Momentary lapses of responsiveness frequently impair vigilance and sustained goal-directed behaviour, sometimes with serious consequences. The literature underpinning research into lapses of responsiveness has generally referred to these lapses as sustained attention lapses. Currently, this literature is divided between two competing theories. On one hand, there is the mindlessness theory and, on the other, the resource depletion theory. Mindlessness theorists propose that sustained attention lapses result from the subject disengaging from sustained tasks due to their monotony and low exogenous support for attention. Conversely, the resource depletion theorists propose that sustained attention lapses arise because demands for endogenus attentional resources outstrip supply, which leads to substantially delayed response and/or errors. In the present study, the predictions from the mindlessness and resource depletion theories were investigated by contrasting performance on attention tasks that differed in cognitive workloads. In the lesser demanding task, participants performed a simple psychomotor vigilance test (PVT). In the more demanding task, the PVT was undertaken concurrently with a continuous tracking task. The higher workload imposed by the dual task should reduce task monotony and the higher attentional requirement should increase the demand for attentional resources. If the mindlessness theory is correct the dual task should result in improved vigilance and reduce sustained attention lapses. If the resource theory is correct, the added attentional demand in the dual task should decrease vigilance and increase sustained attention lapses. However, there are other types of lapses that the literature has not always clearly separated from lapses of sustained attention. One such lapse is the microsleep. Microsleeps are brief periods of non-responsiveness (0.5–15 s) associated with overt signs of drowsiness. The two theories of vigilance impairment provide contrasting explanations in the traditional vigilance literature, but neither theory addresses lapses due to microsleep events, which remains largely ignored. Microsleeps are thought to emanate from a homeostatic drive for sleep/rest and a complex interaction between the brain’s arousal and attention systems and, therefore, depend on the type of task being undertaken to modulate propensity for microsleeps. For example, a more demanding and engaging task should counteract the homeostatic drive for sleep and rest by increasing arousal. If true, tasks that increase cognitive workloads may lead to a reduction in microsleeping propensity. We aimed to test the proposal that microsleep propensity is mediated by task by including in our study a continuous tracking task, which has previously been shown to elicit microsleeps. This task may, because of its consistency and repetitiveness, be considered a boring task. Moreover, it lacks any sudden stimulus onsets and, therefore, can be considered a less engaging task than the dual-task, which features sudden onsets. If more microsleeps were found in the tracking task compared to the dual task this would provide support for the proposition that a task-generated increase in mindlessness would increase microsleep rates. Conversely, if more microsleeps occur during the dual-task, then this suggests that factors other than mindlessness influence microsleeping. Twenty-three non-sleep deprived participants – 12 females and 11 males – with an average age of 26.3 years (range 21–40 years) and an average Epworth Sleepiness Score of 5.1 (range 0–10), completed the tasks during the early afternoon. They completed the two different tasks separately and concurrently (as a dual task), with the three conditions presented in a counterbalanced order. The PVT task was an extended 30-min version of the standard 10-min PVT used in many vigilance studies to match the duration of the continuous tracking task. In this task, the participant had to respond to a discrete randomly-presented visual stimulus. As per convention, failure to respond within 500 ms constituted an attention lapse. The 30-min continuous tracking task required the participant to use a floor-mounted joystick, to monitor and track a target randomly-moving on a computer screen. In this second task, lapses show as periods of flat tracking that, when associated with overt signs of sleepiness and at least 80 % partial eye-closure, are classified as microsleeps. The dual task was the PVT and tracking tasks being undertaken concurrently. Both sustained attention lapses and microsleep rates were affected by task differences. Using only the results from participants who had at least one sustained attention lapse in either the PVT or dual task (N = 23), it was found that a participant was more likely to experience a sustained attention lapse during the more demanding dual task then the PVT task (median 15 vs. 3; range 1–74 vs. 0–76, Wilcoxon z = 3.7, p = .001). Conversely, of those participants who had at least one microsleep in either the tracking or dual task (N = 12), they were more likely to experience a microsleep during the more monotonous tracking task than the dual task (median 0 vs. 0; range 0–18 vs. 0–1, Wilcoxon z = 2.3, p = .022). Time-on-task also had an effect. Sustained attention lapses increased with time-on-task during the PVT task and dual task (χ2 5, N 23 = 48.69, p = .001; and χ2 5, N 23 = 16.33, p = .006 respectively). Moreover, sustained attention lapses increased at a greater rate during the more cognitively demanding dual task (F5, 264 = 4.02, p = .002). Microsleeps also increased with time-on-task, but only during the tracking task and not during the dual task χ2 2, N 23 = 6.72, p = .035). The pattern of results supports the resource depletion theory over the mindlessness theory. When the cognitive workload increased, sustained attention lapses were more frequent. Conversely, the results also demonstrated that when the cognitive workload was decreased, the risk of lapsing due to microsleeps increased. Clarifying this relationship between cognitive workload and two types of lapses of responsiveness, sustained attention lapses and microsleeps, is important if we are to avoid inadvertently increasing lapses of responsiveness. Both sustained attention lapses and microsleeps can have serious real-life consequences and, therefore, any contribution towards a potent, preventative strategy is important.

Performance lapses in occupations where public safety is paramount can have disastrous consequences, resulting in accidents with multiple fatalities. Drowsy individuals performing an active task, like driving, often cycle rapidly between periods of wake and sleep, as exhibited by cyclical variation in both EEG power spectra and task performance measures. The aim of this project was to identify reliable physiological cues indicative of lapses, related to behavioural microsleep episodes, from the EEG, which could in turn be used to develop a real-time lapse detection (or better still, prediction) system. Additionally, the project also sought to achieve an increased understanding of the characteristics of lapses in responsiveness in normal subjects. A study was conducted to determine EEG and/or EOG cues (if any) that expert raters use to detect lapses that occur during a psychomotor vigilance task (PVT), with the subsequent goal of using these cues to design an automated system. A previously-collected dataset comprising physiological and performance data of 10 air traffic controllers (ATCs) was used. Analysis showed that the experts were unable to detect the vast majority of lapses based on EEG and EOG cues. This suggested that, unlike automated sleep staging, an automated lapse detection system needed to identify features not generally visible in the EEG. Limitations in the ATC dataset led to a study where more comprehensive physiological and performance data were collected from normal subjects. Fifteen non-sleep-deprived male volunteers aged 18-36 years were recruited. All performed a 1-D continuous pursuit visuomotor tracking task for 1 hour during each of two sessions that occurred between 1 and 7 weeks apart. A video camera was used to record head and facial expressions of the subject. EEG was recorded from electrodes at 16 scalp locations according to the 10-20 system at 256 Hz. Vertical and horizontal EOG was also recorded. All experimental sessions were held between 12:30 and 17:00 hours. Subjects were asked to refrain from consuming stimulants or depressants, for 4 h prior to each session. Rate and duration were estimated for lapses identified by a tracking flat spot and/or video sleep. Fourteen of the 15 subjects had one or more lapses, with an overall rate of 39.3 ± 12.9 lapses per hour (mean ± SE) and a lapse duration of 3.4 ± 0.5 s. The study also showed that lapsing and tracking error increased during the first 30 or so min of a 1-h session, then decreased during the remaining time, despite the absence of external temporal cues. EEG spectral power was found to be higher during lapses in the delta, theta, and alpha bands, and lower in the beta, gamma, and higher bands, but correlations between changes in EEG power and lapses were low. Thus, complete lapses in responsiveness are a frequent phenomenon in normal subjects - even when not sleep-deprived - undertaking an extended, monotonous, continuous visuomotor task. This is the first study to investigate and report on the characteristics of complete lapses of responsiveness during a continuous tracking task in non-sleep-deprived subjects. The extent to which non-sleep-deprived subjects experience complete lapses in responsiveness during normal working hours was unexpected. Such findings will be of major concern to individuals and companies in various transport sectors. Models based on EEG power spectral features, such as power in the traditional bands and ratios between bands, were developed to detect the change of brain state during behavioural microsleeps. Several other techniques including spectral coherence and asymmetry, fractal dimension, approximate entropy, and Lempel-Ziv (LZ) complexity were also used to form detection models. Following the removal of eye blink artifacts from the EEG, the signal was transformed into z-scores relative to the baseline of the signal. An epoch length of 2 s and an overlap of 1 s (50%) between successive epochs were used for all signal processing algorithms. Principal component analysis was used to reduce redundancy in the features extracted from the 16 EEG derivations. Linear discriminant analysis was used to form individual classification models capable of detecting lapses using data from each subject. The overall detection model was formed by combining the outputs of the individual models using stacked generalization with constrained least-squares fitting used to determine the optimal meta-learner weights of the stacked system. The performance of the lapse detector was measured both in terms of its ability to detect lapse state (in 1-s epochs) and lapse events. Best performance in lapse state detection was achieved using the detector based on spectral power (SP) features (mean correlation of φ = 0.39 ± 0.06). Lapse event detection performance using SP features was moderate at best (sensitivity = 73.5%, selectivity = 25.5%). LZ complexity feature-based detector showed the highest performance (φ = 0.28 ± 0.06) out of the 3 non-linear feature-based detectors. The SP+LZ feature-based model had no improvement in performance over the detector based on SP alone, suggesting that LZ features contributed no additional information. Alpha power contributed the most to the overall SP-based detection model. Analysis showed that the lapse detection model was detecting phasic, rather than tonic, changes in the level of drowsiness. The performance of these EEG-based lapse detection systems is modest. Further research is needed to develop more sensitive methods to extract cues from the EEG leading to devices capable of detecting and/or predicting lapses.