IntroductionTo understand brain function in natural real-world settings, it is crucial to acquire brain activity data in noisy environments with diverse artifacts. Electroencephalography (EEG), while susceptible to environmental and physiological artifacts, can be cleaned using advanced signal processing techniques like Artifact Subspace Reconstruction (ASR) and Independent Component Analysis (ICA). This study aims to demonstrate that ASR and ICA can effectively extract brain activity from the substantial artifacts occurring while skateboarding on a half-pipe ramp.MethodsA dual-task paradigm was used, where subjects were presented with auditory stimuli during skateboarding and rest conditions. The effectiveness of ASR and ICA in cleaning artifacts was evaluated using a support vector machine to classify the presence or absence of a sound stimulus in single-trial EEG data. The study evaluated the effectiveness of ASR and ICA in artifact cleaning using five different pipelines: (1) Minimal cleaning (bandpass filtering), (2) ASR only, (3) ICA only, (4) ICA followed by ASR (ICAASR), and (5) ASR preceding ICA (ASRICA). Three skateboarders participated in the experiment.ResultsResults showed that all ICA-containing pipelines, especially ASRICA (69%, 68%, 63%), outperformed minimal cleaning (55%, 52%, 50%) in single-trial classification during skateboarding. The ASRICA pipeline performed significantly better than other pipelines containing ICA for two of the three subjects, with no other pipeline performing better than ASRICA. The superior performance of ASRICA likely results from ASR removing non-stationary artifacts, enhancing ICA decomposition. Evidenced by ASRICA identifying more brain components via ICLabel than ICA alone or ICAASR for all subjects. For the rest condition, with fewer artifacts, the ASRICA pipeline (71%, 82%, 75%) showed slight improvement over minimal cleaning (73%, 70%, 72%), performing significantly better for two subjects.DiscussionThis study demonstrates that ASRICA can effectively clean artifacts to extract single-trial brain activity during skateboarding. These findings affirm the feasibility of recording brain activity during physically demanding tasks involving substantial body movement, laying the groundwork for future research into the neural processes governing complex and coordinated body movements.

Introduction The processes involved in how the attention system selectively focuses on perceptual and motor aspects related to a specific task, while suppressing features of other tasks and/or objects in the environment, are of considerable interest for cognitive neuroscience. The goal of this experiment was to investigate neural processes involved in selective attention and performance under multi-task situations. Several studies have suggested that attention-related gamma-band activity facilitates processing in task-specific modalities, while alpha-band activity inhibits processing in non-task-related modalities. However, investigations into the phenomenon of inattentional deafness/blindness (inability to observe stimuli in non-dominant task when primary task is demanding) have yet to observe gamma-band activity. Methods This EEG experiment utilizes an engaging whole-body perceptual motor task while carrying out a secondary auditory detection task to investigate neural correlates of inattentional deafness in natural immersive high workload conditions. Differences between hits and misses on the auditory detection task in the gamma (30–50 Hz) and alpha frequency (8–12 Hz) range were carried out at the cortical source level using LORETA. Results Participant auditory task performance correlated with an increase in gamma-band activity for hits over misses pre- and post-stimulus in left auditory processing regions. Alpha-band activity was greater for misses relative to hits in right auditory processing regions pre- and post-stimulus onset. These results are consistent with the facilitatory/inhibitory role of gamma/alpha-band activity for neural processing. Additional gamma- and alpha-band activity was found in frontal and parietal brain regions which are thought to reflect various attentional monitoring, selection, and switching processes. Discussion The results of this study help to elucidate the role of gamma and alpha frequency bands in frontal and modality-specific regions involved with selective attention in multi-task immersive situations.

The advancement of low-cost and highly portable physiological systems presents promising opportunities for monitoring human cognitive processes during daily-life activities and more complex tasks such as operating an aircraft. The Muse 2 system combines electroencephalography (EEG) and photoplethysmography (PPG) sensors allowing the extraction of neural dynamics features in the time and frequency domains and heart rate. In a study, we equipped five pilots with the Muse 2 system while they performed a low-load and high-load traffic pattern task along with a passive auditory oddball task. The group-level analyses revealed that participants exhibited higher average heart rate, lower power spectrum density in the alpha band, decreased P300 amplitude in the high-load compared to the low-load condition. These results are in line with previous laboratory research conducted in highly controlled settings and research-grade instrumentations. The classification of the two levels of mental workload reached 93.2% accuracy on a single-trial basis based on EEG frequency features. Post-hoc analysis revealed that the classifier mainly relied on motion artefact features in the beta and gamma bands. The classifiers using heart rate and ERPs features reached 76% and 77.8% classification accuracy, respectively. Despite its interest, this system presents some limitations for mobile and neuroergonomics applications notably with regards to the limited number of electrodes preventing the use of advanced signal processing techniques to address noise and artifacts in the signals.

There is a gap in our understanding of how best to apply transcranial direct-current stimulation (tDCS) to enhance learning in complex, realistic, and multifocus tasks such as aviation. Our goal is to assess the effects of tDCS and feedback training on task performance, brain activity, and connectivity using functional magnetic resonance imaging (fMRI). Experienced glider pilots were recruited to perform a one-day, three-run flight-simulator task involving varying difficulty conditions and a secondary auditory task, mimicking real flight requirements. The stimulation group (versus sham) received 1.5 mA high-definition HD-tDCS to the right dorsolateral prefrontal cortex (DLPFC) for 30 min during the training. Whole-brain fMRI was collected before, during, and after stimulation. Active stimulation improved piloting performance both during and post-training, particularly in novice pilots. The fMRI revealed a number of tDCS-induced effects on brain activation, including an increase in the left cerebellum and bilateral basal ganglia for the most difficult conditions, an increase in DLPFC activation and connectivity to the cerebellum during stimulation, and an inhibition in the secondary task-related auditory cortex and Broca's area. Here, we show that stimulation increases activity and connectivity in flight-related brain areas, particularly in novices, and increases the brain's ability to focus on flying and ignore distractors. These findings can guide applied neurostimulation in real pilot training to enhance skill acquisition and can be applied widely in other complex perceptual-motor real-world tasks.

Auditory feedback of one’s own speech is used to monitor and adaptively control fluent speech production. A new study in PLOS Biology using electrocorticography (ECoG) in listeners whose speech was artificially delayed identifies regions involved in monitoring speech production.

The present fMRI study aimed at highlighting patterns of brain activations and autonomic activity when confronted with high mental workload and the threat of auditory stressors. Twenty participants performed a complex cognitive task in either safe or aversive conditions. Our results showed that increased mental workload induced recruitment of the lateral frontoparietal executive control network (ECN), along with disengagement of medial prefrontal and posterior cingulate regions of the default mode network (DMN). Mental workload also elicited an increase in heart rate and pupil diameter. Task performance did not decrease under the threat of stressors, most likely due to efficient inhibition of auditory regions, as reflected by a large decrement of activity in the superior temporal gyri. The threat of stressors was also accompanied with deactivations of limbic regions of the salience network (SN), possibly reflecting emotional regulation mechanisms through control from dorsal medial prefrontal and parietal regions, as indicated by functional connectivity analyses. Meanwhile, the threat of stressors induced enhanced ECN activity, likely for improved attentional and cognitive processes toward the task, as suggested by increased lateral prefrontal and parietal activations. These fMRI results suggest that measuring the balance between ECN, SN, and DMN recruitment could be used for objective mental state assessment. In this sense, an extra recruitment of task-related regions and a high ratio of lateral versus medial prefrontal activity may represent a relevant marker of increased but efficient mental effort, while the opposite may indicate a disengagement from the task due to mental overload and/or stressors.

Mental overload and mental fatigue are two degraded cognitive states that are known to promote cognitive incapacitation. We adopted a neuroergonomics approach to investigate these states that remain difficult to induce under laboratory settings thus impeding their measurement. Two experiments were conducted under real flight conditions to respectively measure the electrophysiological correlates of mental fatigue and mental overload with a 32 channel-dry EEG system. Our findings revealed that the occurrence of mental fatigue was related to higher theta and alpha band power. Mental overload was associated with higher beta band power over frontal sites. We performed single trial classification to detect mental fatigue and over-load states. Classification accuracy reached 76.9% and 89.1%, respectively, in discriminating mental fatigue vs. no fatigue and mental overload vs. low-high load. These preliminary results provide evidence for the feasibility of detecting neural correlates of cognitive fatigue and load during real flight conditions and provide promising perspectives on the implementation of neuroadaptive technology especially in the context of single pilot-operation.

The affordance competition hypothesis is an ethologically inspired theory from cognitive neuroscience that provides an integrative neural account of continuous, real-time behavior, and will likely become increasingly relevant to the growing field of neuroergonomics. In the spirit of neuroergonomics in aviation, we designed a three-dimensional, first-person, continuous, and real-time fMRI task during which human subjects maneuvered a simulated airplane in pursuit of a target airplane along constantly changing headings. We introduce a pseudo-event-related, parametric fMRI analysis approach to begin testing the affordance competition hypothesis in neuroergonomic contexts, and attempt to identify regions of the brain that exhibit a linear metabolic relationship with the continuous variables of task performance and distance-from-target. In line with the affordance competition hypothesis, our results implicate the cooperation of the cerebellum, basal ganglia, and cortex in such a task, with greater involvement of the basal ganglia during good performance, and greater involvement of cortex and cerebellum during poor performance and when distance-from-target closes. We briefly review the somatic marker and dysmetria of thought hypotheses, in addition to the affordance competition hypothesis, to speculate on the intricacies of the cooperation of these brain regions in a task such as ours. In doing so, we demonstrate how the affordance competition hypothesis and other cognitive neuroscience theories are ready for testing in continuous, real-time tasks such as ours, and in other neuroergonomic settings more generally.

The field of enhancing skill acquisition, particularly in professions necessitating the mastery of a complex combination of physical and mental abilities, is rapidly progressing and amenable to novel training protocols involving both neuroimaging and neurostimulation. Aircraft piloting in particular is an ideal medium for testing new training protocols, because objective performance measures are well-understood and modern flight simulator programs are realistic and high-fidelity. Here, we describe the development of a flight simulator protocol that allows for the analysis of neurostimulation-enhanced skill acquisition both within and between subjects. A three-block design was created to collect data pre-training, during feedback training, and post-training while being recorded in an fMRI. The dual task consists of 30–45 s trials landing a plane on one of two runways, indicated by an arrow displayed on the simulator screen, while simultaneously responding to auditory stimuli played constantly during each trial with button presses. The landing task is presented at two difficulty levels in pseudorandom balanced order, modulated by wind speed and direction. Two auditory conditions, response and control (no response), are used for a two by two design. For the feedback training, subjects are provided with relevant measures of how well they are able to land on the specified runway as well as their accuracy in the auditory task. Subjects will be randomly assigned to tDCS stimulation or sham groups, with stim receiving 30 min of 1.5 mA high definition-tDCS to the right ventrolateral prefrontal cortex during the training block. Altogether, this novel combination of stimulation, neuroimaging, and dual-task training will allow for an in-depth, multi-factor analysis of cognitive workload, behavioral performance, neurostimulation effects, and learning of a complex mental and physical task.

This chapter reviews recent research combining transcranial direct current stimulation (tDCS) and functional magnetic resonance imaging (fMRI) to investigate the neural processes modulated by tDCS that are related to induced enhancement in behavioral performance. Both the effects during tDCS ("active-effects") and those immediately following tDCS ("after-effects") on brain activity and connectivity recorded by fMRI are discussed. Modulation of task-related brain activity and resting-state brain activity and connectivity by tDCS is dependent on many factors, including the nature of the task and whether learning has occurred. Experiments using more complex tasks need to be conducted to see how well these results generalize to real-world situations.

Neuroergonomics is the study of how the brain functions in real-world situations with the goal of developing technology to enhance human performance. Neuroergonomics constitutes a paradigm shift away from the standard reductionist approach to neuroscience. The neuroergonomic approach maintains that an understanding of neural processes underlying human behavior can best be understood by investigating the underlying interacting brain networks in the context of carrying out various real-world tasks under investigation, rather than under reduced isolated conditions that only occur in the laboratory. In this chapter we discuss why aviation is an ideal paradigm to implement this neuroergonomic approach. By using a combination of high-resolution and lower-resolution portable brain-imaging techniques, as well as noninvasive brain stimulation, the goal of this research is to determine brain processes underlying complex behavior during aviation and space operations such that neuroergonomic technology can be developed to improve human performance.

Accident analyses have revealed that pilots can fail to process auditory stimuli such as alarms, a phenomenon known as inattentional deafness. The motivation of this research is to develop a passive brain computer interface that can predict the occurence of this critical phenomenon during real flight conditions. Ten volunteers, equipped with a dry-EEG system, had to fly a challenging flight scenario while responding to auditory alarms by button press. The behavioral results disclosed that the pilots missed 36% of the auditory alarms. ERP analyses confirm that this phenomenon affects auditory processing at an early (N100) and late (P300) stages as the consequence of a potential attentional bottleneck mechanism. Intersubject classification was carried out over frequency features extracted three second epochs before the alarms’ onset using sparse representation for classification (SRC), sparse and dense representation (SDR) and more conventional approach such as linear discriminant analysis (LDA), shrinkage LDA and nearest neighbor (1NN). In the best case, SRC and SDR gave respectively a performance of 66.9% and 65.4% of correct mean classification rate to predict the occurrence of inattentional deafness, outperforming LDA (60.6%), sLDA (60%) and 1 NN (59.6%). These results open promising perspectives for the implementation of neuroadaptive automation with as ultimate goal to enhance alarm stimulation delivery so that it is perceived and acted upon.

Neuroergonomics is the study of how the brain functions in real-world situations with the goal of developing technology to enhance human performance. Neuroergonomics constitutes a paradigm shift away from the standard reductionist approach to neuroscience. The neuroergonomic approach maintains that an understanding of neural processes underlying human behavior can best be understood by investigating the underlying interacting brain networks in the context of carrying out various realworld tasks under investigation, rather than under reduced isolated conditions that only occur in the laboratory. In this chapter we discuss why aerospace cerebral experimental sciences (ACES) is an ideal paradigm to implement this neuroergonomic approach. By using a combination of high resolution and lower resolution portable brain imaging techniques as well as non-invasive brain stimulation the goal of ACES is to determine brain processes underlying complex behavior during aviation and space operations such that neuroergonomic technology can be developed to improve human performance.

This study expores neural activity underlying creative processes through the investigation of music improvisation. Fourteen guitar players with a high level of improvisation skill participated in this experiment. The experimental task involved playing 32-s alternating blocks of improvisation and scales on guitar. electroencephalography (EEG) data was measured continuously throughout the experiment. In order to remove potential artifacts and extract brain-related activity the following signal processing techniques were employed: bandpass filtering, Artifact Subspace Reconstruction, and Independent Component Analysis (ICA). For each participant, artifact related independent components (ICs) were removed from the EEG data and only ICs found to be from brain activity were retained. Source localization using this brain-related activity was carried out using sLORETA. Greater activity for improvisation over scale was found in multiple frequency bands (theta, alpha, and beta) localized primarily in the medial frontal cortex (MFC), Middle frontal gyrus (MFG), anterior cingulate, polar medial prefrontal cortex (MPFC), premotor cortex (PMC), pre and postcentral gyrus (PreCG and PostCG), superior temporal gyrus (STG), inferior parietal lobule (IPL), and the temporal-parietal junction. Together this collection of brain regions suggests that improvisation was mediated by processes involved in coordinating planned sequences of movement that are modulated in response to ongoing environmental context through monitoring and feedback of sensory states in relation to internal plans and goals. Machine-learning using Common Spatial Patterns (CSP) for EEG feature extraction attained a mean of over 75% classification performance for improvisation vs. scale conditions across participants. These machine-learning results are a step towards the development of a brain-computer interface that could be used for neurofeedback training to improve creativity.

Individuals often have reduced ability to hear alarms in real world situations (e.g., anesthesia monitoring, flying airplanes) when attention is focused on another task, sometimes with devastating consequences. This phenomenon is called inattentional deafness and usually occurs under critical high workload conditions. It is difficult to simulate the critical nature of these tasks in the laboratory. In this study, dry electroencephalography is used to investigate inattentional deafness in real flight while piloting an airplane. The pilots participating in the experiment responded to audio alarms while experiencing critical high workload situations. It was found that missed relative to detected alarms were marked by reduced stimulus evoked phase synchrony in theta and alpha frequencies (6-14 Hz) from 120 to 230 ms poststimulus onset. Correlation of alarm detection performance with intertrial coherence measures of neural phase synchrony showed different frequency and time ranges for detected and missed alarms. These results are consistent with selective attentional processes actively disrupting oscillatory coherence in sensory networks not involved with the primary task (piloting in this case) under critical high load conditions. This hypothesis is corroborated by analyses of flight parameters showing greater maneuvering associated with difficult phases of flight occurring during missed alarms. Our results suggest modulation of neural oscillation is a general mechanism of attention utilizing enhancement of phase synchrony to sharpen alarm perception during successful divided attention, and disruption of phase synchrony in brain networks when attentional demands of the primary task are great, such as in the case of inattentional deafness.

Music is known to be a powerful elicitor of emotions. Here, we examined the possibility of decoding music-induced experienced emotions - affective responses induced in the listener by music stimuli - using functional magnetic resonance imaging (fMRI) data collected while participants listened to various movie soundtrack excerpts. Using a machine learning based classification framework, preliminary results indicate that it is possible to, under certain conditions, decode the arousal (High/Low) and valence (Positive/Negative) of the emotional state experienced during listening. Future work will be aimed at improving methodological issues in order to establish whether these results extend to larger populations and more general types of music stimuli.