EEG Spectral Data Research Articles

A Three-Class Classification of Cognitive Workload Based on EEG Spectral Data

Evaluation of cognitive workload finds its application in many areas, from educational program assessment through professional driver health examination to monitoring the mental state of people carrying out jobs of high responsibility, such as pilots or airline traffic dispatchers. Estimation of multilevel cognitive workload is a task usually realized in a subject-dependent way, while the present research is focused on developing the procedure of subject-independent evaluation of cognitive workload level. The aim of the paper is to estimate cognitive workload level in accordance with subject-independent approach, applying classical machine learning methods combined with feature selection techniques. The procedure of data acquisition was based on registering the EEG signal of the person performing arithmetical tasks divided into six intervals of advancement. The analysis included the stages of preprocessing, feature extraction, and selection, while the final step covered multiclass classification performed with several models. The results discussed show high maximal accuracies achieved: ~91% for both the validation dataset and for the cross-validation approach for kNN model.

Learning to multitask: Effects of video game practice on electrophysiological indices of attention and resource allocation

Changes in attention allocation with complex task learning reflect processing automatization and more efficient control. We studied these changes using ERP and EEG spectral analyses in subjects playing Space Fortress, a complex video game comprising standard cognitive task components. We hypothesized that training would free up attentional resources for a secondary auditory oddball task. Both P3 and delta EEG showed a processing trade-off between game and oddball tasks, but only some game events showed reduced attention requirements with practice. Training magnified a transient increase in alpha power following both primary and secondary task events. This contrasted with alpha suppression observed when the oddball task was performed alone, suggesting that alpha may be related to attention switching. Hence, P3 and EEG spectral data are differentially sensitive to changes in attentional processing occurring with complex task training.

EEG spectral power and negative symptoms in at-risk individuals predict transition to psychosis

Introduction EEG power in the delta, theta and beta1 bands has been shown to be positively correlated with negative symptoms in first episode psychotic patients. The present study investigates this correlation in an “at risk mental state for psychosis” (ARMS) with the aim to improve prediction of transition to psychosis. Methods Thirteen ARMS patients with later transition to psychosis (ARMS-T) and fifteen without (follow-up period of at least 4 years) (ARMS-NT) were investigated using spectral resting EEG data (of 8 electrodes over the fronto-central scalp area placed according to the 10–20 system) and summary score of the Scale for the Assessment of Negative Symptoms (SANS). Linear regressions were used to evaluate the correlation of SANS and EEG power in seven bands (delta, theta, alpha1, alpha2, beta1, beta2, beta3) in both ARMS groups and logistic regressions were used to predict transition to psychosis. Potentially confounding factors were controlled. Results ARMS-T and ARMS-NT showed differential correlations of EEG power and SANS in delta, theta, and beta1 bands (p < .05): ARMS-T showed positive and ARMS-NT negative correlations. Logistic regressions showed that neither SANS score nor EEG spectral power alone predicted transition to psychosis. However, SANS score in combination with power in the delta, theta, beta1, and beta2 bands, respectively, predicted transition significantly (p < .03). Conclusions ARMS-T and ARMS-NT show differential correlations of SANS summary score and EEG power in delta, theta, and beta bands. Prediction of transition to psychosis is possible using combined information from a negative symptom scale and EEG spectral data.

EEG Pattern Analysis for Physiological Indicators of Mental Fatigue in Simulated Air Traffic Control Tasks

Objective: This study was designed to identify potential neurophysiologic markers and patterns of mental fatigue among air traffic controllers in their work environment.Background: The monitoring of mental fatigue in air traffic controllers has been of interest as their tasks involve high cognitive workload and are also critical to the safety of the public.Method: High-density electroencephalogram (EEG) was used to record 2-hour long air traffic control studies in eleven participants. Participants were asked to perform realistic tasks in a simulation, to operate a virtual air traffic control system. Fourier Transforms were used to estimate EEG power spectrum, statistical tests were implemented to reveal EEG spatial pattern changes caused by the time-on-task. The concept of mental state transition was introduced to study the development of certain mental states which are related to the mental fatigue. Results: The observation of EEG spectral data over a period of time revealed statistically significant changes spatially localized to central and parietal cortices. Rhythmic EEG activity within theta, alpha, and beta bands indicates transitions among mental states, which appear to be promising indicators for the development of mental fatigue. Mental fatigue indicated by the transition of mental states was estimated to approximately 70 minutes after the time on task. Application: This study can build the foundation to develop promising technologies for real time monitoring of mental fatigue, which will increase public safety and proper human resource planning.

The effect of cadmium and experimental diabetes on EEG spectral data.

Fifty-two healthy male Swiss albino rats, aged three months, were used in this study. They were divided into four groups: control (C), diabetic (D), cadmium (Cd) and diabetic + Cd (D + Cd). Diabetes was induced in the D and D + Cd groups by administration of alloxane (5 mg/100 g). After this treatment, the Cd and D + Cd groups were injected with CdCl2 i.p. (2 mg/kg/week). At the end of the two month experimental period, EEGs of the four groups were recorded and amplitude spectral analysis was computed by Fast Fourier Transform (FFT) algorithm. Significant amplitude (dB) increment was observed in 16-30 Hz of Cd group compared with C and D groups. Significant amplitude decrement was found in the 2-4 Hz frequency band of the D + Cd group compared with C, Cd and D groups.

Life-span changes in EEG spectral amplitude, amplitude variability and mean frequency

Objectives: The objectives were to assess life-span alterations in intrasubject variability of EEG spectral amplitudes and in amplitude and mean frequency computed from spectral amplitude profiles.Methods: EEG was recorded from the central and occipital scalp of 222 healthy males aged 4 to 90 years. Amplitude spectral profiles derived from FFT procedures provided data for the computation of amplitude variability (ampCV), absolute and relative amplitude and mean frequency for each of five spectral bands. Analysis of variance, coefficient of correlation and t test were employed in data analyses.Results: The ampCV measure did not provide direct evidence of age-related intrasubject changes in EEG frequency but was useful for indexing the presence of rhythmic EEG activity. Marked decreases in absolute amplitude occurred during childhood with little change thereafter. Age changes in relative amplitude were more complex and differed for slow and fast EEG activity. We also report that an algorithm frequently used to estimate mean frequency can introduce consistent bias into mean frequency computations under some conditions.Conclusions: Life-span changes in measured EEG characteristics were generally consistent with those from earlier studies. It is important that investigators validate and describe procedures used to determine mean frequency of EEG spectral data.

From the `EEG age' to a rational scale of brain electric maturation

The aim of the present study was to propose an improved method of quantitative assessment of EEG age-related changes. 40 EEG recordings of healthy subjects (aged 0.7–78 years) were analysed. Multidimensional scaling of EEG spectral data indicated a presence of an `age factor' related non-linearly to the chronological age. Relative integrals of FFT spectra in 6 frequency bands were utilized as predictors of age or, alternatively, logarithmized age. Three regression models based on EEG spectral indicators were examined. Regression from logarithmic predictors to logarithm of age performed best in terms of linearity and residual errors. As a result, the Brain Electric Maturation Scale was proposed, being defined by the logarithm of ratio of the age predicted from the EEG data and chronological age. The scale could serve as an objective measure of brain maturation in children, or as an age-independent indicator of slow EEG abnormalities.

Genetic influences on the spontaneous EEG: An examination of 15‐year‐old and 17‐year‐old twins

In this study, the authors investigated the presence of genetic influences and development on the spontaneous electroencephalogram (EEG) by examining the similarity of 15‐ and 17‐year‐old monozygotic (MZ) and dizygotic (DZ) twins. A total of 50 (nMZ = 33, nDZ = 17) 15‐year‐old and 69 (nMZ = 45, nDZ = 24) 17‐year‐old twin pairs were assessed. Spontaneous EEG was recorded from the vertex and 2 lateral posterior sites while participants rested with eyes closed. Measures of both absolute and relative power were derived for delta, theta, alpha, and beta frequency bands. The intraclass correlations of all bands across all sites for both absolute and relative power were significant for the MZ twins, indicating moderate to high degrees of MZ similarity on these measures. All MZ intraclass correlations were larger than the corresponding DZ correlations, and they were significantly larger for over 70% of the MZ‐DZ comparisons involving the 2 age cohorts. Biometric modeling of EEG spectral data suggested a substantial genetic effect on EEG, with over 70% of 54 calculated heritability coefficients ≥ .70. This pattern of findings suggests a genetic influence on the spontaneous EEG of both middle and late adolescent children. Analysis of EEG power as a function of age revealed a significant decrease in all frequency bands across all sites from age 15 to 17. This EEG “power drop” most likely reflects the result of developmental changes in brain structure and metabolism occurring during the final stages of adolescent central nervous system maturation.

Application of quantitative EEG analysis to workload assessment in an advanced aircraft simulator

A fully portable quantitative EEG assessment system was used to evaluate workload in an advanced technology aircraft simulator. Air refueling and landing approach tasks were each performed at two difficulty levels in 15 Air Force pilots. Averaged and trended EEG spectral data were compared in the 8-12 Hz band to identify functional requirements for increased workload within and between tasks. A progressive suppression of 8-12 Hz activity at medial and right parietal sites accompanied increased workload in the air refueling task, while a sustained suppression at right and left temporal sites was associated with increased workload in the landing task. These findings suggest a potential electrophysiological index for workload. They also identify specific and differential cortical responses to visual integration in air refueling and working memory in ILS approach as primary correlates of the cognitive requirements for these tasks in these subjects.

Methodologie, limites et interet de la cartographie en electroencephalographie

Data processing is mandatory for EEG mapping. After having used a correct filter and ADC, spectral analysis is completed by interpolating between the spectral values obtained on each recording point to allow a sufficient number of values to be expressed in a grey or color scale on the skull projection plane. The obtention of valid results implies extreme care in data recording and processing. Transitory events (paroxysmal bursts) cannot be detected by spectral analysis. Nevertheless, EEG spectral data mapping is a very good technique to show abnormal EEG focuses in neurology, to study all sorts of neuropsychological reactivities, to follow the evolution of tracings during normal or abnormal aging, metabolic diseases or drug effects.

EEG signs of cerebrovascular disorder, using physical exercise as a provocative method

A group of patients suffering from cerebrovascular insufficiency was submitted to a pilot EEG investigation using physical exercise as a provocative method. Two control groups, one of young (20–30 years) and another of older (>50 years) subjects, were also investigated. Quantitative EEG spectral data, determined at rest and during a period after physical exercise, showed that deviations of the frequency distribution of the alpha band were related to cerebrovascular insufficiency.