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• Working memory task plays a vital role in the classification of aging. • MRMR and machine learning techniques were applied to age-related EEG. • Decision tree algorithm gave highest accuracy 87.5% with working memory test. The changes in electroencephalogram (EEG) signals that reflect the changes in physiological structure, cognitive functions, and activities have been observed in healthy aging adults. It is unknown that when the brain aging initiates and whether these age-related alterations can be associated with incipient neurodegenerative diseases in healthy elderly individuals. We employed feature extraction and classification methods to classify and compare the EEG signals of middle-aged and elderly age groups. This study included 20 healthy middle-aged and 20 healthy elderly subjects. The EEG signals were recorded during a resting state (eyes-open and eyes-closed) and during a working memory (WM) task using eight electrodes. The minimum redundancy maximum relevance technique was employed in the selection of the optimal feature. Four classification methods, including decision tree, support vector machine, Naïve Bayes, and K-nearest neighbor, were used to distinguish the elderly age group from the middle-aged group based on their EEG signals. In the resting state, a good correlation was observed among absolute power delta and theta bands and aging, whereas between beta absolute power and aging, a WM task correlation was observed. The results also indicated that the mean frequency and absolute power might be useful for the prediction and classification of EEG signals in aging individuals. Furthermore, the use of the decision tree method in a WM task state distinguished the elderly group from the middle-aged group with an accuracy of 87.5%. Working memory could play a vital role in the optimization of classification of EEG signals in aging and discrimination of age-related issues associated with neurodegeneration. [ABSTRACT FROM AUTHOR]

Value-driven attention capture (VDAC) occurs when previously rewarded stimuli capture attention and impair goal-directed behavior. In a working memory (WM) task with VDAC-related distractors, we observe behavioral variability both within and across individuals. Individuals differ in their ability to maintain relevant information and ignore distractions. These cognitive components shift over time with changes in motivation and attention, making it difficult to identify underlying neural mechanisms of individual differences. In this study, we develop the first participant-specific feedforward neural network models of reaction time from neural data during a VDAC WM task. We used short epochs of electroencephalography (EEG) data from 16 participants to develop the feedforward neural network (NN) models of RT aimed at understanding both WM and VDAC. Using general linear models (GLM), we identified 20 EEG features to predict RT across participants ( r = 0.53 ± 0.08 ). The linear model was compared to the NN model, which improved the predicted trial-by-trial RT for all participants ( r = 0.87 ± 0.04 ). We found that right frontal gamma-band activity and fronto-posterior functional connectivity in the alpha, beta, and gamma bands explain individual differences. Our study shows that NN models can link neural activity to highly variable behavior and can identify potential new targets for neuromodulation interventions. [ABSTRACT FROM AUTHOR]

An individualized model that predicts trial‐by‐trial working memory performance is instrumental for personalized interventions. Here, we propose a single‐trial electroencephalography (EEG) classification process predicting individuals' responses, that is, target correct versus target non‐correct during a working memory task, N‐back. We used event‐related (de‐)synchronization (ERD and ERS) prior to an anticipatory cue as features. The proposed comprehensive process addresses single‐trial EEG classification challenges such as temporal overlap between training and testing datasets, feature selection's stability, and significance of the classification accuracy which have been often overlooked. Our model identified for the first time a few (ranged between 4 and 10) brain regions and oscillations where ERD and ERS predicted an individual's performance. Mean (SD) prediction accuracy across 50 participants (mean age [SD] = 28.56 [7.55]) was 69.51% (8.41). Accuracy was significantly above chance in 34 participants. This machine learning‐based approach provides a proof of principle for individualizing EEG targets for potential interventions. [ABSTRACT FROM AUTHOR]

Motivationally/emotionally engaging stimuli are strong competitors for the limited capacity of sensory and cognitive systems. Thus, they often act as distractors, interfering with performance in concurrent primary tasks. Keeping task-relevant information in focus while suppressing the impact of distracting stimuli is one of the functions of working memory (WM). Macroscopic brain oscillations in the alpha band (8–13 Hz) have recently been identified as a neural correlate of WM processing. Using electroencephalography, we examined the extent to which changes in alpha power and inter-site connectivity during a typical WM task are sensitive to load and emotional distraction. Participants performed a lateralized change-detection task with two levels of load (four vs. two items), which was preceded by naturalistic scenes rated either as unpleasant or neutral, acting as distractors. The results showed the expected parieto-occipital alpha reduction in the hemisphere contralateral to the WM task array, compared to the ipsilateral hemisphere, during the retention interval. Selectively heightened oscillatory coupling between frontal and occipital sensors was observed (1) during the retention interval as a function of load, and (2) upon the onset of the memory array, after viewing neutral compared to unpleasant distractors. At the end of the retention interval, we observed greater coupling during the unpleasant compared to the neutral condition. These findings are consistent with the notions that (1) representing more items in WM requires greater interconnectivity across cortical areas, and (2) unpleasant emotional distractors interfere with subsequent WM processing by disrupting processing during the encoding stage. [ABSTRACT FROM AUTHOR]