Non invasive EEG signal processing framework for real time depression analysis

Mantri, Shamla; Patil, Dipti D.; Agrawal, Pankaj; Wadhai, Vijay M.

doi:10.1109/intellisys.2015.7361188

Cited by 33 publications

(17 citation statements)

References 4 publications

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“…In the literature, various features have been extracted from EEG signals and have shown the importance of MDD diagnosis. Mantri et al ( 2015 ) reported a classification accuracy of 84% based on the power spectrum, involving 13 patients with depression and 12 HCs. In 2017, Mumtaz et al ( 2017b ) extracted features using wavelet transform to achieve an accuracy of 87.5%.…”

Section: Introductionmentioning

confidence: 99%

Machine Learning Approaches for MDD Detection and Emotion Decoding Using EEG Signals

Duan

Duan²,

Qiao

et al. 2020

Front. Hum. Neurosci.

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Emotional decoding and automatic identification of major depressive disorder (MDD) are helpful for the timely diagnosis of the disease. Electroencephalography (EEG) is sensitive to changes in the functional state of the human brain, showing its potential to help doctors diagnose MDD. In this paper, an approach for identifying MDD by fusing interhemispheric asymmetry and cross-correlation with EEG signals is proposed and tested on 32 subjects [16 patients with MDD and 16 healthy controls (HCs)]. First, the structural features and connectivity features of the θ-, α-, and β-frequency bands are extracted on the preprocessed and segmented EEG signals. Second, the structural feature matrix of the θ-, α-, and β-frequency bands are added to and subtracted from the connectivity feature matrix to obtain mixed features. Finally, the structural features, connectivity features, and the mixed features are fed to three classifiers to select suitable features for the classification, and it is found that our mode achieves the best classification results using the mixed features. The results are also compared with those from some state-of-the-art methods, and we achieved an accuracy of 94.13%, a sensitivity of 95.74%, a specificity of 93.52%, and an F1-score (f1) of 95.62% on the data from Beijing Anding Hospital, Capital Medical University. The study could be generalized to develop a system that may be helpful in clinical purposes.

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Section: Introductionmentioning

confidence: 99%

Machine Learning Approaches for MDD Detection and Emotion Decoding Using EEG Signals

Duan

Duan²,

Qiao

et al. 2020

Front. Hum. Neurosci.

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“…[51] Visual [52] N/A [53], [54], [55], [56], [57], [58], [59], [60], [61], [45], [33], [62], [63], [64], [65], [66] Depression both mild and severe stages Emotional Note reading [67] Audio [68] Emotional Picture [69], [70], [71], [72] Distractor and target [73] N/A [74], [75], [76], [77], [78], [79], [80], [81], [82], [83], [84], [85], [86], [87], [88] Depression for other reasons (stress/ epilepsy) Audio [89], [90] N/A [91], [92], [93] This…”

Section: Mild Depressionmentioning

confidence: 99%

“…The windowing method is used as a pre-processing process as it has nonstationary nature [78] and processes the time series data and reshapes the complete information with fixed sized window. It provides the possible necessary information at a given point of time to have the right prediction through the demonstrated model [86].…”

Section: ) Signal Acquisition and Electrode Placementmentioning

confidence: 99%

Exploration of EEG-Based Depression Biomarkers Identification Techniques and Their Applications: A Systematic Review

et al. 2022

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Depression is the most common mental illness, which has become the major cause of fear and suicidal mortality or tendencies. Currently, about 10% of the world population has been suffering from depression. The classical approach for detecting depression relies on the clinical questionnaire, which depends on the patients' responses as well as observing their behavioral activities. However, there is no established method to detect depression from EEG biomarkers. Therefore, exploration of EEG biomarkers for depression assessments is vital and has a great potential to improve our understanding and clinical interventions. In this study, we have conducted a systematic review of 52 research articles using the PRISMA-P systematic review protocol, where we analyzed their research methodologies and outcomes. We categorized the experimentations in these articles according to their physical and psychological aspects scaled by the commonly used clinical questionnaire-based assessments. This study finds that the negative stimuli are the better identification strategies for evaluating depression through EEG signals. From this exploration, researchers observed that the Neural Connectivity Analysis and Brain Topological Mapping have huge potentials for finding depression biomarkers, and it is evident that the right-side hemisphere and frontal and parietal-occipital cortex are distinct regions to detect depression using EEG signals. For this mechanism, researchers are using many signal processing and machine learning approaches. In the case of filtering, Independent Component Analysis (ICA) is commonly used to eliminate physiological and non-physiological artifacts. Among machine learning approaches, Convolutional Neural Network (CNN) and Support Vector Machine (SVM) showed better performance for classifying healthy and depressed brains. The authors hope, this study will create an opportunity to explore more in the future for EEG as diagnostic tools by analyzing brain functional connectivity for focusing on clinical interventions.

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“…FFT is denoted by ( ) and EEG signals in time domain denoted by ( ) [16]. MATLAB statistical toolbox was utilized to extract ten signal features from each of the three EEG datasets (namely mean, median, standard deviation, mean absolute deviation, skewness, kurtosis, spectral entropy and dominant frequency features (maximum frequency, maximum value, maximum ratio)) [17].…”

Section: Feature Extractionmentioning

confidence: 99%

Factors influencing low intension detection rate in a non-invasive EEG-based brain computer interface system

Maswanganyi¹,

Tu²,

Owolawi³

et al. 2020

IJEECS

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Motor imagery (MI) responses extracted from the brain in the form of EEG signals have been widely utilized for intention detection in brain computer interface (BCI) systems. However, due to the non-linearity and the non-stationarity of EEG signals, BCI systems suffer from low MI prediction rate with both known and unknown influncing factors. This paper investigates the impact of visual stimulus, feature dimensions and artifacts on MI task detection rate, towards improving MI prediction rate. Three EEG datasets were utilized to facilitate the investigation. Three filters (band-pass, notch and common average reference) and the independent component analysis (ICA) were applied on each datasets, to eliminate the impact of artifact. Three sets of features where extracted from artifact free ICA components, from which more relevant features were selected. Moreover, the selected feature subsets were incorporated into three classifiers, NB, Regression Tree and K-NN to predict four MI and hybrid tasks. K-NN classifier outperformed the other two classifies in each dataset. The highest classification accuracy is obtained in hybrid task EEG dataset. Moreover, accurately predicted EEG classes were applied to a robotic arm control.

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Non invasive EEG signal processing framework for real time depression analysis

Cited by 33 publications

References 4 publications

Machine Learning Approaches for MDD Detection and Emotion Decoding Using EEG Signals

Machine Learning Approaches for MDD Detection and Emotion Decoding Using EEG Signals

Exploration of EEG-Based Depression Biomarkers Identification Techniques and Their Applications: A Systematic Review

Factors influencing low intension detection rate in a non-invasive EEG-based brain computer interface system

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