingly similar positions on the courses. We identified three factors underlying the eyeblink patterns: the driver's individual blink count, lap pace associated with how strictly they followed their pattern on each lap, and car acceleration associated with when/where to blink at a moment. These findings suggest that the eyeblink pattern reflected cognitive states during in-the-wild driving and experts appear to change such cognitive states continuously and dynamically.
... Past studies have found that blinks are more likely Frontiers in Neuroscience 03 frontiersin.org to occur with higher frequency after a period of blink suppression (e.g., during attentional focus) or when the processing mode changes (e.g., attention re-allocation) (Wascher et al., 2014(Wascher et al., , 2022. Blinks are thus considered to reflect attentional resource allocation (Stern et al., 1984). ...
... However, most research linking eye blinks to cognitive load has focused on characteristics of eye blinks such as the number of blinks and blink deflection while less research has analyzed brain activity related to eye blinks (Orchard and Stern, 1991;Pivik and Dykman, 2004;Valtchanov and Ellard, 2015). Indeed, only a few studies have examined the neuronal processes related to eye blink events (Wascher et al., 2014(Wascher et al., , 2022Wunderlich and Gramann, 2020). More research is thus needed that investigates brain activity during eye blinks when individuals perform cognitive tasks to validate the use of eye blinks as indicators of cognitive load and identify the cognitive processes following spontaneous eye blinks. ...
... Previous research found that event-related potentials (ERPs) occur after eye blinks (Berg and Davies, 1988;Wascher et al., 2014Wascher et al., , 2022Wunderlich and Gramann, 2020). Importantly for the present study, a previous study that used EEG to assess brain activity while using eye blinks as event markers has identified blink event-related potentials (bERPs) associated with the performance of a cognitive task (Wascher et al., 2022). ...
The continuous assessment of pedestrians’ cognitive load during a naturalistic mobile map-assisted navigation task is challenging because of limited experimental control over stimulus presentation, human-map-interactions, and other participant responses. To overcome this challenge, the present study takes advantage of navigators’ spontaneous eye blinks during navigation to serve as event markers in continuously recorded electroencephalography (EEG) data to assess cognitive load in a mobile map-assisted navigation task. We examined if and how displaying different numbers of landmarks (3 vs. 5 vs. 7) on mobile maps along a given route would influence navigators’ cognitive load during navigation in virtual urban environments. Cognitive load was assessed by the peak amplitudes of the blink-related fronto-central N2 and parieto-occipital P3. Our results show increased parieto-occipital P3 amplitude indicating higher cognitive load in the 7-landmark condition, compared to showing 3 or 5 landmarks. Our prior research already demonstrated that participants acquire more spatial knowledge in the 5- and 7-landmark conditions compared to the 3-landmark condition. Together with the current study, we find that showing 5 landmarks, compared to 3 or 7 landmarks, improved spatial learning without overtaxing cognitive load during navigation in different urban environments. Our findings also indicate a possible cognitive load spillover effect during map-assisted wayfinding whereby cognitive load during map viewing might have affected cognitive load during goal-directed locomotion in the environment or vice versa. Our research demonstrates that users’ cognitive load and spatial learning should be considered together when designing the display of future navigation aids and that navigators’ eye blinks can serve as useful event makers to parse continuous human brain dynamics reflecting cognitive load in naturalistic settings.
... In the following years, several studies also highlighted the importance of eye blinks for everyday information structuring to reflect cognitive processes [11] during visual [12] or auditory tasks [13]. While these studies showed that it was possible to deduct useful information from laboratory environments, blinks were shown to represent useful structuring events in real-life situations while walking either on a patch of lawn or in an inner city setting [14], [15]. ...
... Given the impact of blinks on brain-related and cognitive activity, recent literature [14], [12] contradicts arbitrarily categorizing blinks as artifacts. Interestingly, previous studies [17], [18] noticed a rise in low-frequency band (delta) signal time-locked to the blink at rest. ...
... To evaluate whether an event-related approach is feasible for real-life cognitive state determination, a workplace simulation study consisting of a series of alternating active and passive power plant operator conditions was conducted. To retrieve blink event markers, a pattern-matching technique was used [14]. The active task consisted of actively adjusting the temperature of a miniature one-cylinder steam engine. ...
Evaluating and understanding the cognitive demands of natural activities has been difficult using neurocognitive approaches like mobile EEG. While task-unrelated stimuli are commonly added to a workplace simulation to estimate event-related cognitive processes, using eyeblink activity poses an alternative as it is inherent to human behavior. This study aimed to investigate the eye blink event-related EEG activity of fourteen subjects while working in a power-plant operator simulation - actively operating (active condition) or observing (passive condition) a real-world steam engine. The changes in event-related potentials, event-related spectral perturbations, and functional connectivity under both conditions were analyzed. Our results indicated several cognitive changes in relation to task manipulation. Posterior N1 and P3 amplitudes revealed alterations associated with task complexity, with increased N1 and P3 amplitudes for the active condition, indicating greater cognitive effort than the passive condition. Increased frontal theta power and suppressed parietal alpha power were observed during the active condition reflecting high cognitive engagement. Additionally, higher theta connectivity was seen in fronto-parieto-centro-temporo-occipital regions as task demands increased, showing increased communication between brain regions. All of these results suggest using eye blink-related EEG activity to acquire a comprehensive understanding of neuro-cognitive processing while working in realistic environments.
... Therefore, it is reasonable that people tend to refrain from blinking when they expect relevant information to occur. Support for this comes from studies showing that blinks are more likely to occur once relevant visual information has been revealed (Nakano et al., 2009;Wascher et al., 2022). Recently, it has been shown that eye blinks also occur systematically in response to auditory stimuli. ...
... As has been argued before, eye blinks are more prevalent when the processing of a chunk of information has been completed (Wascher et al., 2022). The fact that we observed particularly more eye blinks during pauses in the attended speech stream and that the pauses most likely constituted breakpoints within speech provide further evidence for this view. ...
Eye blinks not only serve to maintain the tear film of the eye but also seem to have a functional role in information processing. People tend to inhibit an eye blink when they expect relevant information to occur in their visual environment and blink more often when the information has been processed. Recent studies have shown that this relation also holds for auditory information processing. However, only artificial auditory stimuli like tones or controlled lists of words were used in studies so far. In the current study, we tested whether there would be a temporal association between the pauses in a continuous speech stream and the listener’s eye blinks. To this end, we analyzed the eye blinks of 35 participants who were instructed to attend to one of two simultaneously presented audiobooks. We found that the blink patterns of 13 participants were coupled with the speech pauses in the attended speech stream. These participants blinked more often during the pauses in the attended speech stream. Contrary to our prediction, participants did not inhibit their blinking preceding a pause in the attended speech stream. As expected, there was no evidence that the listeners’ blink pattern was coupled to the pauses in the ignored speech stream. Thus, the listeners’ blink patterns can reflect attention to continuous speech.
... It is therefore reasonable that people tend to refrain from blinking when they expect relevant information to occur. For example, blinks are more likely to occur once relevant visual information has been revealed (Nakano et al., 2009;Wascher et al., 2022). Recently it has been shown that eye blinks also occur systematically in response to auditory stimuli. ...
... As has been argued before, eye blinks are more prevalent when the processing of a chunk of information has been completed (Wascher et al., 2022). The fact that we observed particularly more eye blinks during pauses in the attended speech stream and that the pauses most likely constituted breakpoint within speech provide further evidence for this view. ...
Eye blinks do not only serve to maintain the tear film of the eye but also seem to have a functional role in information processing. People tend to inhibit an eye blink when they expect relevant information to occur and blink more often when the information has been processed. Recent studies have shown that this relation also holds for auditory information processing. Yet so far, only artificial auditory stimuli like tones or controlled sentences were used. In the current study, we tested whether there is a temporal association between the pauses in a continuous speech stream and the listener’s eye blinks. To this end, we analyzed the eye blinks of 35 participants who were instructed to attended to one of two simultaneously presented audio books. We found that the blink patterns of 13 participants were coupled with the speech pauses in the attended speech stream. These participants blinked more often during the pauses in the attended speech stream. Contrary to our prediction, participants did not inhibit their blinking preceding a pause in the attended speech stream. As expected, there was no evidence that the listeners’ blink pattern was coupled to the pauses in the ignored speech stream. Thus, we conclude that the listeners’ blink patterns can reflect attention to continuous speech.
Recent developments in cognitive neuroscience have emphasized the use of naturalistic experimental paradigms, especially for real-world tasks like driving. This research introduced a blink-locked EEG segmentation method and contrasted its efficacy with traditional EEG segmentation. For three difficulty levels of proactive and reactive driving, we show a significant improvement in classification accuracy using a multi-classifier SVM with the blink-locked method, indicating enhancements of 4.3% for proactive driving and 4.4% for reactive driving. These findings underscore the potential of leveraging physiological markers, such as eye blinks, to enhance EEG data segmentation and deepen our understanding of cognitive dynamics in real-life scenarios.
