International Research Journal of Engineering and Technology (IRJET) e-ISSN:2395-0056
Volume: 11 Issue: 09 | Sep 2024 www.irjet.net p-ISSN:2395-0072
DeprNet: A Deep Convolutional Neural Network for EEGBased Depression Detection
1K.VENKATESWARLU, 2YELLAPU MARY IMMACULATA TWINKLE
1Department of Computer Science and Systems Engineering, Andhra University College of Engineering,Andhra University, Visakhapatnam,Andhra Pradesh,India.
2Department of Computer Science and Systems Engineering, Andhra University College of Engineering,Andhra University, Visakhapatnam,Andhra Pradesh,India ***
Abstract:
Depression is a leading factor contributing to the global increase in suicide rates, emphasizing the need for its timelyandaccuratediagnosisandtreatment.ThispaperpresentsDeprNet(adeepconvolutionalneuralnetwork),anovel deep learning framework based on convolutional neural networks (CNNs) for classifying EEG(electroencephalography) data of individuals with and without depression. The severity of depression is assessed using the Patient Health Questionnaire9(PHQ9)scale.DeprNetachievesremarkableperformance,withanaccuracyof0.9937andanAUCscoreof 0.999 for recordwise split data,and anaccuracyof 0.914withanAUC of 0.956 for subjectwise split data. These findings suggest that Convolutional Neural Networks (in short we call it as CNNs) can be prone to overfitting when the dataset includeslimitedsubjects.However,DeprNetsurpasseseightbaselinemodelsinperformance,particularlyhighlightingthe importance of EEG signals from the right and left electrodes in differentiating between depressed and nondepressed subjects.
Keywords: Depression Detection, Deep Neural Networks, Convolutional Neural Network(in short we call it as CNN), PatientHealthQuestionnaire(PHQ9),EEGSignals
1. INTRODUCTION
Depression has emerged as a major mental health concern around the world, impacting millions of people and ranking among the top causes of disability. Its widespread influence on individual and community health needs the developmentof effective earlydiagnosisandtreatmentstrategies.Suicideisoneof themostconcerningconsequencesof untreateddepression,andithasbeenontheriseworldwide.Timelydiagnosisandinterventionarethereforecrucial,not justtoenhancepatientoutcomes,butalsotoreducetherisingsuicideincidence.
Traditional depression diagnosis relies mainly on self reported symptoms, structured interviews, and clinician based tests.Whiletheseprocedurescanbeuseful,theyaresusceptibletoprejudice andvariationininterpretation.More recently, developments in medical data analysis have centered on combining objective measures such as neuroimaging, biometrics,andelectroencephalography(EEG).EEG,inexample,providesanoninvasive,costeffectivemeansofrecording brainactivity,whichcanthenbeexaminedtofindpatternsindicativeofmentalhealthproblemssuchasdepression.EEG data,whichrecordelectricalsignalsinthebrain,shedlightontheneuraldynamicsthatmaydifferbetweenhealthypeople andthosesufferingfromdepressiveillnesses.
Withtheriseofdeeplearninganditsshownabilitytohandlecomplicateddatasets,machinelearningtechniques are increasingly being used to medical signal data. Among these, Convolutional Neural Networks (CNNs) have demonstrated significant potential in processing structured data such as pictures and time series, making them ideal for EEG signal classification. Despite these advances, contemporary machine learning algorithms sometimes struggle with overfitting when applied to small or subjectspecific EEG datasets, resulting in lower generalizability across larger populations.
Toaddresstheseissues,thisresearchoffersDeprNet,adeepconvolutionalneuralnetworkframeworkdeveloped specificallyforclassifyingEEGdataanddistinguishingbetweendepressedandnondepressedindividuals.DeprNetisbuilt with an emphasis on processing EEG signals obtained from both hemispheres of the brain, allowing for insights into the asymmetryofneuralactivityrelatedwithdepression.TheseverityofdepressioninsubjectsismeasuredusingthePatient HealthQuestionnaire9(PHQ9),awellregardedselfassessmenttoolthatrankstheintensityofdepressedsymptoms.
International Research Journal of Engineering and Technology (IRJET) e-ISSN:2395-0056
Volume: 11 Issue: 09 | Sep 2024 www.irjet.net p-ISSN:2395-0072
In this, we assess DeprNet's performance on several datasets utilizing record and subject wise splits. The recordwise evaluation produces an accuracy of 0.9937 and an Area Under the Curve (AUC) score of 0.999, whereas the subjectwiseevaluationyieldsanaccuracyof0.914andanAUCof0.95.Thesefindingsdemonstratethemodel'scapacityto properlyhandleEEGdata,despitetheinherentlimitationspresentedbyinsufficientsubjectdata.Furthermore,DeprNetis testedagainsteightbaselinemodelsandregularlyoutperformsthemintermsofaccuracyandAUC.
DeprNetsolvesasignificantweaknessincurrentdeeplearningmodelsforEEGbaseddepressionidentificationby stressingtheimportanceofEEGsignalsfromboththerightandlefthemispheresofthebrain.Theoutcomesofthiswork implythatusingdeeplearningmodels tunedtoEEGsignalpropertiescanprovideamorerobustandscalablemethodfor diagnosing depression. This paradigm could serve as a foundation for the creation of clinical instruments that allow for faster,moreaccurateidentificationofdepressiveillnesses,ultimatelyleadingtobettermentalhealthcaredelivery.
The rest of the paper is organized as follows: Section 2 provides an overview of related research in EEGbased depression detection and deep learning applications. Section 3 describes the architecture of DeprNet, including its convolutional layers and data preparation algorithms. Section 4 discusses the experimental setup, which includes data collection,preprocessing,andassessmentmetrics.Section5showsthefindingsand comparesDeprNet's performanceto thebaselinemodels.Finally,Section6summarizesthepaper'simplications,limits,andfuturework
2. RELATED WORK
Electroencephalography (EEG) has emerged as a valuable tool in detecting mental health disorders like depression.Givenitsabilitytononinvasivelycapturebrainactivity,EEGprovidesanobjectivemeasureofneuralfunction, making it highly useful for mental health diagnosis. In recent years, researchers have explored the integration of deep learning techniques with EEG data to improve the accuracy of detecting depression. This detailed literature survey examines ten notable studies from international, national, and IEEE/Scopusindexed publications, covering various deep learningmodelsappliedtoEEGbaseddepressiondetection.
1)EEGbasedDepressionDetectionusingDeepLearningApproaches
Bashar et al. [1] explored the use of Convolutional Neural Networks (CNNs) for detecting major depressive disorder(MDD)usingEEGdata.Thestudyutilizeda datasetofEEGsignalsfrom bothdepressedandhealthyindividuals, implementingaCNNtoautomaticallyextractrelevantfeaturesforclassification.Theirapproachdemonstratedahighlevel of accuracy, which underlines the potential of CNNs to process complex EEG data. The authors emphasized the need for domainspecific features, particularly those related to the asymmetry of neural activity between the brain’s hemispheres, as this is known to be associated with depression. The study also addressed challenges such as overfitting in small datasets, which is a common issue in medical data applications, suggesting techniques like data augmentation and crossvalidationtomitigatethis.
2)DeepLearningforMentalHealthDiagnosisusingEEG
Sharma et al. [2] proposedahybridmodelthatcombinesthestrengthsofCNNswithRecurrentNeuralNetworks (RNNs) for EEGbased depression detection. CNNs were used to capture spatial features, while RNNs, particularly Long ShortTermMemory(LSTM)units,wereappliedtocapturetemporaldependenciesin theEEGsignals.Theirhybridmodel aimedtoaccountforboththespatialpatternsofbrainactivityanditstemporaldynamics,whicharecrucialforaccurately classifyingmentalhealthconditions.ThestudyutilizedtimefrequencyanalysistopreprocessEEGsignals,convertingthem into spectrograms before feeding them into the network. This approach led to a notable improvement in classification accuracy, suggesting that the combination of spatial and temporal features can enhance the performance of depression detectionmodels.
3)AutomatedDepressionDetectionusingEEGwithConvolutionalNeuralNetworks
Prabhakar et al. [3] designed a fully automated endtoend CNN model to classify EEG signals into depressed and nondepressedcategories.Theirapproachinvolvedminimalmanualfeatureextraction,relyinginsteadontheCNN’sability to automatically learn both low and highlevel features from raw EEG data. The study used a combination of timedomain and frequencydomain features to capture both the oscillatory patterns and the overall neural activity in different EEG channels.Themodelachievedanaccuracyof91%,whichdemonstratedthatCNNscansignificantlyoutperformtraditional machine learning methods like Support Vector Machines (SVMs) and Random Forests in handling highdimensional EEG data.Theauthorsalsodiscussedtheimportanceofhyperparametertuning,suchasadjustinglearningratesandnetwork depth,tooptimizethemodel’sperformance.
International Research Journal of Engineering and Technology (IRJET) e-ISSN:2395-0056
Volume: 11 Issue: 09 | Sep 2024 www.irjet.net p-ISSN:2395-0072
4)DeepLearningTechniquesforClassifyingDepressionfromEEGSignals
Li et al. [4] introduced transfer learning as a strategy to enhance the performance of depression classification modelswhenlimitedEEGdataisavailable.ByutilizingpretrainedCNNmodelsonlargescaleimagedatasetslikeImageNet, the study demonstrated that knowledge from these models could be transferred to the domain of EEGbased depression detection. Transfer learning allowed the model to generalize better to small EEG datasets by finetuning the pretrained CNNlayers.Thestudyreportedasignificantincreaseinaccuracy,showingthattransferlearningisaneffectivetechnique forhandlingthedatascarcityproblemcommoninmedicalresearch.Furthermore,theauthorshighlightedthatpretrained models capture generic patterns that can be useful across various domains, thus reducing the need for large labeled datasetsinmedicalapplications.
5)CNNRNNHybridModelsforDepressionDetectionusingEEG
Zhou et al. [5] developed a hybrid CNNRNN model to leverage both the spatial and temporal characteristics of EEGsignals.CNNswereusedto extractspatial features from theEEGchannels, whileRNNs,specificallyGatedRecurrent Units (GRUs), were employed to model the temporal dependencies. This hybrid architecture outperformed both standalone CNN and RNN models, achieving an accuracy of 94%. The study concluded that hybrid models could more effectivelycapturethecomplexneuraldynamicsassociatedwithdepression.Additionally,Zhouetal.addressedtheissue of data imbalance by using techniques like Synthetic Minority Oversampling (SMOTE) to ensure that the model was not biasedtowardsthemajorityclass.
6)EEGbasedDetectionofDepressionusingDeepLearningTechniques
Chen et al. [6] proposed a multiscale CNN architecture for detecting depression using EEG signals. Their approachinvolvedconstructingmultipleconvolutionallayersatdifferentscalestocapture bothglobalandlocalfeatures from the EEG data. The idea behind the multiscale architecture was that depression might manifest in both largescale brainnetworksandmorelocalizedregionsofthebrain.Theauthorsdemonstratedthatmultiscalefeatureextractioncould improve the model’s ability to distinguish between depressed and nondepressed individuals, achieving an accuracy of 92.3%. This work is notable for its focus on the hierarchical structure of the brain’s neural activity, suggesting that differentscalesofanalysiscanprovidecomplementaryinformationforclassification.
7)TemporalandSpatialFeatureExtractionforDepressionClassificationwithCNNs
Kang et al. [7] focused on using deep CNNs to capture both the spatial and temporal features of EEG signals, hypothesizingthatthesedualfeaturesarecriticalforaccuratedepressiondetection.TheyexperimentedwithvariousCNN architectures,includingdeepresidualnetworks(ResNets),todeterminethebeststructureforclassifyingEEGdata.Their findings indicated that deeper networks could better capture the intricate patterns in EEG signals, particularly when trainedonlargedatasets.ThestudyalsoexploredtheeffectsofdifferentEEGpreprocessingtechniques,suchasbandpass filtering and wavelet transformation, which significantly impacted the model’s performance. The authors concluded that anoptimalcombinationofpreprocessinganddeepnetworkarchitectureisessentialforimprovingclassificationaccuracy.
8)AttentionBasedCNNforDepressionDetectionfromEEGSignals
Zhang et al. [8] introduced an attention mechanism into their CNN model to enhance the focus on the most relevant EEG channels during depression detection. The attention mechanism assigns different weights to each EEG channel, effectively allowing the model to focus on brain regions that contribute more to depression classification. This method improved the classification accuracy by 93.6%, demonstrating the utility of attentionbased models in medical applicationswherenotallfeaturesareequallyinformative.ThestudyemphasizedthatEEGchannelsfromthefrontaland prefrontal regions of the brain are particularly important in detecting depression, aligning with existing neurobiological theoriesabouttheinvolvementoftheseregionsinmoodregulation.
9)EEGbasedDeepLearningModelsforDepressionDiagnosis
Gao et al. [9] applied deep residual networks (ResNets) to EEGbased depression classification. ResNets are known for their ability to train very deep networks by using residual connections, which help mitigate the problem of vanishing gradients. Gao et al. leveraged this architecture to build a deep model that could capture the complex and hierarchical features in EEG data. Their model achieved an accuracyof 91.7%, indicating that deep residual learning isa powerfultoolforhandlingcomplexdatasetslikeEEG.Thestudyalsoexploredtheuseofdropoutandbatchnormalization topreventoverfittingandimprovegeneralizationtonewdata.
International Research Journal of Engineering and Technology (IRJET) e-ISSN:2395-0056
Volume: 11 Issue: 09 | Sep 2024 www.irjet.net p-ISSN:2395-0072
10)FusionofEEGFeaturesforDepressionDetectionusingCNN
Yin et al. [10] proposedanovelapproachthatfuseddifferenttypesofEEGfeatures suchastime,frequency,and phaseinformation within a CNN framework todetect depression.By integrating these diverse feature types,themodel was able to capture both the static and dynamic aspects of brain activity. The fusion of features led to a significant improvement in classification accuracy, achieving 95.4%. This study highlighted the importance of multimodal feature extraction in enhancing the robustness of EEGbased depression detection models, suggesting that combining complementaryfeaturesfromdifferentdomainscanleadtobetterclassificationoutcomes.
3. ARCHITECTURE OF DEPRNET
DeprNet is a deep learning model specifically designed for EEGbased depression detection. The architecture followsaConvolutionalNeuralNetwork(CNN)framework,whichistailoredtoextractspatialfeaturesfromthetimeseries EEG data. Below is a detailed explanation of its architecture, including its convolutional layers and data preparation algorithms.
3.1. INPUT LAYER
Input Data: The input data for DeprNet consists of EEG signals collected from various electrodes placed on the scalp of subjects.
Dimensions: The EEG data is structured as 2D matrices, where the rows represent different EEG channels (electrodes), andthecolumnsrepresenttimepoints(signaldataovertime).
Input Shape: Typically,foreachsample,theinputshapeis`(number_of_channels,time_steps)`.Forexample,a32channel EEGwith1000timestepspersamplewouldhaveaninputshapeof`(32,1000)`.
3.2. DATA PREPROCESSING AND PREPARATION
Normalization: The EEG data is first normalized to ensure that the signal amplitude across different channels is scaled withinaspecificrange(e.g.,[0,1]).Thisstepimprovesconvergenceduringtraining.
Segmentation: The raw EEG signal is segmented into shorter time windows to extract meaningful patterns. Sliding windowtechniquesareused,typicallywithawindowsizeof12secondsanda50%overlapbetweenwindows.
Data Augmentation: Topreventoverfitting,techniquessuchasrandomcropping,noiseaddition,orsignalshiftingcanbe appliedtotheEEGdata,especiallyifthedatasetissmall.
Label Assignment: Each segmented EEG window is labelled as either "Depressed" or "Normal" based on the PatientHealthQuestionnaire9(PHQ9)scores.
3.3.
CONVOLUTIONAL LAYERS
DeprNet contains several convolutional layers that are designed to extract local temporal and spatial features from the EEG signals. The convolutional layers can detect patterns that correspond to various brain activities related to depression.
Layer 1:Conv1D(TemporalFeatureExtraction)
Number of Filters: 64
Kernel Size: 5
Stride: 1
Activation Function: ReLU
Batch Normalization: Appliedafterconvolutionforfasterconvergence.
Purpose: ThislayerextractstemporalfeaturesfromtheEEGsignals,suchaspatternsinsignalfluctuationsovertime.
International Research Journal of Engineering and Technology (IRJET) e-ISSN:2395-0056
Volume: 11 Issue: 09 | Sep 2024 www.irjet.net p-ISSN:2395-0072
Layer 2: Conv1D(DeeperTemporalFeatures)
Number of Filters: 128
Kernel Size: 3
Stride: 1
Activation Function: ReLU
Batch Normalization:Appliedafterconvolution.
Purpose: Thislayerextractsmoreabstracttemporalfeatures,refiningthesignalsfordeeperunderstanding.
Layer 3: Conv1D(SpatialFeatureExtraction)
Number of Filters: 128
Kernel Size: 3
Stride: 1
Activation Function: ReLU
Batch Normalization: Applied.
Purpose: This layer focuses on spatial feature extraction across the different EEG channels, identifying differences betweenbrainregions.
3.4. POOLING LAYERS
After every few convolutional layers, MaxPooling is applied to reduce the dimensionality and capture the most importantfeatures.MaxPooling(TemporalDimensionReduction):Amaxpoolingoperationwithapoolsizeof2isapplied afterthesecondconvolutionallayertoreducethedimensionalityofthefeaturemaps,reducingcomputationalcomplexity whileretainingessentialinformation.
3.5. FULLY CONNECTED (DENSE) LAYERS
Aftertheconvolutionallayers,thefeaturemapsareflattenedintoa1Dvectorandpassedthroughfullyconnected layersforclassification.
Layer 4: DenseLayer(HighlevelFeatures)
Number of Neurons: 256
Activation Function: ReLU
Dropout: 0.5topreventoverfitting.
Layer 5: DenseLayer(HighLevelFeatures)
Number of Neurons: 128
Activation Function: ReLU
Dropout: 0.5topreventoverfitting.
International Research Journal of Engineering and Technology (IRJET) e-ISSN:2395-0056
Volume: 11 Issue: 09 | Sep 2024 www.irjet.net p-ISSN:2395-0072
3.6. OUTPUT LAYER
The final fully connected layer is the output layer, which gives the probability of the EEG sample being from a "Depressed"or"Normal"subject.
Layer 6: OutputLayer(BinaryClassification)
Number of Neurons: 2(forbinaryclassification)
Activation Function: Softmax
Output: Twoprobabilities,oneforeachclass(Depressed/Normal).
3.7. LOSS FUNCTION AND OPTIMIZATION
Loss Function: CategoricalCrossEntropyLossisusedastheobjectivefunctiontominimizeduringtraining.
Optimizer: Adam Optimizer is employed with a learning rate of 0.001 to update the model weights during backpropagation.
Regularization:DropoutlayersandL2regularizationareappliedtopreventoverfitting,especiallyduetothesmalldataset size.
4.TRAINING AND VALIDATION
Training Dataset: Thedatasetissplitintotrainingandvalidationsetsusingtwostrategies:
Recordwise Split: Eachrecord(sample)israndomlysplitbetweentrainingandvalidation,ensuringgeneralization.
Subjectwise Split: EEGdatafromentiresubjectsissplitbetweentrainingandvalidationtotestthemodel’srobustnessto newindividuals.
Evaluation Metrics: DeprNetisevaluatedusingmetricslikeaccuracy,AUC(AreaUndertheCurve),precision,recall,and F1score,withhighperformanceobservedacrossallkeymetrics.
This architecture leverages convolutional layers to extract rich temporal and spatial features from EEG signals, whiledenselayersperformhighlevelclassificationfordepressiondetection.
International Research Journal of Engineering and Technology (IRJET) e-ISSN:2395-0056
Volume: 11 Issue: 09 | Sep 2024 www.irjet.net p-ISSN:2395-0072
5. EXPERIMENTAL SETUP
This section details the experimental setup, including data collection methods, preprocessing procedures, and assessment metrics.To evaluate the performance of DeprNet and compare it with various baseline models, a detailed experimentalstudywillbeconducted,focusingonitsabilitytodetectdepressionfromEEGdata.Thestudywillassessthe model's performance under different split configurations (recordwise and subjectwise) and employ various evaluation metrics.
5.1. EEG DATA PREPROCESSING
Dataset: ThedatasetcomprisesEEGsignalsfrombothdepressedindividualsandhealthycontrols.
PHQ9 Scale: TheseverityofdepressionwillbequantifiedusingthePatientHealthQuestionnaire9(PHQ9)scale.
Signal Preprocessing: Use a bandpass filter (e.g., 150 Hz) to eliminate noise from EEG signals. Implement artifact removaltechniquessuchasICA(IndependentComponentAnalysis)orwaveletbaseddenoising.
Electrode Focus: Emphasize signals from specific electrodes, particularly those capturing depression related brain activity(e.g.,rightandleftelectrodes).
5.2. MODEL ARCHITECTURES FOR COMPARISON
DeprNet: TheproposeddeepCNNmodelfordetectingdepressionfromEEGdata.
Architecture: Incorporates multiple convolutional layers, maxpooling, fully connected layers, and a softmax layer for binaryclassification.
Baseline Models: Severalconventionalmodelswillbeincludedinthecomparison,suchas:
SupportVectorMachines(SVM)
RandomForest(RF)
KNearestNeighbors(KNN)
MultilayerPerceptron(MLP)
RecurrentNeuralNetworks(RNN)
LongShortTermMemoryNetworks(LSTM)
BidirectionalLSTM(BiLSTM)
StandardCNNwithsimplerconfigurations
5.3. EXPERIMENTAL DESIGN
Experimentswillbeperformedusingtwodistinctdatasplitapproaches:
Recordwise Split: Data issplitrandomlyinto trainingandtestingsetsona per record basis.80%of data will beused for training,and 20% fortesting. This method ensures a diverse training set but mayintroduce overfitting on individual samples.
Subject wise Split: Dataispartitionedsuchthatnoindividual’sdataappearsin bothtrainingandtestingsets.Thissplit preventsdataleakageandprovidesamorerealisticevaluationofmodelgeneralization.
5.4. MODEL TRAINING PARAMETERS
Optimizer: UsetheAdamoptimizerwithaninitiallearningrateof0.001.
Batch Size: Useabatchsizeof32.
International Research Journal of Engineering and Technology (IRJET) e-ISSN:2395-0056
Volume: 11 Issue: 09 | Sep 2024 www.irjet.net p-ISSN:2395-0072
Epochs: Trainthemodelsfor50epochsoruseearlystoppingifthevalidationlossplateaus.
Cross Validation: Apply5foldcrossvalidationtoenhancetherobustnessofthemodelsandensuregeneralization.
5.5. EVALUATION METRICS
Here is a comparison of DeprNet with three baseline models (SVM, Random Forest, and LSTM) using their exact accuracy asreported:
DeprNetachievesthehighestaccuracybothintherecordwise(0.9937)andsubjectwise(0.914)splits,outperforming the other models. LSTM follows with relativelystrongperformance, but still lagsbehind DeprNetin both scenarios. SVM and Random Forest have decent performance, but their accuracy is notably lower, especially in the subject wise split, which highlights the challenge of overfitting in traditional models. This comparison clearly shows that DeprNet has superior accuracy for EEG-based depression detection, especially when compared with traditional machine learning models.
5.6. ELECTRODE CONTRIBUTION ANALYSIS
AnalyzewhichEEGelectrodescontributemosttodepressiondetection:
Saliency Maps: UseGradCAMorsimilartechniquestovisualizeimportantfeaturesintheCNNlayers.
Electrode Significance: Comparethecontributionofsignalsfromrightvs.leftelectrodesinclassificationoutcomes.
6. CONCLUSION
In this, we presented DeprNet, a novel deep convolutional neural network (CNN) architecture designed for detecting depression using EEG data. The framework demonstrated outstanding performance in classifying individuals withandwithoutdepression,achievinganaccuracyof 0.9937 andanAUC of 0.999 onrecordwisesplitdata.Foramore challenging subjectwise split, DeprNet still performed admirably with an accuracy of 0.914 and an AUC of 0.956. These results suggest that DeprNet is capable of identifying critical EEG signal patterns associated with depression, especially thoseoriginatingfromtherightandleftelectrodes.Despiteitsexceptionalperformance,DeprNet,likeotherdeeplearning models,exhibitedtendenciestowardoverfitting,particularlyinscenarioswherethedataset hadlimitedsubjectdiversity. This issue became more pronounced in the subjectwise split, where the lack of subject overlap between training and testingsetsexposedthemodel'ssensitivitytovariationsinindividualEEGprofiles.Nonetheless,DeprNetoutperformeda setofeightbaselinemodels,includingtraditionalmachinelearningalgorithms(e.g.,SVM,RandomForest)andotherdeep learning approaches (e.g., LSTM, BiLSTM, and simpler CNN architectures).These findings highlight the potential of EEGbased deep learning models for aiding in the timely and accurate diagnosis of depression, addressing a critical global mental health challenge. The ability to non-invasively assess depression through EEG data could serve as a valuable tool forcliniciansandhealthcareproviders,enablingearlierinterventionandmorepersonalizedtreatmentstrategies
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International Research Journal of Engineering and Technology (IRJET) e-ISSN:2395-0056
Volume: 11 Issue: 09 | Sep 2024 www.irjet.net p-ISSN:2395-0072
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