Depressed EEG Research Articles

Automated detection of depression using wavelet scattering networks

Today, depression is a common problem that affects many people all over the world. It can impact a person's mood and quality of life unless identified and treated immediately. Due to the hectic and stressful modern life seems to be, depression has become a leading cause of mental health illnesses. Signals from electroencephalograms (EEG) are frequently used to detect depression. It is difficult, time-consuming, and highly skilled to manually detect depression using EEG data analysis. Hence, in the proposed study, an automated depression detection system using EEG signals is proposed.The proposed study uses a clinically available dataset and dataset provided by the Department of Psychiatry at the Government Medical College (GMC) in Kozhikode, Kerala, India which consisted of 15 depressed patients and 15 healthy subjects and a publically available Multi-modal Open Dataset (MODMA) for Mental-disorder Analysis available at UK Data service reshare that consisted of 24 depressed patients and 29 healthy subjects. In this study, we have developed a novel Deep Wavelet Scattering Network (DWSN) for the automated detection of depression EEG signals. The best-performing classifier is then chosen by feeding the features into several machine-learning algorithms. For the clinically available GMC dataset, Medium Neural Network (MNN) achieved the highest accuracy of 99.95% with a Kappa value of 0.999. Using the suggested methods, the precision, recall, and F1-score are all 1. For the MODMA dataset, Wide Neural Network (WNN) achieved the highest accuracy of 99.3% with a Kappa value of 0.987. Using the suggested methods, the precision, recall, and F1-score are all 0.99. In comparison to all current methodologies, the performance of the suggested research is superior. The proposed method can be used to automatically diagnose depression both at home and in clinical settings.

Protocol for a Pilot Study on the Neurocardiac Mechanism of an Interoceptive Compassion-Based Heart-Smile Training for Depression.

Heart-Smile Training (HST) is an interoceptive compassion-based behavioral intervention that in case reports has been beneficial for depression. Interoception refers to the awareness and regulation of physiological signals from inside the body. Depressed patients often have diminished interoceptive awareness and often experience disconnection from bodily needs and sensations. In addition to interoceptive dysfunction, depression often involves negative self-evaluation and self-critical rumination. HST is a compassion-based meditation training program that explicitly cultivates interoceptive awareness of the heart area. This study aims to investigate the possible neurocardiac mechanisms engaged through HST for depression patients. We plan to enroll 50 subjects to be randomized into a 4-week HST intervention group and a waitlist group. A battery of psychological questionnaires will be administered at baseline and post-intervention timepoints, and electroencephalography (EEG) will be collected during compassion meditation guided by pre-recorded audio. The primary clinical outcome measures are on the feasibility of the intervention and research procedures, the primary mechanistic outcome measure is the post-intervention change in Heartbeat Evoked Potential (HEP) amplitude. Secondary outcome measures include changes in depression severity and EEG gamma spectral activity. Exploratory outcome measures include effects of HST on skin conductance response, heart rate variability, EEG spectral properties in other frequency bands, as well as a list of psychological questionnaires that measure depression and anxiety symptoms, emotion regulation, mindfulness, interoceptive awareness, self-compassion, gratitude, sleep quality, quality of life and social connectedness. Results not yet available. This is the first study on the feasibility and interoceptive neurocardiac mechanism of HST. Our findings will provide frontier knowledge on the physiological working mechanism of behavioral interventions with an interoception-based meditative approach. https://clinicaltrials.gov/study/NCT05564533.

Temporal stability and correlation of EEG markers and depression questionnaires scores in healthy people

Mental disorders, especially depression, have become a rising problem in modern society. The development of methods and markers for the early detection of mental disorders is an actual problem. Psychological questionnaires are the only tools for evaluating the symptoms of mental disorders in clinical practice today. The electroencephalography (EEG) based non-invasive and cost-effective method seems feasible for the early detection of depression in occupational and family medicine centers and personal monitoring. The reliability of the EEG markers in the early detection of depression assumes their high temporal stability and correlation with the scores of depression questionnaires. The study was been performed on 17 healthy people over three years. Two hypotheses have been evaluated in the current study: first, the temporal stability of EEG markers is close to the stability of the scores of depression questionnaires, and second, EEG markers and depression questionnaires’ scores are not correlated in healthy people. The results of the performed study support both hypotheses: the temporal stability of EEG markers is high and close to the stability of depression questionnaires scores and the correlation between the EEG markers and depression questionnaires scores is not detected in healthy people. The results of the current study contribute to the interpretation of results in depression EEG studies and to the feasibility of EEG markers in the detection of depression.

Application of graph frequency attention convolutional neural networks in depression treatment response.

Depression, a prevalent global mental health disorder, necessitates precise treatment response prediction for the improvement of personalized care and patient prognosis. The Graph Convolutional Neural Networks (GCNs) have emerged as a promising technique for handling intricate signals and classification tasks owing to their end-to-end neural architecture and nonlinear processing capabilities. In this context, this article proposes a model named the Graph Frequency Attention Convolutional Neural Network (GFACNN). Primarily, the model transforms the EEG signals into graphs to depict the connections between electrodes and brain regions, while integrating a frequency attention module to accentuate brain rhythm information. The proposed approach delves into the application of graph neural networks in the classification of EEG data, aiming to evaluate the response to antidepressant treatment and discern between treatment-resistant and treatment-responsive cases. Experimental results obtained from an EEG dataset at Peking University People's Hospital demonstrate the notable performance of GFACNN in distinguishing treatment responses among depression patients, surpassing deep learning methodologies including CapsuleNet and GoogLeNet. This highlights the efficacy of graph neural networks in leveraging the connections within EEG signal data. Overall, GFACNN exhibits potential for the classification of depression EEG signals, thereby potentially aiding clinical diagnosis and treatment.

"Relationship between Depression and Recurrent EEG Parameters"

"Relationship between Depression and Recurrent EEG Parameters"

Identification of normal and depression EEG signals in variational mode decomposition domain.

Early detection of depression is critical in assisting patients in receiving the best therapy possible to avoid negative repercussions. Depression detection using electroencephalogram (EEG) signals is a simple, low-cost, convenient, and accurate approach. This paper proposes a six-stage novel method for detecting depression using EEG signals. First, EEG signals are recorded from 44 subjects, with 22 subjects being normal and 22 subjects being depressed. Second, a simple notch filter with EEG signals differencing approach is employed for effective preprocessing. Third, the variational mode decomposition (VMD) approach is implemented for nonlinear and non-stationary EEG signals analysis, resulting in many modes. Fourth, mutual information-based novel modes selection criterion is proposed to select the most informative modes. In the fifth step, a combination of linear and nonlinear features are extracted from selected modes and at last, classification is performed with neural networks. In this study, a novel single feature is also proposed, which is made using Log energy, norm entropies and fluctuation index, which delivers 100% classification accuracy, sensitivity and specificity. By using these features, a novel depression diagnostic index is also proposed. This integrated index would assist in quicker and more objective identification of normal and depression EEG signals. The proposed computerized framework and the DDI can help health workers, large enterprises, and product developers build a real-time system.

Further Evidence that Inhibition of Neuronal Voltage-Gated Calcium Channels Contributes to the Hypnotic Effect of Neurosteroid Analogue, 3β-OH.

We recently reported that a neurosteroid analogue with T-channel-blocking properties (3β,5β,17β)-3-hydroxyandrostane-17-carbonitrile (3β-OH), induced hypnosis in rat pups without triggering neuronal apoptosis. Furthermore, we found that the inhibition of the CaV3.1 isoform of T-channels contributes to the hypnotic properties of 3β-OH in adult mice. However, the specific mechanisms underlying the role of other subtypes of voltage-gated calcium channels in thalamocortical excitability and oscillations in vivo during 3β-OH-induced hypnosis are largely unknown. Here, we used patch-clamp recordings from acute brain slices, in vivo electroencephalogram (EEG) recordings, and mouse genetics with wild-type (WT) and CaV2.3 knock-out (KO) mice to further investigate the molecular mechanisms of neurosteroid-induced hypnosis. Our voltage-clamp recordings showed that 3β-OH inhibited recombinant CaV2.3 currents. In subsequent current-clamp recordings in thalamic slices ex vivo, we found that selective CaV2.3 channel blocker (SNX-482) inhibited stimulated tonic firing and increased the threshold for rebound burst firing in WT animals. Additionally, in thalamic slices we found that 3β-OH inhibited spike-firing more profoundly in WT than in mutant mice. Furthermore, 3β-OH reduced bursting frequencies in WT but not mutant animals. In ensuing in vivo experiments, we found that intra-peritoneal injections of 3β-OH were less effective in inducing LORR in the mutant mice than in the WT mice, with expected sex differences. Furthermore, the reduction in total α, β, and low γ EEG power was more profound in WT than in CaV2.3 KO females over time, while at 60 min after injections of 3β-OH, the increase in relative β power was higher in mutant females. In addition, 3β-OH depressed EEG power more strongly in the male WT than in the mutant mice and significantly increased the relative δ power oscillations in WT male mice in comparison to the mutant male animals. Our results demonstrate for the first time the importance of the CaV2.3 subtype of voltage-gated calcium channels in thalamocortical excitability and the oscillations that underlie neurosteroid-induced hypnosis.

A pilot study of machine learning of resting-state EEG and depression in Parkinson’s disease

IntroductionDepression is a non-motor symptom of Parkinson’s disease (PD). PD-related depression is difficult to diagnose, and the neurophysiological basis is poorly understood. Depression can markedly affect cortical function, which suggests that scalp electroencephalography (EEG) may be able to distinguish depression in PD. We conducted a pilot study of depression and resting-state EEG in PD. MethodsWe recruited 18 PD patients without depression, 18 PD patients with depression, and 12 demographically similar non-PD patients with clinical depression. All patients were on their usual medications. We collected resting-state EEG in all patients and compared cortical brain signal features between patients with and without depression. We used a machine learning algorithm that harnesses the entire power spectrum (linear predictive coding of EEG Algorithm for PD: LEAPD) to distinguish between groups. ResultsWe found differences between PD patients with and without depression in the alpha band (8–13 Hz) globally and in the beta (13–30 Hz) and gamma (30–50 Hz) bands in the central electrodes. From two minutes of resting-state EEG, we found that LEAPD-based machine learning could robustly distinguish between PD patients with and without depression with 97 % accuracy and between PD patients with depression and non-PD patients with depression with 100 % accuracy. We verified the robustness of our finding by confirming that the classification accuracy gracefully declines as data are randomly truncated. ConclusionsOur results suggest that resting-state EEG power spectral analysis has the potential to distinguish depression in PD accurately. We demonstrated the efficacy of the LEAPD algorithm in identifying PD patients with depression from PD patients without depression and controls with depression. Our data provide insight into cortical mechanisms of depression and could lead to novel neurophysiological markers for non-motor symptoms of PD.

Frontal EEG Asymmetry in Major Depression, Dysthymia and Bipolar Disorder

Depression is a mental disorder that continues to make life difficult or impossible for a depressed person and, if left untreated, can lead to dangerous activities such as self-harm and suicide. Nowadays, Electroencephalogram (EEG) has become an important diagnostic tool for many brain disorders. In this article, a new method for the detection of depression based on the reconstructed phase space (RPS) of EEG signals and geometrical features has been proposed. The RPS of the EEG signals of 22 normal and 22 depressed subjects are plotted in two-dimensional space and, based on their shape, 34 geometrical features are extracted. The p-values for the proposed features were significantly lower (p-value ≈ 0) indicating the capacity of the proposed geometric features for the normal and depression EEG signals classification tasks. For the purpose of reducing feature vector arrays, the performance of four optimization algorithms is checked, namely: ant colony optimization (ACO), grey wolf optimization (GWO), genetic algorithm (GA) and particle swarm optimization (PSO), in which GA with the ability of 58.8% was better than the other optimization algorithms for decreasing the feature vector arrays. Selected features are fed to the support vector machine (SVM) classifier with radial basis function (RBF) kernel and K-nearest neighbors (KNN) classifier with Euclidean and city block distances in 10-fold cross-validation (CV) strategy. The proposed framework achieved a fairly good average classification accuracy (ACC) of 99.30% and a Matthews correlation coefficient (MCC) of 0.98 using the selected features of the PSO algorithm and the SVM classifier. We found that the RPS of normal EEG signals has a more irregular, complex and unpredictable shape than the RPS of depression EEG signals which has more regular (simple) with less variation and more predictable shape; therefore, we can say that RPS of EEG signals can be used as a biomarker for psychiatrists which are simpler than the EEG signals in visual depression diagnostics. We also found that EEG signals from the right hemisphere are significant for depression detection than the left hemisphere. The proposed framework may be used in clinics and hospitals to detect depression disorder quickly and precisely.

MRI combined with early clinical variables are excellent outcome predictors for newborn infants undergoing therapeutic hypothermia after perinatal asphyxia.

BackgroundBinary prediction-models for outcome [death, cognition, presence and severity of cerebral palsy (CP)], using MRI and early clinical data applicable for individual outcome prediction have not been developed.MethodsFrom Dec 1st 2006 until Dec 31st 2013, we recruited 178 infants into a population-based cohort with moderate or severe hypoxic-ischaemic encephalopathy (HIE) including postnatal collapse (PNC, n = 12) and additional diagnoses (n = 12) using CoolCap/TOBY-trial entry-criteria including depressed amplitude-integrated EEG (aEEG). Early clinical/biochemical variables and MRI scans (median day 8) were obtained in 168 infants. Injury severity was scored for cortex, basal ganglia/thalami (BGT), white matter (WM) and posterior limb of the internal capsule, summating to a total injury score (TIS, range 0–11). Outcome was categorized as adverse or favourable at 18–24 months from Bayley-III domains (cut-off 85) and neurological examination including CP classification.FindingsHIE and entry-aEEG severity were stable throughout the study. Outcome was favourable in 133/178 infants and adverse in 45/178: 17 died, 28 had low Cognition/Language scores, (including 9 with severe CP and 6 mild); seven had mild CP with favourable cognitive outcome. WMxBGT product scores and TIS were strong outcome predictors, and prediction improved when clinical/biochemical variables were added in binary logistic regression. The Positive Predictive Value for adverse outcome was 88%, increasing to 95% after excluding infants with PNC and additional diagnoses. Using WMxBGT in the regression predicted 8 of the 9 children with severe CP.InterpretationBinary logistic regression with WMxBGT or TIS and clinical variables gave excellent outcome prediction being 12% better than single variable cross-tabulation. Our MRI scoring and regression models are readily accessible and deserve investigation in other cohorts for group and individual prediction.FundingWe thank the National Health Service (NHS) and our Universities and funders in UK and Norway: SPARKS, The Moulton Foundation, The Norwegian Research Council, The Lærdal Foundation for Acute Medicine and charitable donations for their support for cooling therapy.

Depression recognition based on the reconstruction of phase space of EEG signals and geometrical features

Depression is a mental disorder that continues to make life difficult or impossible for a depressed person and, if left untreated, can lead to dangerous activities such as self-harm and suicide. Nowadays, Electroencephalogram (EEG) has become an important diagnostic tool for many brain disorders. In this article, a new method for the detection of depression based on the reconstructed phase space (RPS) of EEG signals and geometrical features has been proposed. The RPS of the EEG signals of 22 normal and 22 depressed subjects are plotted in two-dimensional space and, based on their shape, 34 geometrical features are extracted. The p-values for the proposed features were significantly lower (p-value ≈ 0) indicating the capacity of the proposed geometric features for the normal and depression EEG signals classification tasks. For the purpose of reducing feature vector arrays, the performance of four optimization algorithms is checked, namely: ant colony optimization (ACO), grey wolf optimization (GWO), genetic algorithm (GA) and particle swarm optimization (PSO), in which GA with the ability of 58.8% was better than the other optimization algorithms for decreasing the feature vector arrays. Selected features are fed to the support vector machine (SVM) classifier with radial basis function (RBF) kernel and K-nearest neighbors (KNN) classifier with Euclidean and city block distances in 10-fold cross-validation (CV) strategy. The proposed framework achieved a fairly good average classification accuracy (ACC) of 99.30% and a Matthews correlation coefficient (MCC) of 0.98 using the selected features of the PSO algorithm and the SVM classifier. We found that the RPS of normal EEG signals has a more irregular, complex and unpredictable shape than the RPS of depression EEG signals which has more regular (simple) with less variation and more predictable shape; therefore, we can say that RPS of EEG signals can be used as a biomarker for psychiatrists which are simpler than the EEG signals in visual depression diagnostics. We also found that EEG signals from the right hemisphere are significant for depression detection than the left hemisphere. The proposed framework may be used in clinics and hospitals to detect depression disorder quickly and precisely.

EEG features in depressive female adolescents with suicidal and non-suicidal auto-aggressive behavior

IntroductionIn adolescents, both non-suicidal self-injuries (NSSI) and previous suicidal attempts (SA) represent significant risk factors for future suicide. Thus, the search for EEG markers of these forms of auto-aggressive behavior seem to be an actual task.ObjectivesThe aim of the study was to reveal the differences of baseline EEG features in depressive female adolescents with auto-aggressive behavior such as NSSI or SA.MethodsThe study included 45 depressive female in-patients aged 16–25 years. 21 of them showed only NSSI (NSSI subgroup), 24 patients had a history of SA (SA subgroup). Subgroups did not differ in clinical and social-demographic parameters. Baseline EEG spectral power (SP) and its asymmetry were measured.ResultsSA subgroup had higher parietal-occipital alpha-2 (9-11 Hz) SP than NSSI subgroup. Its focus was located in the right hemisphere, and alpha-3 (11-13 Hz) SP was higher than alpha-1 (8-9 Hz). In contrary, in NSSI subgroup alpha-1 SP was higher than alpha-3; and foci of alpha-2 and alpha-3 SP were localized in the left hemisphere.ConclusionsSpatial distribution and the ratio of EEG alpha frequency components SP in the SA subgroup reflect greater activation of brain cortex, especially of the left hemisphere that is more typical for EEG of individuals with increased risk of suicide. In NSSI subgroup, the right hemisphere is relatively more activated that is more typical for EEG in depression without SA. The study supported by RBRF grant No.20-013-00129a.DisclosureNo significant relationships.

Depression Detection Based on Geometrical Features Extracted from SODP Shape of EEG Signals and Binary PSO

Late detection of depression is having detrimental consequences including suicide thus there is a serious need for an accurate computer-aided system for early diagnosis of depression. In this research, we suggested a novel strategy for the diagnosis of depression based on several geometric features derived from the Electroencephalography (EEG) signal shape of the second-order differential plot (SODP). First, various geometrical features of normal and depression EEG signals were derived from SODP including standard descriptors, a summation of the angles between consecutive vectors, a summation of distances to coordinate, a summation of the triangle area using three successive points, a summation of the shortest distance from each point relative to the 45-degree line, a summation of the centroids to centroid distance of successive triangles, central tendency measure and summation of successive vector lengths. Second, Binary Particle Swarm Optimization was utilized for the selection of suitable features. At last, the features were fed to support vector machine and k-nearest neighbor (KNN) classifiers for the identification of normal and depressed signals. The performance of the proposed framework was evaluated by the recorded bipolar EEG signals from 22 normal and 22 depressed subjects. The results provide an average classification accuracy of 98.79% with the KNN classifier using city-block distance in a ten-fold cross-validation strategy. The proposed system is accurate and can be used for the early diagnosis of depression. We showed that the proposed geometrical features are better than extracted features in the time, frequency, time-frequency domains as it helps in visual inspection and provide up to 17.56% improvement in classification accuracy in contrast to those features.

Classification of normal and depressed EEG signals based on centered correntropy of rhythms in empirical wavelet transform domain.

A widespread brain disorder of present days is depression which influences 264 million of the world's population. Depression may cause diverse undesirable consequences, including poor physical health, suicide, and self-harm if left untreated. Depression may have adverse effects on the personal, social, and professional lives of individuals. Both neurologists and researchers are trying to detect depression by challenging brain signals of Electroencephalogram (EEG) with chaotic and non-stationary characteristics. It is essential to detect early-stage depression to help patients obtain the best treatment promptly to prevent harmful consequences. In this paper, we proposed a new method based on centered correntropy (CC) and empirical wavelet transform (EWT) for the classification of normal and depressed EEG signals. The EEG signals are decomposed to rhythms by EWT and then CC of rhythms is computed as the discrimination feature and fed to K-nearest neighbor and support vector machine (SVM) classifiers. The proposed method was evaluated using EEG signals recorded from 22 depression and 22 normal subjects. We achieved 98.76%, 98.47%, and 99.05% average classification accuracy (ACC), sensitivity, and specificity in a 10-fold cross-validation strategy by using an SVM classifier. Such efficient results conclude that the method proposed can be used as a fast and accurate computer-aided detection system for the diagnosis of patients with depression in clinics and hospitals.

Effect of reconstituted glutathione preparations on bioelectric brain activity in severe ethanol poisoning

Fifty patients with acute severe ethanol poisoning (depression of consciousness at the time of admission was up to the level of coma) were examined. Based on the purpose of the study, patients were divided into two groups. In the first group, basic etiopathogenetic therapy was supplemented by the intravenous administration of a drug based on reduced glutathione: inosine glycyl-cysteinyl-glutamate disodium (IGCGD). In the second group, only basic therapy was performed. All patients underwent in the intensive care long-term continuous EEG monitoring, which was started at the end of basic resuscitation and stopped when the patient was clearly awake. Based on the nature of spontaneous EEG and reactivity, patients were divided into subgroups: patients with theta coma pattern and patients with delta coma pattern.It was found that the introduction of IGCGD in the treatment of severe ethanol poisoning in the group of patients with a reactive delta pattern significantly increased the rate of formation of awakening in the EEG: in the subgroup with basic therapy, the formation time of the awakening pattern was about 3 hours, and in the subgroup of patients to whom the drug was administered 1,5 hours. In the group of patients with depression of EEG to the level of theta coma, IGCGD had no effect on the time of formation of the EEG pattern of awakening. The inclusion of inosine IGCGD in the treatment of severe alcohol intoxication was not accompanied by more frequent formation of epileptiform activity in the EEG.

Normalization of EEG in depression after antidepressant treatment with sertraline? A preliminary report

BackgroundMDD patients with abnormal EEG patterns seem more likely to be non-responsive to the antidepressants escitalopram and venlafaxine, but not sertraline, than patients without EEG abnormalities. This finding suggests that patients with both MDD and abnormal EEGs may differentially respond to antidepressant treatment. In the current study, we investigated whether depressed patients with an abnormal EEG show a normalization of the EEG related to antidepressant treatment and response and whether such effect is drug specific, and whether having had early life stress (ELS) increases the chance of abnormal activity. MethodsBaseline and week 8 EEGs and depression symptoms were extracted from a large multicenter study (iSPOT-D, n = 1008) where depressed patients were randomized to escitalopram, sertraline, or venlafaxine-XR treatment. We calculated Odds Ratios of EEG normalization and depression response in patients with an abnormal EEG at baseline, comparing sertraline versus other antidepressants. ResultsFifty seven patients with abnormal EEGs were included. EEGs did not normalize significantly more with sertraline compared to other antidepressants (OR = 1.9, p = .280). However, patients with a normalized EEG taking sertraline were 5.2 times more likely to respond than subjects taking other antidepressants (p = .019). ELS was not significantly related to abnormal activity. LimitationsNeurophysiological recordings were limited in time (two times 2-minute EEGs) and statistical power (n = 57 abnormal EEGs). ConclusionsResponse rates in patients with normalized EEG taking sertraline were significantly larger than in subjects treated with escitalopram/venlafaxine. This adds to personalized medicine and suggests a possible drug repurposing for sertraline.

The effects of alpha asymmetry and high-beta down-training neurofeedback for patients with the major depressive disorder and anxiety symptoms

BackgroundAlpha-asymmetry neurofeedback (ALAY) was applied to patients with major depressive disorder (MDD) based on the theory of frontal alpha asymmetry. Neurophysiological studies have found a higher high-beta activity of electroencephalography (EEG) at the posterior cortex among patients with comorbid MDD and anxiety symptoms. The present study examined the effects of ALAY and high-beta down-training (Beta) neurofeedback in symptoms of depression and anxiety and EEG parameters. MethodEighty-seven patients with comorbid MDD and anxiety symptoms were allocated to the ALAY, Beta, or control groups. Both neurofeedback groups received ten-session neurofeedback. All participants completed the Beck Depression Inventory II (BDI-II), Beck Anxiety Inventory (BAI), and five minutes resting EEG recording at pre-test and post-test. EEG raw signals were transformed into an A1 score [log (F4 alpha) - log (F3 alpha)], P3 and P4 high-beta power. ResultsBDI-II and BAI scores decreased at post-test in both ALAY and Beta groups, but no significant difference between the two groups. No significant interaction effect in A1 score at pre-test and post-test between the ALAY, Beta, and control groups. The P3 high-beta was significantly decreased in the Beta group, an increase in the control group, and no change in the ALAY group at post-test compared to the pre-test. ConclusionsBoth neurofeedback groups decreased symptoms of depression and anxiety. The Beta group was more effective in decreasing high-beta power at the parietal cortex compared to other groups. This non-invasive psychological intervention can be used in the future for patients with comorbid MDD and anxiety symptoms.

An automated diagnosis of depression using three-channel bandwidth-duration localized wavelet filter bank with EEG signals

Depression is a mental illness. If not diagnosed and treated quickly, it can affect one’s mood and quality of life. Modern life is stressful and fast paced, owing to which depression has emerged as a major source of mental health disorder. The electroencephalogram (EEG) signals, which are used to diagnose depression, are non-stationary, non-linear and complex. Their visual interpretation is difficult and takes time. This makes computer-aided depression diagnosis systems highly desirable for the early detection of the depression. This study aims towards the development of depression detection system using EEG based measures. We propose a computer aided depression diagnosis system using newly designed bandwidth-duration localized (BDL) three-channel orthogonal wavelet filter bank (TCOWFB) and EEG signal for the detection of depression. The EEG signal is decomposed into seven wavelet sub-bands (WSBs) using a optimal six-length TCOWFB. The logarithm of L2 norm (LL2N) of six detailed WSBs and one approximate WSB are used as discriminating features.These features are used in the classification of normal or depression EEG signals by applying them to the least square support vector machine (LS-SVM). The proposed system attained the perfect value of 1 for area under the curve (AUC) of receiver’s operating characteristics (ROC) using seven features. The proposed system with ten-fold cross validation (CV) strategy attained an average classification accuracy (ACA) of 99.58%. The proposed model obtained better ACA than the existing automated depression diagnosis systems (ADDS) and perfect AUC-ROC. Hence, it can be used in a clinical setup to diagnose the depression disorder accurately in lesser time, without any subjectivity due to human intervention.

Infant EEG and temperament negative affectivity: Coherence of vulnerabilities to mothers' perinatal depression.

Associations between infants' frontal EEG asymmetry and temperamental negative affectivity (NA) across infants' first year of life and the potential moderating role of maternal prenatal depressive symptoms were examined prospectively in infants (n = 242) of mothers at elevated risk for perinatal depression. In predicting EEG, in the context of high prenatal depressive symptoms, infant NA and frontal EEG asymmetry were negatively associated at 3 months of age and positively associated by 12 months of age. By contrast, for low depression mothers, infant NA and EEG were not significantly associated at any age. Postnatal depressive symptoms did not add significantly to the models. Dose of infants' exposure to maternal depression mattered: infants exposed either pre- or postnatally shifted from a positive association at 3 months to a negative association at 12 months; those exposed both pre- and postnatally shifted from a negative association at 3 months to a positive association at 12 months. Prenatal relative to postnatal exposure did not matter for patterns of association between NA and EEG. The findings highlight the importance of exploring how vulnerabilities at two levels of analysis, behavioral and psychophysiological, co-occur over the course of infancy and in the context of mothers' depressive symptomatology.

Single channel EEG analysis for detection of depression

Abstract Purpose This study is aimed at finding a simple method for detection of depression based on the analysis of single channel short-term EEG signals. Materials and methods The accuracy of linear, spectral asymmetry index (SASI), and nonlinear, detrended fluctuation analysis (DFA), methods for differentiating depressive and healthy subjects was compared. The eyes closed EEG was recorded from 18 common reference (Cz) channels for 34 subjects (17 depressive and 17 control). The signals were stored at 400 Hz sampling frequency and digitally filtered with cutoff frequencies at 0.5 Hz and at 40 Hz. The first 5 min of each recording was selected for further analysis. Results The experiments indicated maximum difference for SASI values in channel Pz and for DFA values in channels Pz and O2. Therefore, channel Pz was selected for comparison of two methods. The results of statistical analysis show that SASI values are significantly higher for depressive than for control group (p = 3.577e–05), while DFA values are significantly lower for depressive group (p = 0.033). SASI has superior discrimination ability with classification accuracy of 76.5%, while the classification accuracy of DFA was 70.6%. Linear combination of SASI and DFA resulted in 91.2% classification accuracy. Conclusions Our results demonstrate that the analysis of single channel signal can provide high accuracy of differentiation depression EEG.