Long-term and within-day variability of working memory performance and EEG in individuals

An EEG study of human trust in autonomous vehicles based on graphic theoretical analysis

With the development of autonomous vehicle technology, human-centered transport research will likely shift to the interaction between humans and vehicles. This study focuses on the human trust variation in autonomous vehicles (AVs) as the technology becomes increasingly intelligent. This study uses electroencephalogram data to analyze human trust in AVs during simulated driving conditions. Two driving conditions, the semi-autonomous and the autonomous, which correspond to the two highest levels of automatic driving, are used for the simulation, accompanied by various driving and car conditions. The graph theoretical analysis (GTA) is the primary method for data analysis. In semi-autonomous driving mode, the local efficiency and cluster coefficient are lower in car-normal conditions than in car-malfunction conditions with the car approaching. This finding suggests that the human brain has a strong information processing ability while facing predictable potential hazards. However, when it comes to a traffic light with a car malfunctioning under the semi-autonomous driving mode, the characteristic path length is higher for the car malfunction manifesting a weak information processing ability while facing unpredictable potential hazards. Furthermore, in fully automatic driving conditions, participants cannot do anything and need low-level brain function to take emergency actions as lower local efficiency and small worldness for car malfunction. Our results shed light on the design of the human-machine interaction and human factor engineering on the high level of an autonomous vehicle.

Long-term stability of resting state EEG-based linear and nonlinear measures

This preliminary study is aimed to evaluate the stability of various linear and nonlinear EEG measures over three years on healthy adults. The linear measures, relative powers of EEG frequency bands, interhemispheric (IHAS) and spectral (SASI) asymmetries plus nonlinear Higuchi's fractal dimension (HFD) and detrended fluctuation analyses (DFA), have been calculated from the resting state eyes closed EEG of 17 participants during two sessions separated over three years. Our results indicate that the stability is highest for the nonlinear (HFD and DFA) and the linear (relative powers of EEG frequency bands) EEG measures that use the signal from a single EEG channel and frequency band, followed by the SASI employing signals from a single channel and two frequency bands and lowest for the IHAS employing signals from two channels. The result support the prospect of using EEG-based measures in clinical practice.

Decoding human mental states by whole-head EEG+fNIRS during category fluency task performance

OBJECTIVE

Concurrent scalp electroencephalography (EEG) and functional near-infrared spectroscopy (fNIRS), which we refer to as EEG+fNIRS, promises greater accuracy than the individual modalities while remaining nearly as convenient as EEG. We sought to quantify the hybrid system's ability to decode mental states and compare it with its unimodal components.

APPROACH

We recorded from healthy volunteers taking the category fluency test and applied machine learning techniques to the data.

MAIN RESULTS

EEG+fNIRS's decoding accuracy was greater than that of its subsystems, partly due to the new type of neurovascular features made available by hybrid data.

SIGNIFICANCE

Availability of an accurate and practical decoding method has potential implications for medical diagnosis, brain-computer interface design, and neuroergonomics.

Why Is Working Memory Performance Unstable? A Review of 21 Factors

In this paper, we systematically reviewed twenty-one factors that have been shown to either vary with or influence performance on working memory (WM) tasks. Specifically, we review previous work on the influence of intelligence, gender, age, personality, mental illnesses/medical conditions, dieting, craving, stress/anxiety, emotion/motivation, stereotype threat, temperature, mindfulness training, practice, bilingualism, musical training, altitude/hypoxia, sleep, exercise, diet, psychoactive substances, and brain stimulation on WM performance. In addition to a review of the literature, we suggest several frameworks for classifying these factors, identify shared mechanisms between several variables, and suggest areas requiring further investigation. This review critically examines the breadth of research investigating WM while synthesizing the results across related subfields in psychology.

EEG Cortical Connectivity Analysis of Working Memory Reveals Topological Reorganization in Theta and Alpha Bands

Numerous studies have revealed various working memory (WM)-related brain activities that originate from various cortical regions and oscillate at different frequencies. However, multi-frequency band analysis of the brain network in WM in the cortical space remains largely unexplored. In this study, we employed a graph theoretical framework to characterize the topological properties of the brain functional network in the theta and alpha frequency bands during WM tasks. Twenty-eight subjects performed visual n-back tasks at two difficulty levels, i.e., 0-back (control task) and 2-back (WM task). After preprocessing, Electroencephalogram (EEG) signals were projected into the source space and 80 cortical brain regions were selected for further analysis. Subsequently, the theta- and alpha-band networks were constructed by calculating the Pearson correlation coefficients between the power series (obtained by concatenating the power values of all epochs in each session) of all pairs of brain regions. Graph theoretical approaches were then employed to estimate the topological properties of the brain networks at different WM tasks. We found higher functional integration in the theta band and lower functional segregation in the alpha band in the WM task compared with the control task. Moreover, compared to the 0-back task, altered regional centrality was revealed in the 2-back task in various brain regions that mainly resided in the frontal, temporal and occipital lobes, with distinct presentations in the theta and alpha bands. In addition, significant negative correlations were found between the reaction time with the average path length of the theta-band network and the local clustering of the alpha-band network, which demonstrates the potential for using the brain network metrics as biomarkers for predicting the task performance during WM tasks.

Task-Independent Mental Workload Classification Based Upon Common Multiband EEG Cortical Connectivity

Efficient classification of mental workload, an important issue in neuroscience, is limited, so far to single task, while cross-task classification remains a challenge. Furthermore, network approaches have emerged as a promising direction for studying the complex organization of the brain, enabling easier interpretation of various mental states. In this paper, using two mental tasks (N-back and mental arithmetic), we present a framework for cross- as well as within-task workload discrimination by utilizing multiband electroencephalography (EEG) cortical brain connectivity. In detail, we constructed functional networks in EEG source space in different frequency bands and considering the individual functional connections as classification features, we identified salient feature subsets based on a sequential feature selection algorithm. These connectivity subsets were able to provide accuracy of 87% for cross-task, 88% for N-back task, and 86% for mental arithmetic task. In conclusion, our method achieved to detect a small number of discriminative interactions among brain areas, leading to high accuracy in both within-task and cross-task classifications. In addition, the identified functional connectivity features, the majority of which were detected in frontal areas in theta and beta frequency bands, helped delineate the shared as well as the distinct neural mechanisms of the two mental tasks.

Analyze the beta waves of electroencephalogram signals from young musicians and non-musicians in major scale working memory task

Electroencephalograms can record wave variations in any brain activity. Beta waves are produced when an external stimulus induces logical thinking, computation, and reasoning during consciousness. This work uses the beta wave of major scale working memory N-back tasks to analyze the differences between young musicians and non-musicians. After the feature analysis uses signal filtering, Hilbert-Huang transformation, and feature extraction methods to identify differences, k-means clustering algorithm are used to group them into different clusters. The results of feature analysis showed that beta waves significantly differ between young musicians and non-musicians from the low memory load of working memory task.

Randomized double-blind comparison of cognitive and EEG effects of lacosamide and carbamazepine

Differential effectiveness of antiepileptic drugs (AEDs) is more commonly determined by tolerability than efficacy. Cognitive effects of AEDs can adversely affect tolerability and quality of life. This study evaluated cognitive and EEG effects of lacosamide (LCM) compared with carbamazepine immediate-release (CBZ-IR). A randomized, double-blind, double-dummy, two-period crossover, fixed-dose study in healthy subjects compared neuropsychological and EEG effects of LCM (150mg, b.i.d.) and CBZ-IR (200mg, t.i.d.). Testing was conducted at screening, predrug baseline, the end of each treatment period (3-week titration; 3-week maintenance), and the end of each washout period (4weeks after treatment). A composite Z-score was derived for the primary outcome variable (computerized cognitive tests and traditional neuropsychological measures) and separately for the EEG measures. Other variables included individual computer, neuropsychological, and EEG scores and adverse events (AEs). Subjects included 60 healthy adults (57% female; mean age: 34.4years [SD: 10.5]); 44 completed both treatments; 41 were per protocol subjects. Carbamazepine immediate-release had worse scores compared with LCM for the primary composite neuropsychological outcome (mean difference=0.33 [SD: 1.36], p=0.011) and for the composite EEG score (mean difference=0.92 [SD: 1.77], p=0.003). Secondary analyses across the individual variables revealed that CBZ-IR was statistically worse than LCM on 36% (4/11) of the neuropsychological tests (computerized and noncomputerized) and 0% of the four EEG measures; none favored CBZ-IR. Drug-related AEs occurred more with CBZ-IR (49%) than LCM (22%). Lacosamide had fewer untoward neuropsychological and EEG effects and fewer AEs and AE-related discontinuations than CBZ-IR in healthy subjects. Lacosamide exhibits a favorable cognitive profile.

Time course of EEG oscillations during repeated listening of a well-known aria

While previous studies have analyzed mean neurophysiological responses to musical stimuli, the current study aimed to identify specific time courses of electroencephalography (EEG) oscillations, which are associated with dynamic changes in the acoustic features of the musical stimulus. In addition, we were interested in whether these time courses change during a repeated presentation of the same musical piece. A total of 16 subjects repeatedly listened to the well-known aria “Nessun dorma,” sung by Paul Potts, while continuous 128-channel EEG and heart rate, as well as electrodermal responses, were recorded. The time courses for the EEG oscillations were calculated using a time resolution of 1 second for several frequency bands, on the basis of individual alpha-peak frequencies (theta, low alpha-1, low alpha-2, upper alpha, and beta). For all frequency bands, we identified a more or less continuous increase in power relative to a baseline period, indicating strong event-related synchronization (ERS) during music listening. The ERS time courses, however, did not correlate strongly with the time courses of the acoustic features of the aria. In addition, we did not observe changes in EEG oscillations after repeated presentation of the same musical piece. Aside from this distinctive feature, we identified a remarkable variability in EEG oscillations, both within and between the repeated presentations of the aria. We interpret the continuous increase in ERS observed in all frequency bands during music listening as an indicator of a particular neurophysiological and psychological state evoked by music listening. We suggest that this state is characterized by increased internal attention (accompanied by reduced external attention), increased inhibition of brain networks not involved in the generation of this internal state, the maintenance of a particular level of general alertness, and a type of brain state that can be described as “mind wandering.” The overall state can be categorized as a psychological process that may be seen as a “drawing in” to the musical piece. However, this state is not stable and varies considerably throughout the music listening session and across subjects. Most important, however, is the finding that the neurophysiological activations occurring during music listening are dynamic and not stationary.

Neurophysiological pharmacodynamic measures of groups and individuals extended from simple cognitive tasks to more "lifelike" activities

OBJECTIVE

Extend a method to track neurophysiological pharmacodynamics during repetitive cognitive testing to a more complex "lifelike" task.

METHODS

Alcohol was used as an exemplar psychoactive substance. An equation, derived in an exploratory analysis to detect alcohol's EEGs effects during repetitive cognitive testing, was validated in a Confirmatory Study on a new group whose EEGs after alcohol and placebo were recorded during working memory testing and while operating an automobile driving simulator.

RESULTS

The equation recognized alcohol by combining five times beta plus theta power. It worked well (p < .0001) when applied to both tasks in the confirmatory group. The maximum EEG effect occurred 2-2.5 h after drinking (>1 h after peak BAC) and remained at 90% at 3.5-4 h (BAC < 50% of peak). Individuals varied in the magnitude and timing of the EEG effect.

CONCLUSION

The equation tracked the EEG response to alcohol in the Confirmatory Study during both repetitive cognitive testing and a more complex "lifelike" task. The EEG metric was more sensitive to alcohol than several autonomic physiological measures, task performance measures or self-reports.

SIGNIFICANCE

Using EEG as a biomarker to track neurophysiological pharmacodynamics during complex "lifelike" activities may prove useful for assessing how drugs affect integrated brain functioning.

Long-Term Neurophysiologic Impact of Childhood Sleep Disordered Breathing on Neurocognitive Performance

STUDY OBJECTIVE

To determine the impact of sleep disordered breathing (SDB) in children on neurocognitive function 5 years later.

DESIGN SETTING AND PARTICIPANTS

A subgroup of 43 children from the Tucson Children's Assessment of Sleep Apnea Study (TuCASA) who had SDB (RDI ≥ 6 events/hour) at their initial exam (ages 6-11 years) were matched on the basis of age (within 1 year), gender and ethnicity (Anglo/Hispanic) to 43 children without SDB (Control, RDI ≤ 4 events/hour). The Sustained Working Memory Task (SWMT) which combines tests of working memory (1-Back Task), reaction time (Simple Reaction Time) and attention (Multiplexing Task) with concurrent electroencephalographic monitoring was administered approximately 5 years later.

RESULTS

There were no differences in performance on the working memory, reaction time and attention tests between the SDB and Control groups. However, the SDB group exhibited lower P300 evoked potential amplitudes during the Simple Reaction Time and Multiplexing Tasks. Additionally, peak alpha power during the Multiplexing Task was lower in the SDB Group with a similar trend in the Simple Reaction Time Task (p=0.08).

CONCLUSIONS

SDB in children may cause subtle long-term changes in executive function that are not detectable with conventional neurocognitive testing and are only evident during neuroelectrophysiologic monitoring.