EEG alpha band dissociation with increasing task demands

The Impact of Various Cockpit Display Interfaces on Novice Pilots' Mental Workload and Situational Awareness: A Comparative Study

Future airspace is expected to become more congested with additional in-service cargo and commercial flights. Pilots will face additional burdens in such an environment, given the increasing number of factors that they must simultaneously consider while completing their work activities. Therefore, care and attention must be paid to the mental workload (MWL) experienced by operating pilots. If left unaddressed, a state of mental overload could affect the pilot's ability to complete his or her work activities in a safe and correct manner. This study examines the impact of two different cockpit display interfaces (CDIs), the Steam Gauge panel and the G1000 Glass panel, on novice pilots' MWL and situational awareness (SA) in a flight simulator-based setting. A combination of objective (EEG and HRV) and subjective (NASA-TLX) assessments is used to assess novice pilots' cognitive states during this study. Our results indicate that the gauge design of the CDI affects novice pilots' SA and MWL, with the G1000 Glass panel being more effective in reducing the MWL and improving SA compared with the Steam Gauge panel. The results of this study have implications for the design of future flight deck interfaces and the training of future pilots.

Evaluation of mental load using EEG and eye movement characteristics

Mental load is a major cause of human-induced accidents. In this study, an explosive impact sensitivity experiment was used to induce mental load. A combination of subjective questionnaires and objective prospective time-distance tests were used to judge whether subjects experienced mental load. Four indicators, namely, β, γ, mean pupil diameter, and fixation time were selected by statistical analysis and PCA for the construction of a mental load assessment model. The study found that the occipital lobe was the most sensitive to mental load, especially β and γ bands. Lastly, it was found that subjects showed different degrees of mental load for the same mental load induction task. The results of the study are applicable to the evaluation and monitoring of the mental characteristics of workers and provide a scientific basis for adjusting the mental load of workers over time to reduce the rate of accidents and enhance production efficiency.

The Importance of Low-frequency Alpha (8-10 Hz) Waves and Default Mode Network in Behavioral Inhibition

Objective

: Alpha wave of electroencephalography (EEG) is known to be related to behavioral inhibition. Both the alpha wave and default mode network (DMN) are predominantly activated during resting-state. To study the mechanisms of the trait inhibition, this research investigating the relations among alpha wave, DMN and behavioral inhibition in resting-state.

Methods

: We explored the relationship among behavioral inhibition, resting-state alpha power, and DMN. Resting-state EEG, behavioral inhibition/behavioral activation scale (BIS/BAS), Barratt impulsivity scale, and no-go accuracy were assessed in 104 healthy individuals. Three groups (i.e., participants with low/middle/high band power) were formed based on the relative power of each total-alpha, low-alpha (LA), and high-alpha band. Source-reconstructed EEG and functional network measures of 25 DMN regions were calculated.

Results

: Significant differences and correlations were found based on LA band power alone. The high LA group had significantly greater BIS, clustering coefficient, efficiency, and strength, and significantly lower path length than low/middle LA group. BIS score showed a significant correlation with functional network measures of DMN.

Conclusion

: Our study revealed that LA power is related to behavioral inhibition and functional network measures of DMN of LA band appear to represent significant inhibitory function.

Effective utilization of attentional resources in postural control in athletes of skill-oriented sports: an event-related potential study

Objective

Postural control plays a key role in skill-oriented sports. Athletes of skill-oriented sports (hereinafter referred to as "skilled athletes") usually showed better control ability compared with non-athletes. However, research focused on the single postural task, rarely considering the actual situation in skill-oriented sports in which other processes, such as cognitive control, frequently accompany postural control. This study aims to explore how skilled athletes control their posture under the dual-task situation and use limited attentional resources.

Method

A total of 26 skilled athletes and 26 non-athletes were required to perform the postural control and N-back tasks simultaneously. Center of pressure (COP) trajectory, reaction times (RTs), and discriminability (d') of N-back tasks were recorded and evaluated, along with event-related potentials, including N1 (Oz, PO7, and PO8), P2 (Fz, FCz, Cz, and Pz) components, and the spectral power of alpha band.

Results

Skilled athletes demonstrated more postural control stability and a higher d' than non-athletes in all dual tasks. Besides, they showed enhanced N1, P2 amplitudes and reduced alpha band power during dual-tasking. Notably, in skilled athletes, a significant negative correlation between N1 amplitude and d' was observed, while significant positive correlations between alpha band power and postural control performance were also identified.

Conclusion

This study investigates the potential advantages of skilled athletes in postural control from the view of neuroscience. Compared to non-athletes, skilled athletes could decrease the consumption of attentional resources in postural control and recruit more attentional resources in stimulus discrimination and evaluation in cognitive tasks. Since the allocation of attentional resources plays a crucial part in postural control in skilled athletes, optimizing the postural control training program and the selection of skilled athletes from a dual-task perspective is important.

Scientific Problem Solving and Brain Symmetry Index: An exploratory EEG study

Scientific problem solving has recently attracted fixation in the field of cognitive neuroscience and science education. Until now, there is very little evidence on the brain dynamics of scientific problem-solving processes. In this study, we were interested in exploring whether Brain Symmetry Index (BSI) would be an EEG index to reveal neural information. The present EEG study used two levels of complexity physics problems to research the neural mechanism of problem solving. Results indicated that relatively greater BSI found during difficult problem solving on the prefrontal theta and beta band. However, smaller BSI on the occipital alpha was found when solving difficult problems. There was no significant relationship between BSI and self-effort evaluation. The current study proposes an EEG index - BSI to reflect underlying brain functional characteristic.

Effects of spaceflight on the EEG alpha power and functional connectivity

Electroencephalography (EEG) can detect changes in cerebral activity during spaceflight. This study evaluates the effect of spaceflight on brain networks through analysis of the Default Mode Network (DMN)'s alpha frequency band power and functional connectivity (FC), and the persistence of these changes. Five astronauts' resting state EEGs under three conditions were analyzed (pre-flight, in-flight, and post-flight). DMN's alpha band power and FC were computed using eLORETA and phase-locking value. Eyes-opened (EO) and eyes-closed (EC) conditions were differentiated. We found a DMN alpha band power reduction during in-flight (EC: p < 0.001; EO: p < 0.05) and post-flight (EC: p < 0.001; EO: p < 0.01) when compared to pre-flight condition. FC strength decreased during in-flight (EC: p < 0.01; EO: p < 0.01) and post-flight (EC: ns; EO: p < 0.01) compared to pre-flight condition. The DMN alpha band power and FC strength reduction persisted until 20 days after landing. Spaceflight caused electrocerebral alterations that persisted after return to earth. Periodic assessment by EEG-derived DMN analysis has the potential to become a neurophysiologic marker of cerebral functional integrity during exploration missions to space.

Dynamic fluctuations in ascending heart-to-brain communication under mental stress

Dynamical information exchange between central and autonomic nervous systems, as referred to functional brain-heart interplay, occurs during emotional and physical arousal. It is well documented that physical and mental stress lead to sympathetic activation. Nevertheless, the role of autonomic inputs in nervous system-wise communication under mental stress is yet unknown. In this study, we estimated the causal and bidirectional neural modulations between electroencephalogram (EEG) oscillations and peripheral sympathetic and parasympathetic activities using a recently proposed computational framework for a functional brain-heart interplay assessment, namely the sympathovagal synthetic data generation model. Mental stress was elicited in 37 healthy volunteers by increasing their cognitive demands throughout three tasks associated with increased stress levels. Stress elicitation induced an increased variability in sympathovagal markers, as well as increased variability in the directional brain-heart interplay. The observed heart-to-brain interplay was primarily from sympathetic activity targeting a wide range of EEG oscillations, whereas variability in the efferent direction seemed mainly related to EEG oscillations in the γ band. These findings extend current knowledge on stress physiology, which mainly referred to top-down neural dynamics. Our results suggest that mental stress may not cause an increase in sympathetic activity exclusively as it initiates a dynamic fluctuation within brain-body networks including bidirectional interactions at a brain-heart level. We conclude that directional brain-heart interplay measurements may provide suitable biomarkers for a quantitative stress assessment and bodily feedback may modulate the perceived stress caused by increased cognitive demand.

Person-identifying brainprints are stably embedded in EEG mindprints

Electroencephalography (EEG) signals measured under fixed conditions have been exploited as biometric identifiers. However, what contributes to the uniqueness of one's brain signals remains unclear. In the present research, we conducted a multi-task and multi-week EEG study with ten pairs of monozygotic (MZ) twins to examine the nature and components of person-identifiable brain signals. Through machine-learning analyses, we uncovered a person-identifying EEG component that served as "base signals" shared across tasks and weeks. Such task invariance and temporal stability suggest that these person-identifying EEG characteristics are more of structural brainprints than functional mindprints. Moreover, while these base signals were more similar within than between MZ twins, it was still possible to distinguish twin siblings, particularly using EEG signals coming primarily from late rather than early developed areas in the brain. Besides theoretical clarifications, the discovery of the EEG base signals has practical implications for privacy protection and the application of brain-computer interfaces.

Danger, high voltage! Using EEG and EOG measurements for cognitive overload detection in a simulated industrial context

Industrial settings will be characterized by far-reaching production automation brought about by advancements in robotics and artificial intelligence. As a consequence, human assembly workers will need to adapt quickly to new and more complex assembly procedures, which are most likely to increase cognitive workload, or potentially induce overload. Measurement and optimization protocols need to be developed in order to be able to monitor workers' cognitive load. Previous studies have used electroencephalographic (EEG, measuring brain activity) and electrooculographic (EOG, measuring eye movements) signals, using basic computer-based static tasks and without creating an experience of overload. In this study, EEG and EOG data was collected of 46 participants performing an ecologically valid assembly task while inducing three levels of cognitive load (low, high and overload). The lower individual alpha frequency (IAF) was identified as a promising marker for discriminating between different levels of cognitive load and overload.

Visual Demands of Walking Are Reflected in Eye-Blink-Evoked EEG-Activity

Blinking is a natural user-induced response which paces visual information processing. This study investigates whether blinks are viable for segmenting continuous electroencephalography (EEG) activity, for inferring cognitive demands in ecologically valid work environments. We report the blink-related EEG measures of participants who performed auditory tasks either standing, walking on grass, or whilst completing an obstacle course. Blink-related EEG activity discriminated between different levels of cognitive demand during walking. Both behavioral parameters (e.g., blink duration or head motion) and blink-related EEG activity varied with walking conditions. Larger occipital N1 was observed during walking, relative to standing and traversing an obstacle course, which reflects differences in bottom-up visual perception. In contrast, the amplitudes of top-down components (N2, P3) significantly decreased with increasing walking demands, which reflected narrowing attention. This is consistent with blink-related EEG, specifically in Theta and Alpha power that, respectively, increased and decreased with increasing demands of the walking task. This work presents a novel and robust analytical approach to evaluate the cognitive demands experienced in natural work settings, which precludes the use of artificial task manipulations for data segmentation.

How stereotype threat affects the brain dynamics of creative thinking in female students

When people are placed in a situation where they are at risk of substantiating a negative stereotype about their social group (a scenario termed stereotype threat), the extra pressure to avoid this outcome can undermine their performance. Substantial and consistent gender disparities in STEM fields leave women vulnerable to stereotype threat, including the stereotype that women are not as good at generating creative and innovative ideas as men. We tested whether female students' creative thinking is affected by a stereotype threat by measuring power in the alpha frequency band (8-12Hz oscillations) that has been associated with better creative thinking outcomes. Counter to expectations that a stereotype threat would reduce alpha power associated with creative thinking, analyses showed increased alpha power following the introduction of the stereotype threat. This outcome suggests that women may have attempted to increase their internal attention during the task in order to disprove the stereotype. Behaviorally, this effort did not lead to changes in creative performance, suggesting that the stereotype threat decoupled alpha power from creative thinking outcomes. These results support a growing school of thought in the neuroscience of creativity literature that the alpha power often seen in conjunction with creative behavior is not necessarily related to the creativity processes themselves, but rather might be part of a larger network modulating the distribution of attentional resources more broadly.

Design Meets Neuroscience: An Electroencephalogram Study of Design Thinking in Concept Generation Phase

Extant research on design thinking is subjective and limited. This manuscript combines protocol analysis and electroencephalogram (EEG) to read design thoughts in the core design activities of concept generation phase. The results suggest that alpha band power had event related synchronization (ERS) in the scenario task and divergent thinking occupies a dominant position. However, it had event related desynchronization (ERD) in analogy and inference activities, etc., and it is stronger for mental pressure and exercised cognitive processing. In addition, the parietooccipital area differs significantly from other brain areas in most design activities. This study explores the relationship of different design thinking and EEG data, which is innovative and professional in the field of design, providing a more objective data basis and evaluation method for future applied research and diverse educational practices.

Network oscillations imply the highest cognitive workload and lowest cognitive control during idea generation in open-ended creation tasks

Design is a ubiquitous, complex, and open-ended creation behaviour that triggers creativity. The brain dynamics underlying design is unclear, since a design process consists of many basic cognitive behaviours, such as problem understanding, idea generation, idea analysis, idea evaluation, and idea evolution. In this present study, we simulated the design process in a loosely controlled setting, aiming to quantify the design-related cognitive workload and control, identify EEG-defined large-scale brain networks, and uncover their temporal dynamics. The effectiveness of this loosely controlled setting was tested through comparing the results with validated findings available in the literature. Task-related power (TRP) analysis of delta, theta, alpha and beta frequency bands revealed that idea generation was associated with the highest cognitive workload and lowest cognitive control, compared to other design activities in the experiment, including problem understanding, idea evaluation, and self-rating. EEG microstate analysis supported this finding as microstate class C, being negatively associated with the cognitive control network, was the most prevalent in idea generation. Furthermore, EEG microstate sequence analysis demonstrated that idea generation was consistently associated with the shortest temporal correlation times concerning finite entropy rate, autoinformation function, and Hurst exponent. This finding suggests that during idea generation the interplay of functional brain networks is less restricted and the brain has more degrees of freedom in choosing the next network configuration than during other design activities. Taken together, the TRP and EEG microstate results lead to the conclusion that idea generation is associated with the highest cognitive workload and lowest cognitive control during open-ended creation task.

Study of EEG characteristics while solving scientific problems with different mental effort

Studying the mental effort in problem-solving is important to the understanding of how the brain allocates cognitive resources to process information. The electroencephalogram is a promising physiological approach to assessing the online mental effort. In this study, we investigate the EEG indicators of mental effort while solving scientific problems. By manipulating the complexity of the scientific problem, the level of mental effort also changes. With the increase of mental effort, theta synchronization in the frontal region and lower alpha desynchronization in the parietal and occipital regions significantly increase. Also, upper alpha desynchronization demonstrates a widespread enhancement across the whole brain. According to the functional topography of brain activity in the theta and alpha frequency, our results suggest that the mental effort while solving scientific problems is related to working memory, visuospatial processing, semantic processing and magnitude manipulation. This study suggests the reliability of EEG to evaluate the mental effort in an educational context and provides valuable insights into improving the problem-solving abilities of students in educational practice.

Advanced Bioelectrical Signal Processing Methods: Past, Present and Future Approach-Part II: Brain Signals

As it was mentioned in the previous part of this work (Part I)-the advanced signal processing methods are one of the quickest and the most dynamically developing scientific areas of biomedical engineering with their increasing usage in current clinical practice. In this paper, which is a Part II work-various innovative methods for the analysis of brain bioelectrical signals were presented and compared. It also describes both classical and advanced approaches for noise contamination removal such as among the others digital adaptive and non-adaptive filtering, signal decomposition methods based on blind source separation, and wavelet transform.

Predicting Human Intention-Behavior Through EEG Signal Analysis Using Multi-Scale CNN

At present, the application of Electroencephalogram (EEG) signal classification to human intention-behavior prediction has become a hot topic in the brain computer interface (BCI) research field. In recent studies, the introduction of convolutional neural networks (CNN) has contributed to substantial improvements in the EEG signal classification performance. However, there is still a key challenge with the existing CNN-based EEG signal classification methods, the accuracy of them is not very satisfying. This is because most of the existing methods only utilize the feature maps in the last layer of CNN for EEG signal classification, which might miss some local and detailed information for accurate classification. To address this challenge, this paper proposes a multi-scale CNN model-based EEG signal classification method. In this method, first, the EEG signals are preprocessed and converted to time-frequency images using the short-time Fourier Transform (STFT) technique. Then, a multi-scale CNN model is designed for EEG signal classification, which takes the converted time-frequency image as the input. Especially, in the designed multi-scale CNN model, both the local and global information is taken into consideration. The performance of the proposed method is verified on the benchmark data set 2b used in the BCI contest IV. The experimental results show that the average accuracy of the proposed method is 73.9 percent, which improves the classification accuracy of 10.4, 5.5, 16.2 percent compared with the traditional methods including artificial neural network, support vector machine, and stacked auto-encoder.

Motivation-Achievement Cycles in Learning: a Literature Review and Research Agenda

The question of how learners’ motivation influences their academic achievement and vice versa has been the subject of intensive research due to its theoretical relevance and important implications for the field of education. Here, we present our understanding of how influential theories of academic motivation have conceptualized reciprocal interactions between motivation and achievement and the kinds of evidence that support this reciprocity. While the reciprocal nature of the relationship between motivation and academic achievement has been established in the literature, further insights into several features of this relationship are still lacking. We therefore present a research agenda where we identify theoretical and methodological challenges that could inspire further understanding of the reciprocal relationship between motivation and achievement as well as inform future interventions. Specifically, the research agenda includes the recommendation that future research considers (1) multiple motivation constructs, (2) behavioral mediators, (3) a network approach, (4) alignment of intervals of measurement and the short vs. long time scales of motivation constructs, (5) designs that meet the criteria for making causal, reciprocal inferences, (6) appropriate statistical models, (7) alternatives to self-reports, (8) different ways of measuring achievement, and (9) generalizability of the reciprocal relations to various developmental, ethnic, and sociocultural groups.

Alpha and beta neural oscillations differentially reflect age-related differences in bilateral coordination

Bilateral in-phase (IP) and anti-phase (AP) movements represent two fundamental modes of bilateral coordination that are essential for daily living. Although previous studies have shown that aging is behaviorally associated with decline in bilateral coordination, especially in AP movements, the underlying neural mechanisms remain unclear. Here, we use kinematic measurements and electroencephalography to compare motor performance of young and older adults executing bilateral IP and AP hand movements. On the behavioral level, inter-limb synchronization was reduced during AP movements compared to IP and this reduction was stronger in the older adults. On the neural level, we found interactions between group and condition for task-related power change in different frequency bands. The interaction was driven by smaller alpha power decreases over the non-dominant cortical motor area in young adults during IP movements and larger beta power decreases over the midline region in older adults during AP movements. In addition, the decrease in inter-limb synchronization during AP movements was predicted by stronger directional connectivity in the beta-band: an effect more pronounced in older adults. Our results therefore show that age-related differences in the two bilateral coordination modes are reflected on the neural level by differences in alpha and beta oscillatory power as well as interhemispheric directional connectivity.

Applications of EEG indices for the quantification of human cognitive performance: A systematic review and bibliometric analysis

BACKGROUND

Neuroergonomics combines neuroscience with ergonomics to study human performance using recorded brain signals. Such neural signatures of performance can be measured using a variety of neuroimaging techniques, including functional magnetic resonance imaging (fMRI), functional near-infrared spectroscopy (fNIRS), and electroencephalography (EEG). EEG has an excellent temporal resolution, and EEG indices are highly sensitive to human brain activity fluctuations.

OBJECTIVE

The focus of this systematic review was to explore the applications of EEG indices for quantifying human performance in a variety of cognitive tasks at the macro and micro scales. To identify trends and the state of the field, we examined global patterns among selected articles, such as journal contributions, highly cited papers, affiliations, and high-frequency keywords. Moreover, we discussed the most frequently used EEG indices and synthesized current knowledge regarding the EEG signatures of associated human performance measurements.

METHODS

In this systematic review, we analyzed articles published in English (from peer-reviewed journals, proceedings, and conference papers), Ph.D. dissertations, textbooks, and reference books. All articles reviewed herein included exclusively EEG-based experimental studies in healthy participants. We searched Web-of-Science and Scopus databases using specific sets of keywords.

RESULTS

Out of 143 papers, a considerable number of cognitive studies focused on quantifying human performance with respect to mental fatigue, mental workload, mental effort, visual fatigue, emotion, and stress. An increasing trend for publication in this area was observed, with the highest number of publications in 2017. Most studies applied linear methods (e.g., EEG power spectral density and the amplitude of event-related potentials) to evaluate human cognitive performance. A few papers utilized nonlinear methods, such as fractal dimension, largest Lyapunov exponent, and signal entropy. More than 50% of the studies focused on evaluating an individual's mental states while operating a vehicle. Several different methods of artifact removal have also been noted. Based on the reviewed articles, research gaps, trends, and potential directions for future research were explored.

CONCLUSION

This systematic review synthesized current knowledge regarding the application of EEG indices for quantifying human performance in a wide variety of cognitive tasks. This knowledge is useful for understanding the global patterns of applications of EEG indices for the analysis and design of cognitive tasks.

Differences in Power Spectral Densities and Phase Quantities Due to Processing of EEG Signals

There has been a growing interest in computational electroencephalogram (EEG) signal processing in a diverse set of domains, such as cortical excitability analysis, event-related synchronization, or desynchronization analysis. In recent years, several inconsistencies were found across different EEG studies, which authors often attributed to methodological differences. However, the assessment of such discrepancies is deeply underexplored. It is currently unknown if methodological differences can fully explain emerging differences and the nature of these differences. This study aims to contrast widely used methodological approaches in EEG processing and compare their effects on the outcome variables. To this end, two publicly available datasets were collected, each having unique traits so as to validate the results in two different EEG territories. The first dataset included signals with event-related potentials (visual stimulation) from 45 subjects. The second dataset included resting state EEG signals from 16 subjects. Five EEG processing steps, involved in the computation of power and phase quantities of EEG frequency bands, were explored in this study: artifact removal choices (with and without artifact removal), EEG signal transformation choices (raw EEG channels, Hjorth transformed channels, and averaged channels across primary motor cortex), filtering algorithms (Butterworth filter and Blackman-Harris window), EEG time window choices (-750 ms to 0 ms and -250 ms to 0 ms), and power spectral density (PSD) estimation algorithms (Welch's method, Fast Fourier Transform, and Burg's method). Powers and phases estimated by carrying out variations of these five methods were analyzed statistically for all subjects. The results indicated that the choices in EEG transformation and time-window can strongly affect the PSD quantities in a variety of ways. Additionally, EEG transformation and filter choices can influence phase quantities significantly. These results raise the need for a consistent and standard EEG processing pipeline for computational EEG studies. Consistency of signal processing methods cannot only help produce comparable results and reproducible research, but also pave the way for federated machine learning methods, e.g., where model parameters rather than data are shared.

Effects of simultaneous real-time fMRI and EEG neurofeedback in major depressive disorder evaluated with brain electromagnetic tomography

Recently, we reported an emotion self-regulation study (Zotev et al., 2020), in which patients with major depressive disorder (MDD) used simultaneous real-time fMRI and EEG neurofeedback (rtfMRI-EEG-nf) to upregulate two fMRI and two EEG activity measures, relevant to MDD. The target measures included fMRI activities of the left amygdala and left rostral anterior cingulate cortex, and frontal EEG asymmetries in the alpha band (FAA) and high-beta band (FBA). Here we apply the exact low resolution brain electromagnetic tomography (eLORETA) to investigate EEG source activities during the rtfMRI-EEG-nf procedure. The exploratory analyses reveal significant changes in hemispheric lateralities of upper alpha and high-beta current source densities in the prefrontal regions, consistent with upregulation of the FAA and FBA during the rtfMRI-EEG-nf task. Similar laterality changes are observed for current source densities in the amygdala. Prefrontal upper alpha current density changes show significant negative correlations with anhedonia severity. Changes in prefrontal high-beta current density are consistent with reduction in comorbid anxiety. Comparisons with results of previous LORETA studies suggest that the rtfMRI-EEG-nf training is beneficial to MDD patients, and may have the ability to correct functional deficiencies associated with anhedonia and comorbid anxiety in MDD.

Human Mirror Neuron System Based Alarms in the Cockpit: A Neuroergonomic Evaluation

Controlled Flight Into Terrain (CFIT) events still remain among the deadliest accidents in aviation. When facing the possible occurrence of such an event, pilots have to immediately react to the ground proximity alarm ("Pull Up" alarm) in order to avoid the impending collision. However, the pilots' reaction to this alarm is not always optimal. This may be at least partly due to the low visual saliency of the current alarm and the deleterious effects of stress that alleviate the pilot's reactions. In the present study, two experiments (in a laboratory and in a flight simulator) were conducted to (1) investigate whether hand gesture videos (a hand pulling back the sidestick) can trigger brainwave frequencies related to the mirror neuron system; (2) determine whether enhancing the visual characteristics of the "Pull Up" alarm could improve pilots' response times. Electrophysiological results suggest that hand gesture videos attracted more participants' attention (greater alpha desynchronization in the parieto-occipital area) and possibly triggered greater activity of the mirror neuron system (greater mu and beta desynchronizations at central electrodes). Results obtained in the flight simulator revealed that enhancing the visual characteristics of the original "Pull Up" alarm improved the pilots' reaction times. However, no significant difference in reaction times between an enlarged "Pull Up" inscription and the hand gesture video was found. Further work is needed to determine whether mirror neuron system based alarms could bring benefits for flight safety, in particular, these alarms should be assessed during a high stress context.

Assessment of mental workload based on multi-physiological signals

BACKGROUND:

Mental workload is one of the contributing factors to human errors in road accidents or other potentially adverse incidents.

OBJECTIVE:

This research probes the effects of mental workload on the electroencephalographic (EEG) and electrocardiogram (ECG) of subjects in visual monitoring tasks, based on which a comprehensive evaluation model for mental workload is established effectively.

METHODS:

Three degrees of mental workload were obtained by monitoring tasks with different levels of difficulty. 20 healthy subjects were selected to take part in the research.

RESULTS:

The subjective scores showed a significant increase with the increase of task difficulty, meanwhile the reaction time (RT) increased and the accuracy decreased significantly, which proved the validity of three degrees of mental workload induced. For the EEG parameters, a significant decrease of θ energy was found in Frontal, Parietal and Occipital with the increase of level of mental workload, as well as a significant decrease of α energy in Frontal, Central and Occipital, meanwhile a significant increase of β energy occurred in Frontal and Occipital. There was a significant decrease of α/θ in Occipital, and significant increases of θ/β and (α+β)/θ in Frontal, Central and Occipital, meanwhile (α+θ)/β and WPE decreased significantly in Frontal and Occipital. Among the ECG parameters, it was shown that Mean RR, RMSSD, HF_norm and SampEn decreased significantly with the increase of task difficulty, while LF_norm and LF/HF showed significant increases. These EEG indictors in Occipital and ECG indictors were chosen and constituted a multidimensional original sample. Principal Component Analysis (PCA) was used to extract the principal elements and decreased the dimension of sample space in order to simplify the calculation, based on which an effective classification model with accuracy of 80% was achieved by support vector machine (SVM).

CONCLUSION:

This study demonstrates that the proposed algorithm can be applied to mental workload monitoring.

Prediction of Antidepressant Treatment Outcome Using Event-Related Potential in Patients with Major Depressive Disorder

(1) Background: Prediction of treatment outcome has been one of the core objectives in clinical research of patients with major depressive disorder (MDD). This study explored the possibility of event-related potential (ERP) markers to predict antidepressant treatment outcomes among MDD patients; (2) Methods: Fifty-two patients with MDD were recruited and evaluated through Hamilton depression (HAM-D), Hamilton anxiety rating scale (HAM-A), and CORE. Patients underwent a battery of ERP measures including frontal alpha symmetry (FAA) in the low alpha band (8–10 Hz), mismatch negativity (MMN), and loudness-dependent auditory evoked potentials (LDAEP); (3) Results: During the eight weeks of study, 61% of patients achieved remission, and 77% showed successful treatment responsiveness. Patients with low FAA in F5/F6 demonstrated a significantly higher remission/response ratio and better treatment responsiveness (F (2.560, 117.755) = 3.84, p = 0.016) compared to patients with high FAA. In addition, greater FAA in F7/F8 EEG channels was significantly associated with greater melancholia scores (r = 0.34, p = 0.018). Other ERP markers lacked any significant effect; (4) Conclusions: Our results suggested low FAA (i.e., greater left frontal activity) could reflect a good treatment response in MDD patients. These findings support that FAA could be a promising index in understanding both MDD and melancholic subtype.

Microstates-based resting frontal alpha asymmetry approach for understanding affect and approach/withdrawal behavior

The role of resting frontal alpha-asymmetry in explaining neural-mechanisms of affect and approach/withdrawal behavior is still debatable. The present study explores the ability of the quasi-stable resting EEG asymmetry information and the associated neurovascular synchronization/desynchronization in bringing more insight into the understanding of neural-mechanisms of affect and approach/withdrawal behavior. For this purpose, a novel frontal alpha-asymmetry based on microstates, that assess quasi-stable EEG scalp topography information, is proposed and compared against standard frontal-asymmetry. Both proposed and standard frontal alpha-asymmetries were estimated from thirty-nine healthy volunteers resting-EEG simultaneously acquired with resting-fMRI. Further, neurovascular mechanisms of these asymmetry measures were estimated through EEG-informed fMRI. Subsequently, the Hemodynamic Lateralization Index (HLI) of the neural-underpinnings of both asymmetry measures was assessed. Finally, the robust correlation of both asymmetry-measures and their HLI’s with PANAS, BIS/BAS was carried out. The standard resting frontal-asymmetry and its HLI yielded no significant correlation with any psychological-measures. However, the microstate resting frontal-asymmetry correlated significantly with negative affect and its neural underpinning’s HLI significantly correlated with Positive/Negative affect and BIS/BAS measures. Finally, alpha-BOLD desynchronization was observed in neural-underpinning whose HLI correlated significantly with negative affect and BIS. Hence, the proposed resting microstate-frontal asymmetry better assesses the neural-mechanisms of affect, approach/withdrawal behavior.

EEG "Signs" of Verbal Creative Task Fulfillment with and without Overcoming Self-Induced Stereotypes

The study aimed to reveal task-related differences in story creation with and without the mental effort of overcoming self-induced stereotypes. Eighteen right-handed subjects (19.3 ± 1.1 years old) created stories. The subjects reported the formation of story plot stereotypes (as we call them: self-induced) during self-regulated creative production, which had to be overcome with the instruction to continue the story. Creative task fulfillment (without formed stereotypes—first stage of creation) was characterized by a decrease in the wave percentages of 9–10 Hz, 10–11 Hz and 11–12 Hz frequencies and EEG desynchronization (decreases in EEG spectral power) in the theta (4–8 Hz), alpha1 (8–10 Hz) and alpha2 (10–13 Hz) frequency bands in comparison with the REST (random episodic silent thought) state. The effortful creation task (with overcoming of self-induced stereotypes-second stage of creation) was characterized by increases in waves with frequencies of 9–10 Hz, 10–11 Hz, 11–12 Hz in temporal, occipital areas and pronounced EEG synchronization in alpha1,2 frequency bands in comparison with the free creation condition. It was also found, that the participants with the higher originality scores in psychological tests demonstrated increased percentage of high frequencies (11–12 Hz in comparison with those who had lower originality scores. Obtained results support the role of alpha and theta frequency bands dynamics in creative cognition.

Prediction of working memory ability based on EEG by functional data analysis

BACKGROUND

There is always a demand for fast and accurate algorithms for EEG signal processing. Owing to the high sample rate, EEG signals usually come with a large number of sample points, making it difficult to predict the working memory ability in cognitive research with EEG.

NEW METHOD

Following well-designed experiments, the functional linear model provides a simple framework for regressions involving EEG signal predictors. The use of a data-driven basis in a linear structure naturally extends the standard linear regression model. The proposed approach utilizes B-spline approximation of functional principal components that greatly facilitates implementation.

RESULTS

Using LASSO feature selection, critical features have been extracted from eight frontal electrodes, and the R-square of 0.72 indicates rather strong linear association of actual observations and out-of-sample predictions.

COMPARISON WITH EXISTING METHODS

There does not seem to be any existing methods of predicting working memory ability from N-back task tests via EEG signals; the data-driven functional linear regression method proposed in this work is, to the best of our knowledge, the first of its kind.

CONCLUSIONS

The data analytics suggest that a multiple functional linear regression model for the predictive relationship between working memory ability and frontal activity of the brain is both feasible and accurate via EEG signal processing.

Slow-paced inspiration regularizes alpha phase dynamics in the human brain

The phase of low-frequency, rhythmic cortical activity is essential for organizing brain processes because it provides a recurrent temporal frame for information coding. However, the low-frequency cortical phase exhibits great flexibility in response to external influences. Given that brain rhythms have been found to track respiratory inputs, we hypothesized that slow breathing, commonly associated with mental regulation, could reorganize the relationship between these two rhythmic systems through the adjustment of the cortical phase to such a slow train of inputs. Based on simultaneous magnetoencephalography and respiratory measurements, we report that while participants performed paced breathing, slow relative to normal breathing modulated cortical phase activity in the alpha range across widespread brain areas. Such modulation effects were specifically locked to the middle of the inspiration stage and exhibited a well-structured pattern. At the single-subject level, the phase angles underlying the effects became more likely to be diametrically opposed across breaths, indicating unique and consistent phase adjustment to slow inspiratory inputs. Neither cardiac fluctuations nor breathing-unrelated task effects could account for the findings. We suggest that slow-paced inspiration could organize the cortical phase in a regularized phase pattern, revealing a rhythmic but dynamic neural network integrated with different neurophysiological systems through volitional control.NEW & NOTEWORTHY Breathing is more complicated than a simple gas exchange, as it is integrated with numerous cognitive and emotional functions. Controlled slow breathing has often been used to regulate mental processes. This magnetoencephalography study demonstrates that slow-paced relative to normal-paced inspiration could organize the timing of alpha rhythmic activities across breathing cycles in a structured manner over widespread brain areas. Our results reveal how a volitionally controlled change in respiratory behavior could systematically modulate cortical activity.

Frontal Alpha Asymmetry Correlates with Suicidal Behavior in Major Depressive Disorder

Objective

Based on the constant associations made between major depressive disorder (MDD) and alpha asymmetry, and MDD and suicide, this study aimed to examine the relationship between frontal alpha asymmetry and suicide in MDD patients.

Methods

Sixty-six MDD patients, of whom fifteen were male and fifty-one were female, were recruited. Independent groups were created based on the median score of frontal alpha asymmetry: the left dominant (LD) group and the right dominant (RD) group. The alpha band (8‒12 Hz) and its sub-bands (i.e., low alpha band: 8‒10 Hz; high alpha band: 10‒12 Hz) were of interest. Source level alpha asymmetry was calculated as well.

Results

Suicidal behavior was positively correlated with the asymmetry indices of the low alpha band and the alpha band in the LD group and that of the high alpha band in the RD group. Source level analysis revealed positive correlations between suicidal behavior and the asymmetry index of the low alpha band in the LD group.

Conclusion

Frontal alpha asymmetry, especially that of the low alpha band, might reflect the cognitive deficits associated with suicidal behaviors in MDD patients.

Selective effects of acute low-grade inflammation on human visual attention

Illness is often accompanied by perceived cognitive sluggishness, a symptom that may stem from immune system activation. The current study used electroencephalography (EEG) to assess how inflammation affected three different distinct attentional processes: alerting, orienting and executive control. In a double-blinded placebo-controlled within-subjects design (20 healthy males, mean age = 24.5, SD = 3.4), Salmonella typhoid vaccination (0.025 mg; Typhim Vi, Sanofi Pasteur) was used to induce transient mild inflammation, while a saline injection served as a placebo-control. Participants completed the Attention Network Test with concurrent EEG recorded 6 h post-injection. Analyses focused on behavioral task performance and on modulation of oscillatory EEG activity in the alpha band (9-12 Hz) for alerting as well as orienting attention and frontal theta band (4-8 Hz) for executive control. Vaccination induced mild systemic inflammation, as assessed by interleukin-6 (IL-6) levels. While no behavioral task performance differences between the inflammation and placebo condition were evident, inflammation caused significant alterations to task-related brain activity. Specifically, inflammation produced greater cue-induced suppression of alpha power in the alerting aspect of attention and individual variation in the inflammatory response was significantly correlated with the degree of alpha power suppression. Notably, inflammation did not affect orienting (i.e., alpha lateralization) or executive control (i.e., frontal theta activity). These results reveal a unique neurophysiological sensitivity to acute mild inflammation of the neural network that underpins attentional alerting functions. Observed in the absence of performance decrements, these novel findings suggest that acute inflammation requires individuals to exert greater cognitive effort when preparing for a task in order to maintain adequate behavioral performance.

Mental workload of young and older adults gauged with ERPs and spectral power during N-Back task performance

Mental workload has been shown to correlate with alpha and theta band power but only few EEG studies focused on the relation between these bands and Event Related Potentials (ERPs), more specifically the P300 component. We report on an EEG study on mental workload where not only young but also older adults performed an N-Back task. Participants watched a sequence of visual pictures and indicated whether the current picture was the same as the one shown N pictures before. We considered N = 4 difficulty levels and analyzed the relation between these and P300 amplitude and theta and alpha band power, and also examined the effect of age, level of education, work activities, and task accuracy. Our results revealed a decrease in P300 amplitude and alpha band activity for higher difficulty levels for young adults in the parietal region. However, for older adults, fatigue played a more important role than we could anticipate as the alpha band power increased for the highest task difficulty level, and since performance accuracy also decreased, it could even be a sign of task disengagement. Beside alpha band, theta band activity showed a positive correlation with task difficulty level for both young and older adults. Additionally, we found higher P300 amplitudes for young adults compared to older adults, in line with their higher performance accuracies and lower reaction times. In conclusion, we showed that P300 amplitude and alpha and theta bands power provide complementary information for judging mental workload during N-Back performance for young and older subjects and for detecting mental fatigue and task disengagement.

The Effect of Zhongyong Thinking on Remote Association Thinking: An EEG Study

The Doctrine of the Mean (zhongyong) introduced by Confucianism is not only an aspect of faith, but also a way of thinking for Chinese individuals. Zhongyong includes two thinking forms: eclectic thinking (ET; i.e., “neither-A-nor-B”) and integrated thinking (IT; i.e., “both-A-and-B”). Given the inclination of Asian individuals toward situational cognition, this study used questions about situations familiar to Chinese undergraduates to activate either ET or IT. This was done to investigate the effects of the two divergent thinking forms of zhongyong on performance levels on the Remote Associates Test (RAT). Both behavioral and EEG results found that participants in the IT condition demonstrated higher RAT scores than those in the ET condition. The conclusion was that the RAT and priming tasks shared the same neural mechanism. This meant that the priming tasks of IT allowed participants to enter a state of creative preparation in advance, further affecting resolution of the RAT.

Increased Neural Activity in Hazardous Drinkers During High Workload in a Visual Working Memory Task: A Preliminary Assessment Through Event-Related Potentials

Despite equated behavioral performance levels, hazardous drinkers generally exhibited increased neural activity while performing simple cognitive tasks compared to light drinkers. Here, 49 participants (25 hazardous and 24 light drinkers) participated in an event-related potentials (ERPs) study while performing an n-back working memory task. In the control zero-back (N0) condition, the subjects were required to press a button when the number "2" or "6" was displayed. In the two-back and three-back (N2; N3) conditions, the subjects had to press a button when the displayed number was identical to the number shown two/three trials earlier. To assess for the impact of alcohol consumption on the updating of working memory processes under various cognitive loads, difference waveforms of "N2 minus N0" and "N3 minus N0" were computed by subtracting waveforms in the N0 condition from waveforms in the N2 and N3 conditions, for the light and the hazardous drinkers. Three main ERP components were noted for both groups: a P200/N200 complex, a P300 component, and an N400/P600 activity. The results show that, to perform the task at the same level as the light drinkers, the hazardous drinkers exhibited larger amplitude differences, mainly around the P300 and P600 components. These data may be considered, at the preventive level, as vulnerability factors for developing adult substance use disorders, and they stress the importance, at a clinical level, to consider such working memory processes in the management of alcohol dependence.

The frequency of alpha oscillations: Task-dependent modulation and its functional significance

Power (amplitude) and frequency are two important characteristics of EEG alpha oscillations (8-12 Hz). There is an extensive literature showing that alpha power can be modulated in a goal-oriented manner to either enhance or suppress sensory information processing. Only a few studies to date have examined the task-dependent modulation of alpha frequency. Instead, alpha frequency is often viewed as a trait variable, and used to characterize individual differences in cognitive functioning. We performed two experiments to examine the task-dependent modulation of alpha frequency and its functional significance. In the first experiment, high-density EEG was recorded from 21 participants performing a Sternberg working memory task. The results showed that: (1) during memory encoding, alpha frequency decreased with increasing memory load, whereas during memory retention and retrieval, alpha frequency increased with increasing memory load, (2) higher alpha frequency prior to the onset of probe was associated with longer reaction time, and (3) higher alpha frequency prior to the onset of cue or probe was associated with weaker early cue-evoked or probe-evoked neural responses. In the second experiment, simultaneous EEG-fMRI was recorded from 59 participants during resting state. An EEG-informed fMRI analysis revealed that the spontaneous fluctuations of alpha frequency, but not alpha power, were inversely associated with BOLD activity in the visual cortex. Taken together, these findings suggest that alpha frequency is task-dependent, may serve as an indicator of cortical excitability, and along with alpha power, provides more comprehensive indexing of sensory gating.

Resting-state EEG power and connectivity are associated with alpha peak frequency slowing in healthy aging

The individual alpha peak frequency (IAPF) of the human electroencephalography (EEG) typically experiences slowing with increasing age. Despite this hallmark change, studies that investigate modulations of conventional EEG alpha power and connectivity by aging and age-related neuropathology neglect to account for intergroup differences in IAPF. To investigate the relationship of age-related IAPF slowing with EEG power and connectivity, we recorded eyes-closed resting-state EEG in 37 young adults and 32 older adults. We replicated the finding of a slowed IAPF in older adults. IAPF values were significantly correlated with the frequency of maximum global connectivity and the means of their distributions did not differ, suggesting that connectivity was highest at the IAPF. Older adults expressed reduced global EEG power and connectivity at the conventional upper alpha band (10-12 Hz) compared with young adults. By contrast, groups had equivalent power and connectivity at the IAPF. The results suggest that conventional spectral boundaries may be biased against older adults or any group with a slowed IAPF. We conclude that investigations of alpha activity in aging and age-related neuropathology should be adapted to the IAPF of the individual and that previous findings should be interpreted with caution. EEG in the dominant alpha range may be unsuitable for examining cortico-cortical connectivity due to its subcortical origins.

Alpha band frequency differences between low-trait and high-trait anxious individuals

Trait anxiety has been shown to cause significant impairments on attentional tasks. Current research has identified alpha band frequency differences between low-trait and high-trait anxious individuals. Here, we further investigated the underlying alpha band frequency differences between low-trait and high-trait anxious individuals during their resting state and the completion of an inhibition executive functioning task. Using human participants and quantitative electroencephalographic recordings, we measured alpha band frequency in individuals both high and low in trait anxiety during their resting state, and while they completed an Eriksen Flanker Task. Results indicated that high-trait anxious individuals exhibit a desynchronization in alpha band frequency from a resting state to when they complete the Eriksen Flanker Task. This suggests that high-trait anxious individuals maintain fewer attentional resources at rest and must martial resources for task performance as compared with low-trait anxious individuals, who appear to maintain stable cognitive resources between rest and task performance. These findings add to the cognitive neuroscience literature surrounding the role of alpha band frequency in low-trait and high-trait anxious individuals.

Towards multilevel mental stress assessment using SVM with ECOC: an EEG approach

Mental stress has been identified as one of the major contributing factors that leads to various diseases such as heart attack, depression, and stroke. To avoid this, stress quantification is important for clinical intervention and disease prevention. This study aims to investigate the feasibility of exploiting electroencephalography (EEG) signals to discriminate between different stress levels. We propose a new assessment protocol whereby the stress level is represented by the complexity of mental arithmetic (MA) task for example, at three levels of difficulty, and the stressors are time pressure and negative feedback. Using 18-male subjects, the experimental results showed that there were significant differences in EEG response between the control and stress conditions at different levels of MA task with p values < 0.001. Furthermore, we found a significant reduction in alpha rhythm power from one stress level to another level, p values < 0.05. In comparison, results from self-reporting questionnaire NASA-TLX approach showed no significant differences between stress levels. In addition, we developed a discriminant analysis method based on multiclass support vector machine (SVM) with error-correcting output code (ECOC). Different stress levels were detected with an average classification accuracy of 94.79%. The lateral index (LI) results further showed dominant right prefrontal cortex (PFC) to mental stress (reduced alpha rhythm). The study demonstrated the feasibility of using EEG in classifying multilevel mental stress and reported alpha rhythm power at right prefrontal cortex as a suitable index.

ICA-Derived EEG Correlates to Mental Fatigue, Effort, and Workload in a Realistically Simulated Air Traffic Control Task

Electroencephalograph (EEG) has been increasingly studied to identify distinct mental factors when persons perform cognitively demanding tasks. However, most of these studies examined EEG correlates at channel domain, which suffers the limitation that EEG signals are the mixture of multiple underlying neuronal sources due to the volume conduction effect. Moreover, few studies have been conducted in real-world tasks. To precisely probe EEG correlates with specific neural substrates to mental factors in real-world tasks, the present study examined EEG correlates to three mental factors, i.e., mental fatigue [also known as time-on-task (TOT) effect], workload and effort, in EEG component signals, which were obtained using an independent component analysis (ICA) on high-density EEG data. EEG data were recorded when subjects performed a realistically simulated air traffic control (ATC) task for 2 h. Five EEG independent component (IC) signals that were associated with specific neural substrates (i.e., the frontal, central medial, motor, parietal, occipital areas) were identified. Their spectral powers at their corresponding dominant bands, i.e., the theta power of the frontal IC and the alpha power of the other four ICs, were detected to be correlated to mental workload and effort levels, measured by behavioral metrics. Meanwhile, a linear regression analysis indicated that spectral powers at five ICs significantly increased with TOT. These findings indicated that different levels of mental factors can be sensitively reflected in EEG signals associated with various brain functions, including visual perception, cognitive processing, and motor outputs, in real-world tasks. These results can potentially aid in the development of efficient operational interfaces to ensure productivity and safety in ATC and beyond.

Exploring the heterogeneity of neural social indices for genetically distinct etiologies of autism

Background

Autism spectrum disorder (ASD) is a genetically and phenotypically heterogeneous disorder. Promising initiatives utilizing interdisciplinary characterization of ASD suggest phenotypic subtypes related to specific likely gene-disrupting mutations (LGDMs). However, the role of functionally associated LGDMs in the neural social phenotype is unknown.

Methods

In this study of 26 children with ASD (n = 13 with an LGDM) and 13 control children, we characterized patterns of mu attenuation and habituation as children watched videos containing social and nonsocial motions during electroencephalography acquisition.

Results

Diagnostic comparisons were consistent with prior work suggesting aberrant mu attenuation in ASD within the upper mu band (10–12 Hz), but typical patterns within the lower mu band (8–10 Hz). Preliminary exploration indicated distinct social sensitization patterns (i.e., increasing mu attenuation for social motion) for children with an LGDM that is primarily expressed during embryonic development. In contrast, children with an LGDM primarily expressed post-embryonic development exhibited stable typical patterns of lower mu attenuation. Neural social indices were associated with social responsiveness, but not cognition.

Conclusions

These findings suggest unique neurophysiological profiles for certain genetic etiologies of ASD, further clarifying possible genetic functional subtypes of ASD and providing insight into mechanisms for targeted treatment approaches.

Electronic supplementary material

The online version of this article (doi:10.1186/s11689-017-9199-4) contains supplementary material, which is available to authorized users.

Mu rhythm suppression reflects mother-child face-to-face interactions: a pilot study with simultaneous MEG recording

Spontaneous face-to-face interactions between mothers and their children play crucial roles in the development of social minds; however, these inter-brain dynamics are still unclear. In this pilot study, we measured MEG mu suppression during face-to-face spontaneous non-linguistic interactions between mothers and their children with autism spectrum disorder (ASD) using the MEG hyperscanning system (i.e., simultaneous recording). The results demonstrated significant correlations between the index of mu suppression (IMS) in the right precentral area and the traits (or severity) of ASD in 13 mothers and 8 children (MEG data from 5 of the children could not be obtained due to motion noise). In addition, higher IMS values (i.e., strong mu suppression) in mothers were associated with higher IMS values in their children. To evaluate the behavioral contingency between mothers and their children, we calculated cross correlations between the magnitude of the mother and child head-motion during MEG recordings. As a result, in mothers whose head motions tended to follow her child's head motion, the magnitudes of mu suppression in the mother's precentral area were large. Further studies with larger sample sizes, including typically developing children, are necessary to generalize this result to typical interactions between mothers and their children.

User Experience May be Producing Greater Heart Rate Variability than Motor Imagery Related Control Tasks during the User-System Adaptation in Brain-Computer Interfaces

Brain-computer interface (BCI) is technology that is developing fast, but it remains inaccurate, unreliable and slow due to the difficulty to obtain precise information from the brain. Consequently, the involvement of other biosignals to decode the user control tasks has risen in importance. A traditional way to operate a BCI system is via motor imagery (MI) tasks. As imaginary movements activate similar cortical structures and vegetative mechanisms as a voluntary movement does, heart rate variability (HRV) has been proposed as a parameter to improve the detection of MI related control tasks. However, HR is very susceptible to body needs and environmental demands, and as BCI systems require high levels of attention, perceptual processing and mental workload, it is important to assess the practical effectiveness of HRV. The present study aimed to determine if brain and heart electrical signals (HRV) are modulated by MI activity used to control a BCI system, or if HRV is modulated by the user perceptions and responses that result from the operation of a BCI system (i.e., user experience). For this purpose, a database of 11 participants who were exposed to eight different situations was used. The sensory-cognitive load (intake and rejection tasks) was controlled in those situations. Two electrophysiological signals were utilized: electroencephalography and electrocardiography. From those biosignals, event-related (de-)synchronization maps and event-related HR changes were respectively estimated. The maps and the HR changes were cross-correlated in order to verify if both biosignals were modulated due to MI activity. The results suggest that HR varies according to the experience undergone by the user in a BCI working environment, and not because of the MI activity used to operate the system.

Towards an effective cross-task mental workload recognition model using electroencephalography based on feature selection and support vector machine regression

Electroencephalographic (EEG) has been believed to be a potential psychophysiological measure of mental workload. There however remain a number of challenges in building a generalized mental workload recognition model, one of which includes the inability of an EEG-based workload classifier trained on a specific task to handle other tasks. The primary goal of the present study was to examine the possibility of addressing this challenge using feature selection and regression model. Support vector machine classifier and regression models were examined under within-task conditions (trained and tested on the same task) and cross-task conditions (trained on one task and tested on another task) for well-trained verbal and spatial n-back tasks. A specifically designed cross-task recursive feature elimination (RFE) based feature selection was used to handle the possible causes responsible for the deterioration of the performance of cross-task regression model. The within-task classification and regression performed fairly well. Cross-task classification and regression performance, however, deteriorated to unacceptable levels (around chance level). Trained and tested with the most robust feature subset selected by cross-task RFE, the performance of cross-task regression was significantly improved, and there were no significant changes in the performance of within-task regression. It can be inferred that workload-related features can be picked out from those which have been contaminated using RFE, and regression models rather than classifiers may be a wiser choice for cross-task conditions. These encouraging results suggest that the cross-task workload recognition model built in this study is much more generalizable across task when compared to the model built in traditional way.