Theta and Alpha Oscillations in Attentional Interaction during Distracted Driving

How the cognitive load of simulated driving affects the brain dynamics underlying auditory attention

OBJECTIVE

Distracted driving is a primary contributor to for motor vehicle crashes, the leading cause for injuries and fatalities for youth. Although attention and working memory clearly underlie driving abilities, few studies explore these functions on the brain-level under the cognitive load of driving. To understand the load driving has on auditory attention processing, we examined the differences in dynamic brain response to auditory stimuli during LOAD (while driving in a high-fidelity driving simulator) and No-LOAD conditions (seated in simulator, parked on the side of the road).

METHODS

Twenty-seven young adult drivers (18-27 y/o; 15 = women) completed a Selective Auditory Attention Task during both a LOAD (driving) and No-LOAD condition in a ½ cab miniSim® high-fidelity driving simulator. During the task, participants responded by pressing the volume control button on the steering wheel when a target tone was presented to a target ear. Electroencephalography-recorded event-related brain responses to the target tones were evaluated through alpha and theta oscillations for two response windows (early: 150-330ms; late: 350-540ms).

RESULTS

During an early time window, we observed a significant interaction between attended/unattended and LOAD/No-LOAD theta power in the right frontal cortical region (F(1, 24)= 5.4, p=.03, partial η2=.18). During the later window, we observed a significant interaction between attended/unattended and LOAD/No-LOAD alpha response in the posterior cortical region (F(1, 24)=11.81, p=.002, partial η2=.15) and in the right temporal cortical region during the window (F(1, 24)=4.3, p=.05, partial η2=.33).

CONCLUSIONS

Our data provide insight into the demand that driving has on cognitive faculties and how dual task engagement may draw resources away from driving. We suggest future research directly incorporate vehicle control abilities into study design to understand how brain-based measures relate to driving behaviors.

Attention-based cross-frequency graph convolutional network for driver fatigue estimation

Fatigue driving significantly contributes to global vehicle accidents and fatalities, making driver fatigue level estimation crucial. Electroencephalography (EEG) is a proven reliable predictor of brain states. With Deep Learning (DL) advancements, brain state estimation algorithms have improved significantly. Nonetheless, EEG's multi-domain nature and the intricate spatial-temporal-frequency correlations among EEG channels present challenges in developing precise DL models. In this work, we introduce an innovative Attention-based Cross-Frequency Graph Convolutional Network (ACF-GCN) for estimating drivers' reaction times using EEG signals from theta, alpha, and beta bands. This method utilizes a multi-head attention mechanism to detect long-range dependencies between EEG channels across frequencies. Concurrently, the transformer's encoder module learns node-level feature maps from the attention-score matrix. Subsequently, the Graph Convolutional Network (GCN) integrates this matrix with feature maps to estimate driver reaction time. Our validation on a publicly available dataset shows that ACF-GCN outperforms several state-of-the-art methods. We also explore the brain dynamics within the cross-frequency attention-score matrix, identifying theta and alpha bands as key influencers in fatigue estimating performance. The ACF-GCN method advances brain state estimation and provides insights into the brain dynamics underlying multi-channel EEG signals.

Gender-Specific Interactions in a Visual Object Recognition Task in Persons with Opioid Use Disorder

Opioid use disorder (OUD)-associated overdose deaths have reached epidemic proportions worldwide over the past two decades, with death rates for men reported at twice the rate for women. Using a controlled, cross-sectional, age-matched (18-56 y) design to better understand the cognitive neuroscience of OUD, we evaluated the electroencephalographic (EEG) responses of male and female participants with OUD vs. age- and gender-matched non-OUD controls during a simple visual object recognition Go/No-Go task. Overall, women had significantly slower reaction times (RTs) than men. In addition, EEG N200 and P300 event-related potential (ERP) amplitudes for non-OUD controls were significantly larger for men, while their latencies were significantly shorter than for women. However, while N200 and P300 amplitudes were not significantly affected by OUD for either men or women in this task, latencies were also affected differentially in men vs. women with OUD. Accordingly, for both N200 and P300, male OUD participants exhibited longer latencies while female OUD participants exhibited shorter ones than in non-OUD controls. Additionally, robust oscillations were found in all participants during a feedback message associated with performance in the task. Although alpha and beta power during the feedback message were significantly greater for men than women overall, both alpha and beta oscillations exhibited significantly lower power in all participants with OUD. Taken together, these findings suggest important gender by OUD differences in cognitive processing and reflection of performance in this simple visual task.

TMS Bursts Can Modulate Local and Networks Oscillations During Lower-Limb Movement

PURPOSE

Lower-limb motor functions involve processing information via both motor and cognitive control networks. Measuring oscillations is a key element in communication within and between cortical networks during high-order motor functions. Increased midfrontal theta oscillations are related to improved lower-limb motor performances in patients with movement disorders. Noninvasive neuromodulation approaches have not been explored extensively to understand the oscillatory mechanism of lower-limb motor functions. This study aims to examine the effects of repetitive transcranial magnetic stimulation on local and network EEG oscillations in healthy elderly subjects.

METHODS

Eleven healthy elderly subjects (67-73 years) were recruited via advertisements, and they underwent both active and sham stimulation procedures in a random, counterbalanced design. Transcranial magnetic stimulation bursts (θ-transcranial magnetic stimulation; 4 pulses/second) were applied over the midfrontal lead (vertex) before a GO-Cue pedaling task, and signals were analyzed using time-frequency methods.

RESULTS

Transcranial magnetic stimulation bursts increase the theta activity in the local ( p = 0.02) and the associated network during the lower-limb pedaling task ( p = 0.02). Furthermore, after task-related transcranial magnetic stimulation burst sessions, increased resting-state alpha activity was observed in the midfrontal region ( p = 0.01).

CONCLUSIONS

This study suggests the ability of midfrontal transcranial magnetic stimulation bursts to directly modulate local and network oscillations in a frequency manner during lower-limb motor task. Transcranial magnetic stimulation burst-induced modulation may provide insights into the functional roles of oscillatory activity during lower-limb movement in normal and disease conditions.

Postural control paradigm (BioVRSea): towards a neurophysiological signature

Objective.To define a new neurophysiological signature from electroencephalography (EEG) during a complex postural control task using the BioVRSea paradigm, consisting of virtual reality (VR) and a moving platform, mimicking the behavior of a boat on the sea.Approach.EEG (64 electrodes) data from 190 healthy subjects were acquired. The experiment is composed of 6 segments (Baseline, PRE, 25%, 50%, 75%, POST). The baseline lasts 60 s while standing on the motionless platform with a mountain view in the VR goggles. PRE and POST last 40 s while standing on the motionless platform with a sea simulation. The 3 other tasks last 40 s each, with the platform moving to adapt to the waves, and the subject holding a bar to maintain its balance. The power spectral density (PSD) difference for each task minus baseline has been computed for every electrode, for five frequency bands (delta, theta, alpha, beta, and low-gamma). Statistical significance has been computed.Main results.All the bands were significant for the whole cohort, for each task regarding baseline. Delta band shows a prefrontal PSD increase, theta a fronto-parietal decrease, alpha a global scalp power decrease, beta an increase in the occipital and temporal scalps and a decrease in other areas, and low-gamma a significant but slight increase in the parietal, occipital and temporal scalp areas.Significance.This study develops a neurophysiological reference during a complex postural control task. In particular, we found a strong localized activity associated with certain frequency bands during certain phases of the experiment. This is the first step towards a neurophysiological signature that can be used to identify pathological conditions lacking quantitative diagnostics assessment.

Study on the Effect of Judgment Excitation Mode to Relieve Driving Fatigue Based on MF-DFA

Driving fatigue refers to a phenomenon in which a driver’s physiological and psychological functions become unbalanced after a long period of continuous driving, and their driving skills decline objectively. The hidden dangers of driving fatigue to traffic safety should not be underestimated. In this work, we propose a judgment excitation mode (JEM), which adds secondary cognitive tasks to driving behavior through dual-channel human–computer interaction, so as to delay the occurrence of driving fatigue. We used multifractal detrended fluctuation analysis (MF-DFA) to study the dynamic properties of subjects’ EEG, and analyzed the effect of JEM on fatigue retardation by Hurst exponent value and multifractal spectrum width value. The results show that the multifractal properties of the two driving modes (normal driving mode and JEM) are significantly different. The JEM we propose can effectively delay the occurrence of driving fatigue, and has good prospects for future practical applications.

Improving EEG-Based Driver Distraction Classification Using Brain Connectivity Estimators

This paper discusses a novel approach to an EEG (electroencephalogram)-based driver distraction classification by using brain connectivity estimators as features. Ten healthy volunteers with more than one year of driving experience and an average age of 24.3 participated in a virtual reality environment with two conditions, a simple math problem-solving task and a lane-keeping task to mimic the distracted driving task and a non-distracted driving task, respectively. Independent component analysis (ICA) was conducted on the selected epochs of six selected components relevant to the frontal, central, parietal, occipital, left motor, and right motor areas. Granger-Geweke causality (GGC), directed transfer function (DTF), partial directed coherence (PDC), and generalized partial directed coherence (GPDC) brain connectivity estimators were used to calculate the connectivity matrixes. These connectivity matrixes were used as features to train the support vector machine (SVM) with the radial basis function (RBF) and classify the distracted and non-distracted driving tasks. GGC, DTF, PDC, and GPDC connectivity estimators yielded the classification accuracies of 82.27%, 70.02%, 86.19%, and 80.95%, respectively. Further analysis of the PDC connectivity estimator was conducted to determine the best window to differentiate between the distracted and non-distracted driving tasks. This study suggests that the PDC connectivity estimator can yield better classification accuracy for driver distractions.

Towards defining biomarkers to evaluate concussions using virtual reality and a moving platform (BioVRSea)

Current diagnosis of concussion relies on self-reported symptoms and medical records rather than objective biomarkers. This work uses a novel measurement setup called BioVRSea to quantify concussion status. The paradigm is based on brain and muscle signals (EEG, EMG), heart rate and center of pressure (CoP) measurements during a postural control task triggered by a moving platform and a virtual reality environment. Measurements were performed on 54 professional athletes who self-reported their history of concussion or non-concussion. Both groups completed a concussion symptom scale (SCAT5) before the measurement. We analyzed biosignals and CoP parameters before and after the platform movements, to compare the net response of individual postural control. The results showed that BioVRSea discriminated between the concussion and non-concussion groups. Particularly, EEG power spectral density in delta and theta bands showed significant changes in the concussion group and right soleus median frequency from the EMG signal differentiated concussed individuals with balance problems from the other groups. Anterior-posterior CoP frequency-based parameters discriminated concussed individuals with balance problems. Finally, we used machine learning to classify concussion and non-concussion, demonstrating that combining SCAT5 and BioVRSea parameters gives an accuracy up to 95.5%. This study is a step towards quantitative assessment of concussion.

Changes in Electroencephalography Activity of Sensory Areas Linked to Car Sickness in Real Driving Conditions

Car sickness is a major concern for car passengers, and with the development of autonomous vehicles, increasing numbers of car occupants are likely to be affected. Previous laboratory studies have used EEG measurements to better understand the cerebral changes linked to symptoms. However, the dynamics of motion in labs/simulators differ from those of a real car. This study sought to identify specific cerebral changes associated with the level of car sickness experienced in real driving conditions. Nine healthy volunteers participated as front passengers in a slalom session inducing lateral movements at very low frequency (0.2 Hz). They were continuously monitored via EEG recordings and subjectively rated their level of symptoms after each slalom, using a 5-point likert scale. Car-sickness symptoms evolved concomitantly with changes in theta and alpha power in the occipital and parietal areas. These changes may reflect altered sensory integration, as well as a possible influence of sleepiness mitigating symptoms.

Let Complexity Bring Clarity: A Multidimensional Assessment of Cognitive Load Using Physiological Measures

The effects of cognitive load on driver behavior and traffic safety are unclear and in need of further investigation. Reliable measures of cognitive load for use in research and, subsequently, in the development and implementation of driver monitoring systems are therefore sought. Physiological measures are of interest since they can provide continuous recordings of driver state. Currently, however, a few issues related to their use in this context are not usually taken into consideration, despite being well-known. First, cognitive load is a multidimensional construct consisting of many mental responses (cognitive load components) to added task demand. Yet, researchers treat it as unidimensional. Second, cognitive load does not occur in isolation; rather, it is part of a complex response to task demands in a specific operational setting. Third, physiological measures typically correlate with more than one mental state, limiting the inferences that can be made from them individually. We suggest that acknowledging these issues and studying multiple mental responses using multiple physiological measures and independent variables will lead to greatly improved measurability of cognitive load. To demonstrate the potential of this approach, we used data from a driving simulator study in which a number of physiological measures (heart rate, heart rate variability, breathing rate, skin conductance, pupil diameter, eye blink rate, eye blink duration, EEG alpha power, and EEG theta power) were analyzed. Participants performed a cognitively loading n-back task at two levels of difficulty while driving through three different traffic scenarios, each repeated four times. Cognitive load components and other coinciding mental responses were assessed by considering response patterns of multiple physiological measures in relation to multiple independent variables. With this approach, the construct validity of cognitive load is improved, which is important for interpreting results accurately. Also, the use of multiple measures and independent variables makes the measurements (when analyzed jointly) more diagnostic-that is, better able to distinguish between different cognitive load components. This in turn improves the overall external validity. With more detailed, diagnostic, and valid measures of cognitive load, the effects of cognitive load on traffic safety can be better understood, and hence possibly mitigated.

Distractor filtering and its electrophysiological correlates in schizophrenia

OBJECTIVE

Patients with schizophrenia are characterized by compromised working memory (WM) performance and increased distractibility. Theta synchronization (especially over the frontal midline areas) is related to cognitive control and executive processes during WM encoding and retention. Alpha event-related desynchronization (ERD) is associated with information processing and attention.

METHODS

Participants (35 patients and 39 matched controls) performed a modified Sternberg WM task, containing salient and non-salient distractor items in the retention period. A high-density 128 channel EEG was recorded during the task. Theta (4-7 Hz) and fast alpha (10-13 Hz) event-related spectral perturbation (ERSP) were analyzed during the retention and encoding period.

RESULTS

Patients with schizophrenia showed worse WM performance and increased attentional distractibility in terms of lower hit rates and increased distractor-related commission errors compared to healthy controls. Theta synchronization was modulated by condition (learning vs. distractor) in both groups but it was modulated by salience only in controls. Furthermore, salience of distractors modulated less the fast alpha ERD in patients.

CONCLUSIONS

Our results suggest that patients with schizophrenia process salient and non-salient distracting information less efficiently and show weaker cognitive control compared to controls.

SIGNIFICANCE

These differences may partly account for diminished WM performance and increased distractibility in schizophrenia.

Exploring Covert States of Brain Dynamics via Fuzzy Inference Encoding

Human brain inherently exhibits latent mental processes which are likely to change rapidly over time. A framework that adopts a fuzzy inference system is proposed to model the dynamics of the human brain. The fuzzy inference system is used to encode real-world data to represent the salient features of the EEG signals. Then, an unsupervised clustering is conducted on the extracted feature space to identify the brain (external and covert) states that respond to different cognitive demands. To understand the human state change, a state transition diagram is introduced, allowing visualization of connectivity patterns between every pair of states. We compute the transition probability between every pair of states to represent the relationships between the states. This state transition diagram is named as the Fuzzy Covert State Transition Diagram (FCOSTD), which helps the understanding of human states and human performance. We then apply FCOSTD on distracted driving experiments. FCOSTD successfully discovers the external and covert states, faithfully reveals the transition of the brain between states, and the route of the state change when humans are distracted during a driving task. The experimental results demonstrate that different subjects have similar states and inter-state transition behaviour (establishing the consistency of the system) but different ways to allocate brain resources as different actions are being taken.

Driving-Induced Neurological Biomarkers in an Advanced Driver-Assistance System

Physiological signals are immediate and sensitive to neurological changes resulting from the mental workload induced by various driving environments and are considered a quantifying tool for understanding the association between neurological outcomes and driving cognitive workloads. Neurological assessment, outside of a highly-equipped clinical setting, requires an ambulatory electroencephalography (EEG) headset. This study aimed to quantify neurological biomarkers during a resting state and two different scenarios of driving states in a virtual driving environment. We investigated the neurological responses of seventeen healthy male drivers. EEG data were measured in an initial resting state, city-roadways driving state, and expressway driving state using a portable EEG headset in a driving simulator. During the experiment, the participants drove while experiencing cognitive workloads due to various driving environments, such as road traffic conditions, lane changes of surrounding vehicles, the speed limit, etc. The power of the beta and gamma bands decreased, and the power of the delta waves, theta, and frontal theta asymmetry increased in the driving state relative to the resting state. Delta-alpha ratio (DAR) and delta-theta ratio (DTR) showed a strong correlation with a resting state, city-roadways driving state, and expressway driving state. Binary machine-learning (ML) classification models showed a near-perfect accuracy between the resting state and driving state. Moderate classification performances were observed between the resting state, city-roadways state, and expressway state in multi-class classification. An EEG-based neurological state prediction approach may be utilized in an advanced driver-assistance system (ADAS).

A Driving Performance Forecasting System Based on Brain Dynamic State Analysis Using 4-D Convolutional Neural Networks

Vehicle accidents are the primary cause of fatalities worldwide. Most often, experiencing fatigue on the road leads to operator errors and behavioral lapses. Thus, there is a need to predict the cognitive state of drivers, particularly their fatigue level. Electroencephalography (EEG) has been demonstrated to be effective for monitoring changes in the human brain state and behavior. Thirty-seven subjects participated in this driving experiment and performed a perform lane-keeping task in a visual-reality environment. Three domains, namely, frequency, temporal, and 2-D spatial information, of the EEG channel location were comprehensively considered. A 4-D convolutional neural-network (4-D CNN) algorithm was then proposed to associate all information from the EEG signals and the changes in the human state and behavioral performance. A 4-D CNN achieves superior forecasting performance over 2-D CNN, 3-D CNN, and shallow networks. The results showed a 3.82% improvement in the root mean-square error, a 3.45% improvement in the error rate, and a 11.98% improvement in the correlation coefficient with 4-D CNN compared with 3-D CNN. The 4-D CNN algorithm extracts the significant theta and alpha activations in the frontal and posterior cingulate cortices under distinct fatigue levels. This work contributes to enhancing our understanding of deep learning methods in the analysis of EEG signals. We even envision that deep learning might serve as a bridge between translation neuroscience and further real-world applications.

Measuring Correlates of Mental Workload During Simulated Driving Using cEEGrid Electrodes: A Test-Retest Reliability Analysis

The EEG reflects mental processes, especially modulations in the alpha and theta frequency bands are associated with attention and the allocation of mental resources. EEG has also been used to study mental processes while driving, both in real environments and in virtual reality. However, conventional EEG methods are of limited use outside of controlled laboratory settings. While modern EEG technologies offer hardly any restrictions for the user, they often still have limitations in measurement reliability. We recently showed that low-density EEG methods using film-based round the ear electrodes (cEEGrids) are well-suited to map mental processes while driving a car in a driving simulator. In the present follow-up study, we explored aspects of ecological and internal validity of the cEEGrid measurements. We analyzed longitudinal data of 127 adults, who drove the same driving course in a virtual environment twice at intervals of 12-15 months while the EEG was recorded. Modulations in the alpha and theta frequency bands as well as within behavioral parameters (driving speed and steering wheel angular velocity) which were highly consistent over the two measurement time points were found to reflect the complexity of the driving task. At the intraindividual level, small to moderate (albeit significant) correlations were observed in about 2/3 of the participants, while other participants showed significant deviations between the two measurements. Thus, the test-retest reliability at the intra-individual level was rather low and challenges the value of the application for diagnostic purposes. However, across all participants the reliability and ecological validity of cEEGrid electrodes were satisfactory in the context of driving-related parameters.

Alpha oscillations and event-related potentials reflect distinct dynamics of attribute construction and evidence accumulation in dietary decision making

How does regulatory focus alter attribute value construction (AVC) and evidence accumulation (EA)? We recorded electroencephalogram during food choices while participants responded naturally or regulated their choices by attending to health attributes or decreasing attention to taste attributes. Using a drift diffusion model, we predicted the time course of neural signals associated with AVC and EA. Results suggested that event-related potentials (ERPs) correlated with the time course of model-predicted taste-attribute signals, with no modulation by regulation. By contrast, suppression of frontal and occipital alpha power correlated with the time course of EA, tracked tastiness according to its goal relevance, and predicted individual variation in successful down-regulation of tastiness. Additionally, an earlier rise in frontal and occipital theta power represented food tastiness more strongly during regulation and predicted a weaker influence of food tastiness on behaviour. Our findings illuminate how regulation modifies the representation of attributes during the process of EA.

The impact of multisensory integration and perceptual load in virtual reality settings on performance, workload and presence

Real-world experience is typically multimodal. Evidence indicates that the facilitation in the detection of multisensory stimuli is modulated by the perceptual load, the amount of information involved in the processing of the stimuli. Here, we used a realistic virtual reality environment while concomitantly acquiring Electroencephalography (EEG) and Galvanic Skin Response (GSR) to investigate how multisensory signals impact target detection in two conditions, high and low perceptual load. Different multimodal stimuli (auditory and vibrotactile) were presented, alone or in combination with the visual target. Results showed that only in the high load condition, multisensory stimuli significantly improve performance, compared to visual stimulation alone. Multisensory stimulation also decreases the EEG-based workload. Instead, the perceived workload, according to the "NASA Task Load Index" questionnaire, was reduced only by the trimodal condition (i.e., visual, auditory, tactile). This trimodal stimulation was more effective in enhancing the sense of presence, that is the feeling of being in the virtual environment, compared to the bimodal or unimodal stimulation. Also, we show that in the high load task, the GSR components are higher compared to the low load condition. Finally, the multimodal stimulation (Visual-Audio-Tactile-VAT and Visual-Audio-VA) induced a significant decrease in latency, and a significant increase in the amplitude of the P300 potentials with respect to the unimodal (visual) and visual and tactile bimodal stimulation, suggesting a faster and more effective processing and detection of stimuli if auditory stimulation is included. Overall, these findings provide insights into the relationship between multisensory integration and human behavior and cognition.

Eye-head coordination and dynamic visual scanning as indicators of visuo-cognitive demands in driving simulator

Driving is an everyday task involving a complex interaction between visual and cognitive processes. As such, an increase in the cognitive and/or visual demands can lead to a mental overload which can be detrimental for driving safety. Compiling evidence suggest that eye and head movements are relevant indicators of visuo-cognitive demands and attention allocation. This study aims to investigate the effects of visual degradation on eye-head coordination as well as visual scanning behavior during a highly demanding task in a driving simulator. A total of 21 emmetropic participants (21 to 34 years old) performed dual-task driving in which they were asked to maintain a constant speed on a highway while completing a visual search and detection task on a navigation device. Participants did the experiment with optimal vision and with contact lenses that introduced a visual perturbation (myopic defocus). The results indicate modifications of eye-head coordination and the dynamics of visual scanning in response to the visual perturbation induced. More specifically, the head was more involved in horizontal gaze shifts when the visual needs were not met. Furthermore, the evaluation of visual scanning dynamics, based on time-based entropy which measures the complexity and randomness of scanpaths, revealed that eye and gaze movements became less explorative and more stereotyped when vision was not optimal. These results provide evidence for a reorganization of both eye and head movements in response to increasing visual-cognitive demands during a driving task. Altogether, these findings suggest that eye and head movements can provide relevant information about visuo-cognitive demands associated with complex tasks. Ultimately, eye-head coordination and visual scanning dynamics may be good candidates to estimate drivers' workload and better characterize risky driving behavior.

Statistical Analysis of Brain Connectivity Estimators during Distracted Driving

This paper presents comparison of brain connectivity estimators of distracted drivers and non-distracted drivers based on statistical analysis. Twelve healthy volunteers with more than one year of driving experience participated in this experiment. Lane-keeping tasks and the Math problem-solving task were introduced in the experiment and EEGs (electroencephalogram) were used to record the brain waves. Granger-Geweke causality (GGC), directed transfer function (DTF) and partial directed coherence (PDC) brain connectivity estimation methods were used in brain connectivity analysis. Correlation test and a student's t-test were conducted on the connectivity matrixes. Results show a significant difference between the mean of distracted drivers and non-distracted driver's brain connectivity matrixes. GGC and DTF methods student's t-tests shows a p-value below 0.05 with the correlation coefficients varying from 0.62 to 0.38. PDC connectivity estimation method does not show a significant difference between the connectivity matrixes means unless it is compared with lane keeping task and the normal driving task. Furthermore, it shows a strong positive correlation between the connectivity matrixes.

Inter- and Intra-subject Template-Based Multivariate Synchronization Index Using an Adaptive Threshold for SSVEP-Based BCIs

The steady-state visually evoked potential (SSVEP) has been widely used in brain-computer interfaces (BCIs). Many studies have proved that the Multivariate synchronization index (MSI) is an efficient method for recognizing the frequency components in SSVEP-based BCIs. Despite its success, the recognition accuracy has not been satisfactory because the simplified pre-constructed sine-cosine waves lack abundant features from the real electroencephalogram (EEG) data. Recent advances in addressing this issue have achieved a significant improvement in recognition accuracy by using individual calibration data. In this study, a new extension based on inter- and intra-subject template signals is introduced to improve the performance of the standard MSI method. Through template transfer, inter-subject similarity and variability are employed to enhance the robustness of SSVEP recognition. Additionally, most existed methods for SSVEP recognition utilize a fixed time window (TW) to perform frequency domain analysis, which limits the information transfer rate (ITR) of BCIs. For addressing this problem, a novel adaptive threshold strategy is integrated into the extension of MSI, which uses a dynamic window to extract the temporal features of SSVEPs and recognizes the stimulus frequency based on a pre-set threshold. The pre-set threshold contributes to obtaining an appropriate and shorter signal length for frequency recognition and filtering ignored-invalid trials. The proposed method is evaluated on a 12-class SSVEP dataset recorded from 10 subjects, and the result shows that this achieves higher recognition accuracy and information transfer rate when compared with the CCA, MSI, Multi-set CCA, and Individual Template-based CCA. This paper demonstrates that the proposed method is a promising approach for developing high-speed BCIs.

EEG dynamics and neural generators of psychological flow during one tightrope performance

Psychological “flow” emerges from a goal requiring action, and a match between skills and challenge. Using high-density electroencephalographic (EEG) recording, we quantified the neural generators characterizing psychological “flow” compared to a mindful “stress” state during a professional tightrope performance. Applying swLORETA based on self-reported mental states revealed the right superior temporal gyrus (BA38), right globus pallidus, and putamen as generators of delta, alpha, and beta oscillations, respectively, when comparing “flow” versus “stress”. Comparison of “stress” versus “flow” identified the middle temporal gyrus (BA39) as the delta generator, and the medial frontal gyrus (BA10) as the alpha and beta generator. These results support that “flow” emergence required transient hypo-frontality. Applying swLORETA on the motor command represented by the tibialis anterior EMG burst identified the ipsilateral cerebellum and contralateral sensorimotor cortex in association with on-line control exerted during both “flow” and “stress”, while the basal ganglia was identified only during “flow”.

EEG Theta Power Activity Reflects Workload among Army Combat Drivers: An Experimental Study

We aimed to evaluate the effects of mental workload variations, as a function of the road environment, on the brain activity of army drivers performing combat and non-combat scenarios in a light multirole vehicle dynamic simulator. Forty-one non-commissioned officers completed three standardized driving exercises with different terrain complexities (low, medium, and high) while we recorded their electroencephalographic (EEG) activity. We focused on variations in the theta EEG power spectrum, a well-known index of mental workload. We also assessed performance and subjective ratings of task load. The theta EEG power spectrum in the frontal, temporal, and occipital areas were higher during the most complex scenarios. Performance (number of engine stops) and subjective data supported these findings. Our findings strengthen previous results found in civilians on the relationship between driver mental workload and the theta EEG power spectrum. This suggests that EEG activity can give relevant insight into mental workload variations in an objective, unbiased fashion, even during real training and/or operations. The continuous monitoring of the warfighter not only allows instantaneous detection of over/underload but also might provide online feedback to the system (either automated equipment or the crew) to take countermeasures and prevent fatal errors.

Challenge for Affective Brain-Computer Interfaces: Non-stationary Spatio-spectral EEG Oscillations of Emotional Responses

Electroencephalogram (EEG)-based affective brain-computer interfaces (aBCIs) have been attracting ever-growing interest and research resources. Whereas most previous neuroscience studies have focused on single-day/-session recording and sensor-level analysis, less effort has been invested in assessing the fundamental nature of non-stationary EEG oscillations underlying emotional responses across days and individuals. This work thus aimed to use a data-driven blind source separation method, i.e., independent component analysis (ICA), to derive emotion-relevant spatio-spectral EEG source oscillations and assess the extent of non-stationarity. To this end, this work conducted an 8-day music-listening experiment (i.e., roughly interspaced over 2 months) and recorded whole-scalp 30-ch EEG data from 10 subjects. Given the large size of the data (i.e., from 80 sessions), results indicated that EEG non-stationarity was clearly revealed in the numbers and locations of brain sources of interest as well as their spectral modulation to the emotional responses. Less than half of subjects (two to four) showed the same relatively day-stationary (source reproducibility >6 days) spatio-spectral tendency towards one of the binary valence and arousal states. This work substantially advances the previous work by exploiting intra- and inter-individual EEG variability in an ecological multiday scenario. Such EEG non-stationarity may inevitably present a great challenge for the development of an accurate, robust, and generalized emotion-classification model.

Exploring the Brain Responses to Driving Fatigue Through Simultaneous EEG and fNIRS Measurements

Fatigue is one problem with driving as it can lead to difficulties with sustaining attention, behavioral lapses, and a tendency to ignore vital information or operations. In this research, we explore multimodal physiological phenomena in response to driving fatigue through simultaneous functional near-infrared spectroscopy (fNIRS) and electroencephalography (EEG) recordings with the aim of investigating the relationships between hemodynamic and electrical features and driving performance. Sixteen subjects participated in an event-related lane-deviation driving task while measuring their brain dynamics through fNIRS and EEGs. Three performance groups, classified as Optimal, Suboptimal, and Poor, were defined for comparison. From our analysis, we find that tonic variations occur before a deviation, and phasic variations occur afterward. The tonic results show an increased concentration of oxygenated hemoglobin (HbO₂) and power changes in the EEG theta, alpha, and beta bands. Both dynamics are significantly correlated with deteriorated driving performance. The phasic EEG results demonstrate event-related desynchronization associated with the onset of steering vehicle in all power bands. The concentration of phasic HbO₂ decreased as performance worsened. Further, the negative correlations between tonic EEG delta and alpha power and HbO₂ oscillations suggest that activations in HbO₂ are related to mental fatigue. In summary, combined hemodynamic and electrodynamic activities can provide complete knowledge of the brain's responses as evidence of state changes during fatigue driving.

Neural Comodulation of Independent Brain Processes Related to Multitasking

Distracted driving is regarded as an integrated task requiring different regions of the brain to receive sensory data, coordinate information, make decisions, and synchronize movements. In this paper, we applied an independent modulator analysis (IMA) method to temporally independent electroencephalography (EEG) components to understand how the human executive control system coordinates different brain regions to simultaneously perform multiple tasks with distractions presented in different modalities. The behavioral results showed that the reaction time (RT) in response to traffic events increased while multitasking. Moreover, the RT was longer when the distractor was presented in an auditory form versus a visual form. The IMA results showed that there were performance-related IMs coordinating different brain regions during distracted driving. The component spectral fluctuations affected by the modulators were distinct between the single- and dual-task conditions. Specifically, more modulatory weight was projected to the occipital region to address the additional distracting stimulus in both visual and auditory modality in the dual-task conditions. A comparison of modulatory weights between auditory and visual distractors showed that more modulatory weight was projected to the frontal region during the processing of the auditory distractor. This paper provides valuable insights into the temporal dynamics of attentional modulation during multitasking as well as an understanding of the underlying brain mechanisms that mediate the synchronization across brain regions and govern the allocation of attention in distracted driving.

Demonstrating Brain-Level Interactions Between Visuospatial Attentional Demands and Working Memory Load While Driving Using Functional Near-Infrared Spectroscopy

Driving is a complex task concurrently drawing on multiple cognitive resources. Yet, there is a lack of studies investigating interactions at the brain-level among different driving subtasks in dual-tasking. This study investigates how visuospatial attentional demands related to increased driving difficulty interacts with different working memory load (WML) levels at the brain level. Using multichannel whole-head high density functional near-infrared spectroscopy (fNIRS) brain activation measurements, we aimed to predict driving difficulty level, both separate for each WML level and with a combined model. Participants drove for approximately 60 min on a highway with concurrent traffic in a virtual reality driving simulator. In half of the time, the course led through a construction site with reduced lane width, increasing visuospatial attentional demands. Concurrently, participants performed a modified version of the n-back task with five different WML levels (from 0-back up to 4-back), forcing them to continuously update, memorize, and recall the sequence of the previous 'n' speed signs and adjust their speed accordingly. Using multivariate logistic ridge regression, we were able to correctly predict driving difficulty in 75.0% of the signal samples (1.955 Hz sampling rate) across 15 participants in an out-of-sample cross-validation of classifiers trained on fNIRS data separately for each WML level. There was a significant effect of the WML level on the driving difficulty prediction accuracies [range 62.2-87.1%; χ2(4) = 19.9, p < 0.001, Kruskal-Wallis H test] with highest prediction rates at intermediate WML levels. On the contrary, training one classifier on fNIRS data across all WML levels severely degraded prediction performance (mean accuracy of 46.8%). Activation changes in the bilateral dorsal frontal (putative BA46), bilateral inferior parietal (putative BA39), and left superior parietal (putative BA7) areas were most predictive to increased driving difficulty. These discriminative patterns diminished at higher WML levels indicating that visuospatial attentional demands and WML involve interacting underlying brain processes. The changing pattern of driving difficulty related brain areas across WML levels could indicate potential changes in the multitasking strategy with level of WML demand, in line with the multiple resource theory.