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Depressive states in both healthy individuals and those with major depressive disorder exhibit differences primarily in symptom severity rather than symptom type, suggesting that there is a spectrum of depressive symptoms. The increasing prevalence of mild depression carries lifelong implications, emphasizing its clinical and social significance, which parallels that of moderate depression. Early intervention and psychotherapy have shown effective outcomes in subthreshold depression. Electroencephalography serves as a non-invasive, powerful tool in depression research, with many studies employing it to discover biomarkers and explore underlying mechanisms for the identification and diagnosis of depression. However, the efficacy of these biomarkers in distinguishing various depressive states in healthy individuals and in understanding the associated mechanisms remains uncertain. In our study, we examined the power spectrum density and the region-based phase-locking value in healthy individuals with various depressive states during their resting state. We found significant differences in neural activity, even among healthy individuals. Participants were categorized into high, middle, and low depressive state groups based on their response to a questionnaire, and eyes-open resting-state electroencephalography was conducted. We observed significant differences among the different depressive state groups in theta- and beta-band power, as well as correlations in the theta–beta ratio in the frontal lobe and phase-locking connections in the frontal, parietal, and temporal lobes. Standardized low-resolution electromagnetic tomography analysis for source localization comparing the differences in resting-state networks among the three depressive state groups showed significant differences in the frontal and temporal lobes. We anticipate that our study will contribute to the development of effective biomarkers for the early detection and prevention of depression.

Background: Affective computing has gained increasing attention in the area of the human-computer interface where electroencephalography (EEG)-based emotion recognition occupies an important position. Nevertheless, the diversity of emotions and the complexity of EEG signals result in unexplored relationships between emotion and multichannel EEG signal frequency, as well as spatial and temporal information. Methods: Audio-video stimulus materials were used that elicited four types of emotions (sad, fearful, happy, neutral) in 32 male and female subjects (age 21–42 years) while collecting EEG signals. We developed a multidimensional analysis framework using a fusion of phase-locking value (PLV), microstates, and power spectral densities (PSDs) of EEG features to improve emotion recognition. Results: An increasing trend of PSDs was observed as emotional valence increased, and connections in the prefrontal, temporal, and occipital lobes in high-frequency bands showed more differentiation between emotions. Transition probability between microstates was likely related to emotional valence. The average cross-subject classification accuracy of features fused by Discriminant Correlation Analysis achieved 64.69%, higher than that of single mode and direct-concatenated features, with an increase of more than 7%. Conclusions: Different types of EEG features have complementary properties in emotion recognition, and combining EEG data from three types of features in a correlated way, improves the performance of emotion classification.

Biometric identification uses person recognition techniques based on the extraction of some of their physical or biological properties, which make it possible to characterize and differentiate one person from another and provide irreplaceable and critical information that is suitable for application in security systems. The extraction of information from the electrical biosignal of the human brain has received a great deal of attention in recent years. Analysis of EEG signals has been widely used over the last century in medicine and as a basis for brain–machine interfaces (BMIs). In addition, the application of EEG signals for biometric recognition has recently been demonstrated. In this context, EEG-based biometric systems are often considered in two different applications: identification (one-to-many classification) and authentication (one-to-one or true/false classification). In this article, we establish a methodology for selecting and reducing the minimum number of EEG sensors necessary to carry out effective biometric identification of individuals. Two methodologies were applied, one based on principal component analysis and the other on the Wilcoxon signed-rank test in order to reduce the number of electrodes. This allowed us to identify, according to the methodology used, the areas of the cerebral cortex that would allow selection of the minimum number of electrodes necessary for the identification of individuals. The methodologies were applied to two databases, one with 13 people with self-collected recordings using low-cost EEG equipment, EMOTIV EPOC+, and another publicly available database with recordings from 109 people provided by the PhysioNet BCI.

Arithmetic requires complex and fast processes orchestrated within a large-scale network spanning multiple brain regions. However, reports on the network’s temporal dynamics are scarce. Here, we present data from intracranial EEG (iEEG) of 20 subjects (epilepsy surgery candidates) performing a sequential three-operand arithmetic task. Utilizing the high temporal and spatial resolution of iEEG, we analysed changes in high-gamma band (HGB; 52–120 Hz) activity and functional connectivity assessed by phase-locking value (PLV) in the delta (0.1–3 Hz) and theta (3–7 Hz) frequency bands. Strong and transient HGB activations peaked first in the ventral occipito-temporal cortex, followed by a more gradual increase in the lateral parietal, sensorimotor, and frontal cortices, accompanied by deactivations in default mode network areas. The connectivity patterns were more extensive during calculation than number recognition, with the theta PLV peaking ~ 150 ms earlier than the delta PLV. Earliest connectivity appeared, surprisingly, between ventral temporal and frontal regions at ~ 100–200 ms, evolving into a robust pattern among key network nodes at ~ 200–400 ms after the presentation of each operand. The presented results elucidate information flow within the putative arithmetic network during calculation in the human brain, offering high-temporal-resolution insights into its functional architecture.

The principal goal of the brain-computer interface (BCI) is to translate brain signals into meaningful commands to control external devices or neuroprostheses to restore lost functions of patients with severe motor disabilities. The invasive recording of brain signals involves numerous health issues. Therefore, BCIs based on non-invasive recording modalities such as electroencephalography (EEG) are safer and more comfortable for the patients. The BCI requires reconstructing continuous movement parameters such as position or velocity for practical application of neuroprostheses. The BCI studies in continuous decoding have extensively relied on extracting features from the amplitude of brain signals, whereas the brain connectivity features have rarely been explored. This study aims to investigate the feasibility of using phase-based connectivity features in decoding continuous hand movements from EEG signals. To this end, the EEG data were collected from seven healthy subjects performing a 2D center-out hand movement task in four orthogonal directions. The phase-locking value (PLV) and magnitude-squared coherence (MSC) are exploited as connectivity features along with multiple linear regression (MLR) for decoding hand positions. A brute-force search approach is employed to find the best channel pairs for extracting features related to hand movements. The results reveal that the regression models based on PLV and MSC features achieve the average Pearson correlations of 0.43±0.03 and 0.42±0.06, respectively, between predicted and actual trajectories over all subjects. The delta and alpha band features have the most contribution in regression analysis. The results also demonstrate that both PLV and MSC decoding models lead to superior results on our data compared to two recently proposed feature extraction methods solely based on the amplitude or phase of recording signals (p<0.05). This study verifies the ability of PLV and MSC features in the continuous decoding of hand movements with linear regression. Thus, our findings suggest that extracting features based on brain connectivity can improve the accuracy of trajectory decoder BCIs.

The Speech-to-Speech Synchronization task is a well-established behavioral approach to assess individual differences in auditory-motor synchronization. In this task, participants listen to a series of syllables that progressively increase in frequency, while simultaneously whispering the syllable /ta/ to synchronize with the rhythm of the incoming syllables. In our study, we replicated the bimodal distribution of high- and low-synchronizers in a sample of native German speakers. We present a refined analysis pipeline based on existing analysis scripts, address minor task-related issues and observations, and incorporate new analysis features such as the removal of silent gaps. Crucially, our analysis revealed that (sub-)harmonic interactions can emerge during various stages of synchronization and its assessment, obscured by the synchronization measurement. Subharmonic synchronizers were found to produce the /ta/-syllables to only every second or third incoming syllable which can result in deceptively high Phase Locking Values, thus challenging the conceptualization of low- and high-synchronizers. Our data analysis is available at OSF .

Introduction: The present study explores brain connectivity in Parkinson’s disease (PD) and in age matched healthy controls (HC), using quantitative EEG analysis, at rest and during a motor tasks. We also evaluated the diagnostic performance of the phase locking value (PLV), a measure of functional connectivity, in differentiating PD patients from HCs. Methods: High-density, 64-channels, EEG data from 26 PD patients and 13 HC were analyzed. EEG signals were recorded at rest and during a motor task. Phase locking value (PLV), as a measure of functional connectivity, was evaluated for each group in a resting state and during a motor task for the following frequency bands: (i) delta: 2–4 Hz; (ii) theta: 5–7 Hz; (iii) alpha: 8–12 Hz; beta: 13–29 Hz; and gamma: 30–60 Hz. The diagnostic performance in PD vs. HC discrimination was evaluated. Results: Results showed no significant differences in PLV connectivity between the two groups during the resting state, but a higher PLV connectivity in the delta band during the motor task, in HC compared to PD. Comparing the resting state versus the motor task for each group, only HCs showed a higher PLV connectivity in the delta band during motor task. A ROC curve analysis for HC vs. PD discrimination, showed an area under the ROC curve (AUC) of 0.75, a sensitivity of 100%, and a negative predictive value (NPV) of 100%. Conclusions: The present study evaluated the brain connectivity through quantitative EEG analysis in Parkinson’s disease versus healthy controls, showing a higher PLV connectivity in the delta band during the motor task, in HC compared to PD. This neurophysiology biomarkers showed the potentiality to be explored in future studies as a potential screening biomarker for PD patients.

This pilot EEG study examined the feasibility of a soundscape-based 40 Hz auditory stimulation format by using a soundscape-only condition as a contrast control. We tested whether a nature-based soundscape with an additively layered pure 40 Hz sine component (40 Hz ON; not amplitude-modulated) yields a more pronounced narrowband response centered at 40 Hz than the same soundscape without the 40 Hz layer (40 Hz OFF). Participants completed both conditions in a single-blind, randomized-order, within-participant crossover session with a washout interval. EEG outcomes included 40 Hz power, frequency-domain SNR around 40 Hz, scalp distribution of 40 Hz power, and phase-based connectivity in the gamma range. This study evaluates EEG-level detectability of 40 Hz–centered neural signatures and does not assess cognitive/clinical efficacy or therapeutic benefit. Across metrics, the 40 Hz ON soundscape showed a consistent ON > OFF directionality, including localized electrode-level signals and a temporal-region summary measure under nominal, uncorrected testing, accompanied by a clearer narrowband feature near 40 Hz in spectral profiles. Overall, the observed trends are consistent with the feasibility of embedding an additive 40 Hz layer into a naturalistic soundscape in a manner that yields EEG-quantifiable, 40 Hz centered signatures; however, because this is an exploratory pilot without multiplicity control, all effects should be interpreted as hypothesis-generating and warrant confirmation in larger, preregistered studies with multiplicity-aware inference.

Freezing of gait (FOG) is a complex gait disturbance in Parkinson’s disease (PD), during which the patient is not able to effectively initiate gait or continue walking. The mystery of the FOG phenomenon is still unsolved. Recent studies have revealed abnormalities in cortical activities associated with FOG, which highlights the importance of cortical and cortical-subcortical network dysfunction in PD patients with FOG. In this paper, phase-locking value (PLV) of eight frequency sub-bands between 0.05-35 Hz over frontal, motor, and parietal areas (during an ankle dorsiflexion task) is used to investigate EEG phase synchronization. PLV was investigated over both superficial and deeper networks by analyzing EEG signals preprocessed with and without Surface Laplacian (SL) spatial filter. Four groups of participants were included: PD patients with severe FOG (N = 5, 5 males), PD patients with mild FOG (N = 7, 6 males), PD patients without FOG (N = 14, 13 males), and healthy age-matched controls (N = 13, 10 males). Fifteen trials were recorded from each participant. At superficial layers, frontoparietal theta phase synchrony was a unique feature present in PD with FOG groups. At deeper networks, significant dominance of interhemispheric frontoparietal alpha phase synchrony in PD with FOG, in contrast to beta phase synchrony in PD without FOG, was identified. Alpha phase synchrony was more distributed in PD with severe FOG, with higher levels of frontoparietal alpha phase synchrony. In addition to FOG-related abnormalities in PLV analysis, phase-amplitude coupling (PAC) analysis was also performed on frequency bands with PLV abnormalities. PAC analysis revealed abnormal coupling between theta and low beta frequency bands in PD with severe FOG at the superficial layers over frontal areas. At deeper networks, theta and alpha frequency bands show high PAC over parietal areas in PD with severe FOG. Alpha and low beta also presented PAC over frontal areas in PD groups with FOG. The results introduced significant phase synchrony differences between PD with and without FOG and provided important insight into a possible unified underlying mechanism for FOG. These results thus suggest that PLV and PAC can potentially be used as EEG-based biomarkers for FOG.

Objective: This study aimed to investigate the effects of transcranial direct current stimulation (tDCS) on brain functional networks in children with autism spectrum disorder (ASD). Methods: We constructed brain functional networks using phase-locking value (PLV) and assessed the temporal variability of these networks using fuzzy entropy. Graph theory was applied to analyze network characteristics. Resting-state electroencephalography (EEG) data were used to compare differences in brain functional connectivity, temporal variability, and network properties between children with ASD and typically developing (TD) children. Additionally, we examined the changes in functional connectivity, temporal variability, and network properties in children with ASD after 20 sessions of tDCS intervention. Results: The study revealed that children with ASD exhibited lower connectivity in the alpha band and higher connectivity in the beta band. In the delta and theta bands, ASD children demonstrated a mixed pattern of both higher and lower connectivity. Furthermore, ASD children exhibited higher temporal variability across all four frequency bands, particularly in the delta and beta bands. After tDCS intervention, the total score of the Autism Behavior Checklist (ABC) significantly decreased. Additionally, functional connectivity in the delta and alpha bands increased, while temporal variability in the delta and beta bands decreased, indicating positive changes in brain network characteristics. Conclusion: These results suggest that tDCS may be a promising intervention for modulating brain functional networks in children with ASD. Clinical Trial Registration: ChiCTR2400092790. Registered 22 November, 2024, https://www.chictr.org.cn/showproj.html?proj=249950.