EEG microstate changes according to mental fatigue induced by aircraft piloting simulation: An exploratory study

A comprehensive review of deep learning in EEG-based emotion recognition: classifications, trends, and practical implications

Emotion recognition utilizing EEG signals has emerged as a pivotal component of human-computer interaction. In recent years, with the relentless advancement of deep learning techniques, using deep learning for analyzing EEG signals has assumed a prominent role in emotion recognition. Applying deep learning in the context of EEG-based emotion recognition carries profound practical implications. Although many model approaches and some review articles have scrutinized this domain, they have yet to undergo a comprehensive and precise classification and summarization process. The existing classifications are somewhat coarse, with insufficient attention given to the potential applications within this domain. Therefore, this article systematically classifies recent developments in EEG-based emotion recognition, providing researchers with a lucid understanding of this field's various trajectories and methodologies. Additionally, it elucidates why distinct directions necessitate distinct modeling approaches. In conclusion, this article synthesizes and dissects the practical significance of EEG signals in emotion recognition, emphasizing its promising avenues for future application.

Assessment of rTMS treatment effects for methamphetamine use disorder based on EEG microstates

Microstates have been proposed as topographical maps representing large-scale resting-state networks and have recently been suggested as markers for methamphetamine use disorder (MUD). However, it is unknown whether and how they change after repetitive transcranial magnetic stimulation (rTMS) intervention. This study included a comprehensive subject population to investigate the effect of rTMS on MUD microstates. 34 patients with MUD underwent a 4-week randomized, double-blind rTMS intervention (active=17, sham=17). Two resting-state EEG recordings and VAS evaluations were conducted before and after the intervention period. Additionally, 17 healthy individuals were included as baseline controls. The modified k-means clustering method was used to calculate four microstates (MS-A∼MS-D) of EEG, and the FC network was also analyzed. The differences in microstate indicators between groups and within groups were compared. The durations of MS-A and MS-B microstates in patients with MUD were significantly lower than that in HC but showed significant improvements after rTMS intervention. Changes in microstate indicators were found to be significantly correlated with changes in craving level. Furthermore, selective modulation of the resting-state network by rTMS was observed in the FC network. The findings indicate that changes in microstates in patients with MUD are associated with craving level improvement following rTMS, suggesting they may serve as valuable evaluation markers.

Changes in electroencephalography microstates are associated with reduced levels of vigilance after sleep deprivation

Total sleep deprivation (TSD) negatively affects cognitive functions, especially vigilance attention, but studies on vigilance changes in terms of electroencephalography (EEG) microstates after TSD are limited. This study investigates the impact of TSD on vigilance attention, EEG microstates and its relationship. Thirty healthy adult males completed a psychomotor vigilance task (PVT) before, 24 h after, and 36 h after TSD while their EEG was recorded during rest. Microstate analysis revealed significant changes in the occurrence and contribution of microstate class B after TSD. Moreover, changes in the probability of transitioning between microstate classes A and D were observed, correlating with decreased vigilance. Specifically, a positive correlation was found between transitioning from class B to class C and vigilance, while a trend of negative correlation was observed between transitioning between classes A and D and vigilance. These findings indicate abnormal activity in the salience network and dorsal attention network following sleep deprivation. TSD impairs vigilance attention, as demonstrated by the effects on EEG microstate class B and the transitions between classes A and D. The study suggests its potential as an early warning indicator for predicting vigilance attention after sleep deprivation.

The Use of Quantitative Electroencephalography (QEEG) to Assess Post-COVID-19 Concentration Disorders in Professional Pilots: An Initial Concept

Announced by WHO in 2020, the global COVID-19 pandemic caused by SARS-CoV-2 has affected many people, leading to serious health consequences. These consequences are observed in the daily lives of infected patients as various dysfunctions and limitations. More and more people are suffering post-COVID-19 complications that interfere with or completely prevent them from working or even functioning independently on a daily basis. The aim of our study was to demonstrate that innovative quantitative electroencephalography (QEEG) can be used to assess cognitive function disorders reported after the COVID-19 pandemic. It is worth noting that no similar study has been conducted to date in a group of pilots. The QEEG method we used is currently one of the basic neurological examinations, enabling easy observation of post-COVID-19 changes in the nervous system. With the innovativeness of this technique, our study shows that the use of quantitative electroencephalography can be a precursor in identifying complications associated with cognitive function disorders after COVID-19. Our study was conducted on twelve 26-year-old pilots. All participants had attended the same flight academy and had contracted SARS-CoV-2 infection. The pilots began to suspect COVID-19 infection when they developed typical symptoms such as loss of smell and taste, respiratory problems, and rapid fatigue. Quantitative electroencephalography (QEEG), which is one of the most innovative forms of diagnostics, was used to diagnose the patients. Comparison of the results between the study and control groups showed significantly higher values of all measurements of alpha, theta, and beta2 waves in the study group. In the case of the sensorimotor rhythm (SMR), the measurement results were significantly higher in the control group compared to the study group. Our study, conducted on pilots who had recovered from COVID-19, showed changes in the amplitudes of brain waves associated with relaxation and concentration. The results confirmed the issues reported by pilots as evidenced by the increased amplitudes of alfa, theta, and beta2 waves. It should be emphasized that the modern diagnostic method (QEEG) presented here has significant importance in the medical diagnosis of various symptoms and observation of treatment effects in individuals who have contracted the SARS-CoV-2 virus. The present study demonstrated an innovative approach to the diagnosis of neurological complications after COVID-19.