Gamma Band Research Articles

Does radiofrequency radiation impact sleep? A double-blind, randomised, placebo-controlled, crossover pilot study

The most common source of Radiofrequency Electromagnetic Field (RF-EMF) exposures during sleep includes digital devices, yet there are no studies investigating the impact of multi-night exposure to electromagnetic fields emitted from a baby monitor on sleep under real-world conditions in healthy adults. Given the rise in the number of people reporting to be sensitive to manmade electromagnetic fields, the ubiquitous use of Wi-Fi enabled digital devices and the lack of real-world data, we investigated the effect of 2.45 GHz radiofrequency exposure during sleep on subjective sleep quality, and objective sleep measures, heart rate variability and actigraphy in healthy adults. This pilot study was a 4-week randomised, double-blind, crossover trial of 12 healthy adults. After a one-week run-in period, participants were randomised to exposure from either an active or inactive (sham) baby monitor for 7 nights and then crossed over to the alternate intervention after a one-week washout period. Subjective and objective assessments of sleep included the Pittsburgh Insomnia Rating Scale (PIRS-20), electroencephalography (EEG), actigraphy and heart rate variability (HRV) derived from electrocardiogram. Sleep quality was reduced significantly (p < 0.05) and clinically meaningful during RF-EMF exposure compared to sham-exposure as indicated by the PIRS-20 scores. Furthermore, at higher frequencies (gamma, beta and theta bands), EEG power density significantly increased during the Non-Rapid Eye Movement sleep (p < 0.05). No statistically significant differences in HRV or actigraphy were detected. Our findings suggest that exposure to a 2.45 GHz radiofrequency device (baby monitor) may impact sleep in some people under real-world conditions however further large-scale real-world investigations with specified dosimetry are required to confirm these findings.

Investigating Brain Responses to Transcutaneous Electroacupuncture Stimulation: A Deep Learning Approach

This study investigates the neurophysiological effects of transcutaneous electroacupuncture stimulation (TEAS) on brain activity, using advanced machine learning techniques. This work analyzed the electroencephalograms (EEG) of 48 study participants, in order to analyze the brain’s response to different TEAS frequencies (2.5, 10, 80, and sham at 160 pulses per second (pps)) across 48 participants through pre-stimulation, during-stimulation, and post-stimulation phases. Our approach introduced several novel aspects. EEGNet, a convolutional neural network specifically designed for EEG signal processing, was utilized in this work, achieving over 95% classification accuracy in detecting brain responses to various TEAS frequencies. Additionally, the classification accuracies across the pre-stimulation, during-stimulation, and post-stimulation phases remained consistently high (above 92%), indicating that EEGNet effectively captured the different time-based brain responses across different stimulation phases. Saliency maps were applied to identify the most critical EEG electrodes, potentially reducing the number needed without sacrificing accuracy. A phase-based analysis was conducted to capture time-based brain responses throughout different stimulation phases. The robustness of EEGNet was assessed across demographic and clinical factors, including sex, age, and psychological states. Additionally, the responsiveness of different EEG frequency bands to TEAS was investigated. The results demonstrated that EEGNet excels in classifying EEG signals with high accuracy, underscoring its effectiveness in reliably classifying EEG responses to TEAS and enhancing its applicability in clinical and therapeutic settings. Notably, gamma band activity showed the highest sensitivity to TEAS, suggesting significant effects on higher cognitive functions. Saliency mapping revealed that a subset of electrodes (Fp1, Fp2, Fz, F7, F8, T3, T4) could achieve accurate classification, indicating potential for more efficient EEG setups.

Effects of Continuous Theta Burst Stimulation on Contralateral Primary Motor Cortex: A Concurrent TMS-EEG Study.

Continuous theta burst stimulation (cTBS) is a non-invasive brain stimulation technique. cTBS modulation is an effective treatment for motor dysfunction rehabilitation in post-stroke patients. However, there's currently a lack of research on the effects of cTBS stimulation on the contralesional hemisphere. To better understand the role of cTBS in motor rehabilitation, we investigated the neuroregulatory mechanisms of cTBS in the contralateral cortex using transcranial magnetic stimulation-evoked electroencephalography (TMS-EEG). In this randomized, sham-controlled, single-blind study, 18 healthy subjects received two separate stimulation conditions:cTBS or sham stimulation applied to the left M1. TMS-EEG measurements were taken before and immediately after stimulation. We investigated the TMS-evoked potentials (TEPs), evoked oscillatory responses (EOR), and phase synchronization index(PSI) of TMS-EEG. The effects of cTBS were analyzed using two-way repeated measures analysis of variance (RMANOVA). There was a significant "cTBS condition×time" interaction effect on the theta and gamma bands of EOR, as well as on inter-hemisphere PSI (inter-PSI) and global PSI in both cTBS stimulation conditions. (theta:F=4.526,p=0.041;gamma:F=5.574,p=0.024;inter-PSI:F=5.028,p=0.032;global PSI:F=5.129,p=0.030).After real cTBS modulation, the energy in the theta and gamma frequency bands was significantly higher than before (theta: F=5.747, p=0.022; gamma: F=5.545, p=0.024). The inter-PSI and global PSI significantly increased after real cTBS modulation (inter-PSI: F=6.209, p=0.018; global PSI: F=6.530, p=0.015). cTBS modulation significantly increased EOR and PSI in contralateral brain regions, thereby enhancing cortical excitability and cortical functional connectivity throughout the brain. This provides a theoretical basis for cTBS neuromodulation in stroke patients.

Performance of game sessions in VR vs standard 2D monitor environment. an EEG study

Nowadays studies using Virtual Reality (VR) are gaining high popularity due to VR being a better approximation of the ecological environment for visual experiments than standard 2D display settings. VR technology has been already applied in medicine in the therapy of mental disorders, neurorehabilitation, and neurofeedback. However, its effectiveness compared to the standard 2D procedure is still not fully documented and limited information about the neurophysiological underpinnings of VR is provided. In this study, we tested participants’ performance during several sessions of the computer game in two different environments, VR vs. 2D monitor display. Participants performed three 25 min gaming sessions of adapted Delay Match-To-Sample task during EEG recording. The results showed that the VR group outperformed the 2D display group in the first session and then maintained its performance level throughout the remaining two sessions while the 2D group increased performance in each session eventually leveling up in the last one. Also group differences in the EEG activity were most profound only in the first session. In this session, the VR group was characterized by stronger and more synchronized neuronal activity, especially in delta, theta, and gamma bands. The VR group was less impacted by visual arousals as indicated by the theta/beta2 ratio in parietal electrodes.

Potentiation of Motor Adaptation Via Cerebellar tACS: Characterization of the Stimulation Frequency.

Motor adaptation is critical to update motor tasks in new or modified environmental conditions. While the cerebellum supports error-based adaptations, its neural implementation is partially known. By controlling the frequency of cerebellar transcranial alternating current stimulation (c-tACS), we can test the influence of neural oscillation from the cerebellum for motor adaptation. Two independent experiments were conducted. In Experiment 1, 16 participants received four c-tACS protocols (45Hz, 50Hz, 55Hz, and sham) on four different days while they practiced a visuomotor adaptation task (30 degrees CCW) with variable intensity (within-subject design). In Experiment 2, 45 participants separated into three groups received the effect of 45Hz, 55Hz c-tACS, and sham, respectively (between-subject design), performing the same visuomotor task with a fixed intensity (0.9mA). In Experiment 1, 45Hz and 50Hz of c-tACS accelerated motor adaptation when participants performed the task only for the first time, independent of the time interval between sessions or the stimulation intensity. The effect of active c-tACS was ratified in Experiment 2, where 45Hz c-tACS benefits motor adaptation during the complete practice period. Reaction time, velocity, or duration of reaching are not affected by c-tACS. Cerebellar alternating current stimulation is an effective strategy to potentiate visuomotor adaptations. Frequency-dependent effects on the gamma band, especially for 45Hz c-tACS, ratify the oscillatory profile of cerebellar processes behind the motor adaptation. This can be exploited in future interventions to enhance motor learning.

Near-infrared light stimulation regulates neural oscillation and memory behavior of mice with Alzheimer’s disease

Photobiomodulation (PBM) is a non-invasive neuromodulation technique for the brain. Low-intensity near-infrared light (1–500 mw) has demonstrated the ability to improve memory in Alzheimer’s disease (AD) model mice, suggesting its potential for AD treatment. However, the impact of PBM on neural oscillations in the hippocampal region affected by AD remains unknown. In this study, AD model mice were subjected to PBM for 60 days and then tested using novel object recognition behavior (NOR) experiments. During behavioral experiments, local field potential signals (LFP) of the mice was recorded using a single electrode in the CA1 region to analyze memory ability and neural oscillation characteristics. The results revealed that mice stimulated with PBM exhibited significantly higher new object differentiation indices compared to the Sham group (p < 0.01). Furthermore, PBM stimulation led to a significant increase in relative power and sample entropy of theta and gamma bands (p < 0.01). The coupling intensities of θ-low-γ and θ-high-γ were also significantly higher in the PBM group compared to the Sham group (p < 0.01). In conclusion, these findings suggest that PBM may improve memory ability in AD mice through regulation of neural oscillation characteristics, providing a theoretical basis for utilizing PBM as a treatment modality for Alzheimer’s disease.

Long-term administration of paroxetine increases cortical EEG beta and gamma band activities in healthy awake rats

Understanding the electrophysiological properties of antidepressant medications is important to resolve the response heterogeneity of these drugs in clinical practice. Administration of paroxetine, a selective serotonin reuptake inhibitor, has been shown to increase serotonin levels that affect cortical activities in healthy subjects. However, the extent to which cortical oscillations can be altered by ongoing administration of paroxetine is not known. Here, we develop EEG biomarkers showing long-term effects of paroxetine. EEG changes were analyzed using Neuroscan in healthy wakeful rats administered paroxetine (4 mg/kg/day) for six weeks. Subsequent EEG recordings taken at 3 and 6 weeks after treatment showed differences in cortical oscillations obtained from both hemispheres and frontal-central-parietal regions. Chronic paroxetine administration resulted in an increase in gamma band activity. Comparison of EEG frequency bands of paroxetine and saline groups showed an enhancement in higher frequency activities at third weeks after the treatment. Higher activity of alpha oscillations in the temporal cortex was persistent at sixth week of the administration. Overall, our results suggest that chronic paroxetine administration affects cortical oscillations across an expansive network.

Multimodal investigation of dynamic brain network alterations in autism spectrum disorder: Linking connectivity dynamics to symptoms and developmental trajectories

BackgroundAutism spectrum disorder (ASD) has been associated with disrupted brain connectivity, yet a comprehensive understanding of the dynamic neural underpinnings remains lacking. This study employed concurrent electroencephalography (EEG) and functional near-infrared spectroscopy (fNIRS) techniques to investigate dynamic functional connectivity (dFC) patterns and neurovascular characteristics in children with ASD. We also explored associations between neurovascular characteristics and the developmental trajectory of adaptive behavior in individuals with ASD. MethodsResting-state EEG and fNIRS data were simultaneously recorded from 58 ASD and 63 TD children. We implemented a k-means clustering approach to extract the dFC states for each modality. In addition, a multimodal covariance network (MCN) was constructed from the EEG and fNIRS dFC features to capture the neurovascular characteristics linked to ASD. ResultsEEG analyses revealed atypical properties of dFC states in the beta and gamma bands in children with ASD compared to TD children. For fNIRS, the ASD group exhibited atypical properties of dFC states such as duration and transitions relative to the TD group. The MCN analysis revealed significantly suppressed functional covariance between right superior temporal and left Broca's areas, alongside enhanced right dorsolateral prefrontal-left Broca covariance in ASD. Notably, we found that early neurovascular characteristics can predict the developmental progress of adaptive functioning in ASD. ConclusionThe multimodal investigation revealed distinct dFC patterns and neurovascular characteristics associated with ASD, elucidating potential neural mechanisms underlying core symptoms and their developmental trajectories. Our study highlights that integrating complementary neuroimaging modalities may aid in unraveling the complex neurobiology of ASD.

Thalamo-cortical neural mechanism of sodium salicylate-induced hyperacusis and anxiety-like behaviors

Tinnitus has been identified as a potential contributor to anxiety. Thalamo-cortical pathway plays a crucial role in the transmission of auditory and emotional information, but its casual link to tinnitus-associated anxiety remains unclear. In this study, we explore the neural activities in the thalamus and cortex of the sodium salicylate (NaSal)-treated mice, which exhibit both hyperacusis and anxiety-like behaviors. We find an increase in gamma band oscillations (GBO) in both auditory cortex (AC) and prefrontal cortex (PFC), as well as phase-locking between cortical GBO and thalamic neural activity. These changes are attributable to a suppression of GABAergic neuron activity in thalamic reticular nucleus (TRN), and optogenetic activation of TRN reduces NaSal-induced hyperacusis and anxiety-like behaviors. The elevation of endocannabinoid (eCB)/ cannabinoid receptor 1 (CB1R) transmission in TRN contributes to the NaSal-induced abnormalities. Our results highlight the regulative role of TRN in the auditory and limbic thalamic-cortical pathways.

The human olfactory bulb communicates perceived odor valence to the piriform cortex in the gamma band and receives a refined representation back in the beta band.

A core function of the olfactory system is to determine the valence of odors. In humans, central processing of odor valence perception has been shown to take form already within the olfactory bulb (OB), but the neural mechanisms by which this important information is communicated to, and from, the olfactory cortex (piriform cortex, PC) in humans are not known. To assess communication between the 2 nodes, we simultaneously measured odor-dependent neural activity in the OB and PC from human participants while obtaining trial-by-trial valence ratings. By doing so, we could determine when subjective valence information was communicated, what kind of information was transferred, and how the information was transferred (i.e., in which frequency band). Support vector machine (SVM) learning was used on the coherence spectrum and frequency-resolved Granger causality to identify valence-dependent differences in functional and effective connectivity between the OB and PC. We found that the OB communicates subjective odor valence to the PC in the gamma band shortly after odor onset, while the PC subsequently feeds valence-related information back to the OB in the beta band. Decoding accuracy was better for negative than positive valence, suggesting a focus on negative valence. Critically, we replicated these findings in an independent data set using additional odors across a larger perceived valence range. Combined, these results demonstrate that the OB and PC communicate levels of subjective odor pleasantness across multiple frequencies, at specific time points, in a direction-dependent pattern in accordance with a two-stage model of odor processing.

Olfactory dysfunction decreased local field potential in the reward system and increased EtOH consumption in mice

The relationship between olfactory dysfunction and alcohol intake is unobvious. Chronic alcohol intake results in reduced olfactory acuity and olfactory discrimination and addiction in humans. However, alcohol is a beverage with distinctive odors, which usually works as a cue to induce addictive memories and craving behavior. Whether olfactory impairment increase or decrease alcohol consumption remains an important but unclear issue. In this study, we measured ethanol (EtOH) consumption in the two-bottle choice EtOH drinking test, two bottle choice EtOH/sucrose drinking test and the drinking in the dark (DID) test during the olfactory loss. We also recorded local field potentials (LFPs) from the brain reward system, the ventral tegmental area (VTA), nucleus accumbens (NAc), and piriform cortex (Pir) one and four weeks after the induction of olfactory epithelium lesions using zinc sulfate (ZnSO4) in mice. The results showed that the EtOH consumption and preference were increased during the period of olfactory dysfunction. 1 week after the olfactory injury, LFP powers in the reward system at low- and high-gamma bands decreased significantly, coherence between the Pir and the reward system was also decrease. 4 weeks after the ZnSO4 treatment, LFP powers were reversed, but the coherence between VTA and NAc was decreased, indicating lasting effects post-recovery. This study demonstrates that olfactory dysfunction increased EtOH consumption in mice, which was accompanied by decreased LFP power and coherence in the reward system, which suggest that olfactory deficits changed activities in the reward system and could alter reward-seeking behaviors, which provide insights into the neurobiology of alcohol addiction.

Oscillatory features of German–English sentence processing: Evidence from an EEG study

Aims and objectives: In this study, we investigated the neural oscillatory patterns occurring during sentence-level overt bilingual processing tasks on 46 German–English bilinguals with the intention of finding out if there were task-specific oscillations for this language pair. Methodology: Four tasks, including Language 2 (L2) listening, Language 1 (L1) speaking, L2 shadowing (i.e., repeating a segment of an utterance), and backward interpreting (i.e., orally translating L2 sentences to L1), were implemented with the technique of electroencephalography (EEG). Data and analysis: Data included demographic indices, neural oscillation metrics, and oral production scores from participants. Static spectral analysis, time–frequency analysis, principal components analysis, cross-frequency coupling, and inferential statistics were applied to the data. Findings: The sensory task and articulatory tasks differed greatly in the delta–theta band and the beta–gamma band in terms of time–frequency dynamics. Analyses also revealed variations in a frontal/prefrontal theta component and a distinct gamma component across the four tasks. Moreover, we found a right fronto-temporal theta–gamma coupling unique to backward interpreting. Originality: The task-wise comparison experiment paradigm has rarely been implemented in neuroimaging research on language, especially so in EEG research on bilingualism. Significance: The findings provide novel insights for the understanding of language conversion mechanism and may benefit the training of professional interpreters.

Olfactory bulb stimulation mitigates Alzheimer's-like disease progression.

Deep brain stimulation (DBS) has demonstrated potential in mitigating Alzheimer's disease (AD). However, the invasive nature of DBS presents challenges for its application. The olfactory bulb (OB), showing early AD-related changes and extensive connections with memory regions, offers an attractive entry point for intervention, potentially restoring normal activity in deteriorating memory circuits. Our study examined the impact of electrically stimulating the OB on working memory as well as pathological and electrophysiological alterations in the OB, medial prefrontal cortex, hippocampus, and entorhinal cortex in amyloid beta (Aβ) AD model rats. Male Wistar rats underwent surgery for electrode implantation in brain regions, inducing Alzheimer's-like disease. Bilateral olfactory bulb (OB) electrical stimulation was performed for 1 hour daily to the OB of stimulation group animals for 18 consecutive days, followed by the evaluations of histological, behavioral, and local field potential signal processing. OB stimulation counteracted Aβ plaque accumulation and prevented AD-induced working memory impairments. Furthermore, it prompted an increase in power across diverse frequency bands and enhanced functional connectivity, particularly in the gamma band, within the investigated regions during a working memory task. This preclinical investigation highlights the potential of olfactory pathway-based brain stimulation to modulate the activity of deep-seated memory networks for AD treatment. Importantly, the accessibility of this pathway via the nasal cavity lays the groundwork for the development of minimally invasive approaches targeting the olfactory pathway for brain modulation.

Change in EEG-EMG synchronization reflecting abnormal functional corticomuscular coupling following stroke: A pilot study

Stroke is a common clinical cardiovascular and cerebrovascular disease, which can cause severe motor dysfunction. Therefore, it is an important topic to investigate the abnormality mechanism of the cerebral cortex and the corresponding muscles after stroke. In this study, we investigated the functional corticomuscular coupling (FCMC) at specific frequencies by analyzing differences between stroke patients and healthy controls in hand movements. The transfer spectral entropy (TSE) method was used to analyze simultaneous electroencephalography (EEG) and electromyography (EMG) in the right-hand steady state force task. The results illustrated that healthy subjects had the highest TSE values at the beta band in the EMG→EEG and EEG→EMG directions, and the TSE value in the EEG→EMG direction was higher than that in the EMG→EEG direction. In contrast, for stroke patients, beta band coupling was weakened, and there was a notably higher enhancement of alpha and gamma bands in the EMG→EEG direction relative to the EEG→EMG direction. Further analysis found significant correlations between TSE area values at beta2 and gamma2 bands and clinical rating scales. This study demonstrates the frequency specificity properties of FCMC estimated by TSE can assess the rehabilitation status of stroke patients and contribute to our comprehension of the potential mechanism of motor control systems.

Decoding Depth of Meditation: EEG Insights from Expert Vipassana Practitioners

BackgroundMeditation practices have demonstrated numerous psychological and physiological benefits, yet capturing the neural correlates of varying meditative depths remains challenging. This study aimed to decode self-reported time-varying meditative depth in expert practitioners using EEG. MethodsExpert Vipassana meditators (n=34) participated in two separate sessions. Participants reported their meditative depth on a personally defined 1-5 scale using both traditional probing and a novel "spontaneous emergence" method. EEG activity and effective connectivity in theta, alpha, and gamma bands was used to predict meditative depth using machine/deep learning, including a novel method that fused source activity and connectivity information. ResultsWe achieved significant accuracy in decoding self-reported meditative depth across unseen sessions. The "spontaneous emergence" method yielded improved decoding performance to traditional probing and correlated more strongly with post-session outcome measures. Best performance was achieved by a novel machine learning method which fused spatial, spectral, and connectivity information. Conventional EEG channel-level methods and pre-selected default mode network regions fell short in capturing the complex neural dynamics associated with varying meditation depths. ConclusionsThis study demonstrates the feasibility of decoding personally defined meditative depth using EEG. The findings highlight the complex, multivariate nature of neural activity during meditation and introduce "spontaneous emergence" as an ecologically valid and less obtrusive experiential sampling method. These results have implications for advancing neurofeedback techniques and enhancing our understanding of meditative practices.

EEG Oscillations in Recent Suicide Attempters: Assessing Responses to Positive and Negative Future Imagination Tasks

Background: Our goal is to uncover Electroencephalography (EEG) power spectrum patterns during imagination tasks in individuals who attempted suicide within the past 1-4 weeks, addressing gaps in understanding the neural correlates of future imagination in suicidal behavior. Methods: This case-control study comprised a total of 60 participants, consisting of 47 females and 13 males. The sample comprised three groups: Attempted Suicide + Major Depressive Disorder (SA+MDD), Major Depressive Disorder (MDD), and Healthy Controls (HC). To assess participants' future imagination, a unique approach called the Positive and Negative Future Imagination Task was designed, which involved scenario visualization concurrent with EEG recording. Statistical analyses included ANOVA with post-hoc pairwise comparisons to compare EEG power spectrum between three groups. Results: It was observed that the SA+MDD group experienced significantly increased theta frequency band in the right frontocentral regions when compared to the MDD group during both positive and negative imagination tasks (P-value< 0.05). Furthermore, increased gamma activity was observed in the SA+MDD group compared to the HC group, predominantly in the right frontocentral areas during both imagination tasks. Conclusion: The results of this study indicate that individuals who have recently attempted suicide exhibit heightened neural activity in the frontocentral regions of the right hemisphere of the brain, specifically in theta and gamma band frequencies, when contemplating both positive and negative aspects of the future. These findings, in the context of behavioral tasks may indicate a decrease in the ability to envision a positive future and an increase in visualizing a negative future.

Analysis of brain network differences in the active, motor imagery, and passive stoke rehabilitation paradigms based on the task-state EEG

Different movement paradigms have varying effects on stroke rehabilitation, and their mechanisms of action on the brain are not fully understood. This study aims to investigate disparities in brain network and functional connectivity of three movement paradigms (active, motor imagery, passive) on stroke recovery. EEG signals were recorded from 11 S patients (SP) and 13 healthy controls (HC) during fist clenching and opening tasks under the three paradigms. Brain networks were constructed to analyze alterations in brain network connectivity, node strength (NS), clustering coefficients (CC), characteristic path length (CPL), and small-world index(S). Our findings revealed increased activity in the contralateral motor area in SP and higher activity in the ipsilateral motor area in HC. In the beta band, SP exhibited significantly higher CC in motor imagery (MI) than in active and passive tasks. Furthermore, the small world index of SP during MI tasks in the beta band was significantly smaller than in the active and passive tasks. NS in the gamma band for SP during the MI paradigm was significantly higher than in the active and passive paradigms. These findings suggest reorganization within both ipsilateral and contralateral motor areas of stroke patients during MI tasks, providing evidence for neural restructuring. Collectively, these findings contribute to a deeper understanding of task-state brain network changes and the rehabilitative mechanism of MI on motor function.

Numerical Simulation of Spectral Radiation for Hypersonic Vehicles

The spectral radiation of hypersonic vehicles and surrounding flow fields is crucial for optical target detection. A novel approach combines Low-Discrepancy Sequences with the Reverse Monte Carlo Method to simulate the spectral radiation of hypersonic missiles. The effects of chemical non-equilibrium reactions and high-emissivity coating failures on the spectral radiation of a typical biconical hypersonic missile were investigated. Significant differences were found among chemical equilibrium, non-equilibrium, non-reactive, and catalytic wall models. High-emissivity coating failures occur mainly in the high-temperature regions of the nose cone and fins. The non-equilibrium model shows a transition peak distribution of NO in the head shock layer within the ultraviolet gamma band, with integrated ultraviolet radiation (200–300 nm) three times higher than the equilibrium model. In the 1–3 μm band, the non-equilibrium model’s radiation intensity at a 120° horizontal detection angle is about 1.46 times that of the equilibrium model. Using real coating emissivity, the 3–5 μm band radiation intensity is about 5% higher, and the 8–14 μm band is about 8.51% lower than the uniform emissivity model. When high-emissivity coating emissivity fails by 40%, the 3–5 μm band intensity decreases by about 15.38%, and the 8–14 μm band intensity decreases by about 12.67%.

Depression detection based on the temporal-spatial-frequency feature fusion of EEG

Depression is a prevalent affective psychiatric disorder projected to be the leading contributor to the world’s disease burden by 2030. Due to its high prevalence and low recognition rate, an objective and effective detection method is urgently needed. Deep learning methods based on electroencephalography (EEG) have shown significant potential in depression detection. However, excessive channels can increase redundancy and computational complexity in EEG, while irrelevant channels may reduce accuracy. Additionally, existing models often overlook the complementarity between the temporal-, spatial-, and frequency-domain features of EEG, limiting their detection capabilities. To address these issues, we propose a method that fuses the temporal, spatial, and frequency domain features of EEG to enhance the detection accuracy while eliminating redundant channels. We introduce an EEG channel selection method based on frequency domain weighting that automatically adjusts the channel weights to select the EEG channels that best capture spatial information across the delta, theta, alpha, beta, and gamma bands, thereby optimizing the extraction of spatial-frequency features. In addition, we designed a multiscale spatiotemporal convolutional attention network to extract the spatiotemporal features of EEG. In this network, the multiscale convolutional attention module enhanced the model’s ability to capture spatial features, whereas the temporal trend-aware self-attention module extracted long-term temporal features by analyzing global correlations across different time points. Experimental results on the MODMA dataset show that our method achieved a 97.24% detection accuracy, surpassing current state-of-the-art models. This study offers a novel approach for constructing depression detection models, providing a foundation for future research and application.

Cognitive State Classification Using Convolutional Neural Networks on Gamma-Band EEG Signals

This study introduces a novel methodology for classifying cognitive states using convolutional neural networks (CNNs) on electroencephalography (EEG) data of 41 students, aimed at streamlining the traditionally labor-intensive analysis procedures utilized in EEGLAB. Concentrating on the 30–40 Hz frequency range within the gamma band, we developed a CNN model to analyze EEG signals recorded from the inferior parietal lobule during various cognitive tasks. The model demonstrated substantial efficacy, achieving an accuracy of 91.42%, precision of 71.41%, and recall of 72.51%, effectively distinguishing between high and low gamma activity states. This performance surpasses traditional machine learning methods for EEG analysis, such as support vector machines and random forests, which typically achieve accuracies between 70–85% for similar tasks. Our approach offers significant time savings over manual EEGLAB methods. The integration of event-related spectral perturbation (ERSP) analysis with a novel CNN architecture enables capture of both fine-grained and broad spectral EEG features, advancing the field of computational neuroscience. This research has implications for brain-computer interfaces, clinical diagnostics, and cognitive monitoring, offering a more efficient and accurate alternative to current EEG analysis methods.