Automated Approach for Depression Recognition Using Fast Fourier Transform Based EEG Signals

[ 1] Jaworska, Natalia, et al. "Leveraging machine learning approaches for predicting

antidepressant treatment response using electroencephalography (EEG) and clinical

data." Frontiers in psychiatry 9 (2019): 768.

[ 2] Mumtaz, Wajid, et al. "A wavelet-based technique to predict treatment outcome for major

depressive disorder." PloS one 12.2 (2017): e0171409.

[ 3] Zhu, Jing, et al. "Toward depression recognition using EEG and eye tracking: an ensemble

classification model CBEM." 2019 IEEE International Conference on Bioinformatics and

Biomedicine (BIBM). IEEE, 2019.

[ 4] Jiang, Chao, et al. "Enhancing EEG-based classification of depression patients using spatial

information." IEEE Transactions on Neural Systems and Rehabilitation Engineering 29 (2021):

-575.

[ 5] Zhang, Bingtao, et al. "Brain functional networks based on resting-state EEG data for major

depressive disorder analysis and classification." IEEE Transactions on Neural Systems and

Rehabilitation Engineering 29 (2020): 215-229.

[ 6] Zhu, Yibo, et al. "Classifying major depressive disorder using fNIRS during motor

rehabilitation." IEEE Transactions on Neural Systems and Rehabilitation Engineering 28.4

(2020): 961-969.

[ 7] Song, XinWang, et al. "LSDD-EEGNet: An efficient end-to-end framework for EEG-based

depression detection." Biomedical Signal Processing and Control 75 (2022): 103612.

[ 8] Dev, Antora, et al. "Exploration of EEG-based depression biomarkers identification techniques

and their applications: A systematic review." IEEE Access (2022).

[ 9] Zhu, Yibo, et al. "Classifying major depressive disorder using fNIRS during motor

rehabilitation." IEEE Transactions on Neural Systems and Rehabilitation Engineering 28.4

(2020): 961-969.

[ 10] Dessai, Sukanya, and Soniya Shakil Usgaonkar. "Depression Detection on Social Media Using

Text Mining." 2022 3rd International Conference for Emerging Technology (INCET). IEEE,

[ 11] Movahed, Reza Akbari, et al. "A major depressive disorder classification framework based on

EEG signals using statistical, spectral, wavelet, functional connectivity, and nonlinear

analysis." Journal of Neuroscience Methods 358 (2021): 109209.

[ 12] Mahato, Shalini, and Sanchita Paul. "Detection of major depressive disorder using linear and

non-linear features from EEG signals." Microsystem Technologies 25.3 (2019): 1065-1076.

[ 13] Jiang, Chao, et al. "Enhancing EEG-based classification of depression patients using spatial

information." IEEE transactions on neural systems and rehabilitation engineering 29 (2021):

-575.

[ 14] Harati, Sahar, et al. "Depression severity classification from speech emotion." 2018 40th Annual

International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC).

IEEE, 2018.

[ 15] Jiang, Zifan, et al. "Classifying major depressive disorder and response to deep brain

stimulation over time by analyzing facial expressions." IEEE Transactions on Biomedical

Engineering 68.2 (2020): 664-672.

[ 16] Acharya, U. Rajendra, et al. "A novel depression diagnosis index using nonlinear features in

EEG signals." European neurology 74.1-2 (2015): 79-83.

[ 17] Li, Xiaowei, et al. "A deep learning approach for mild depression recognition based on

functional connectivity using electroencephalography." Frontiers in neuroscience 14 (2020): 192.

[ 18] Bachmann, Maie, et al. "Methods for classifying depression in single channel EEG using linear

and nonlinear signal analysis." Computer methods and programs in biomedicine 155 (2018): 11-

[ 19] Sharma, Manish, et al. "An automated diagnosis of depression using three-channel bandwidthduration

localized wavelet filter bank with EEG signals." Cognitive Systems Research 52 (2018):

-520.

[ 20] Saeedi, Maryam, Abdolkarim Saeedi, and Arash Maghsoudi. "Major depressive disorder

assessment via enhanced k-nearest neighbor method and EEG signals." Physical and Engineering

Sciences in Medicine 43.3 (2020): 1007-1018.

[ 21] Mahato, Shalini, and Sanchita Paul. "Classification of depression patients and normal subjects

based on electroencephalogram (EEG) signal using alpha power and theta asymmetry." Journal

of medical systems 44.1 (2020): 1-8.

[ 22] Akbari, Hesam, et al. "Depression recognition based on the reconstruction of phase space of

EEG signals and geometrical features." Applied Acoustics 179 (2021): 108078

[ 23] Hagemann, Dirk, Ewald Naumann, and Julian F. Thayer. "The quest for the EEG reference

revisited: A glance from brain asymmetry research." Psychophysiology 38.5 (2001): 847-857.

[ 24] Teplan, Michal. "Fundamentals of EEG measurement." Measurement science review 2.2 (2002):

-11.

[ 25] Newson, Jennifer J., and Tara C. Thiagarajan. "EEG frequency bands in psychiatric

disorders: a review of resting state studies." Frontiers in human neuroscience 12 (2019): 521.

[ 26] Brigham, E. Oran. The fast Fourier transform and its applications. Prentice-Hall, Inc., 1988.

[ 27] Almuhammadi, Wafaa S., Khald AI Aboalayon, and Miad Faezipour. "Efficient obstructive

sleep apnea classification based on EEG signals." 2015 Long Island Systems, Applications and

Technology. IEEE, 2015.

[ 28] Shakshi, Ramavtar Jaswal. "Brain wave classification and feature extraction of EEG signal by

using FFT on lab view." Int. Res. J. Eng. Technol 3 (2016): 1208-1212.

[ 29] Barahona-Pereira, David. "Evaluation of feature extraction techniques for an Internet of Things

Electroencephalogram." (2016).

[ 30] Bekele, A. J. A. A. "Cooley-tukey fft algorithms." Advanced algorithms (2016).

[ 31] Vetterling, William T., et al. Numerical recipes: example book C. Cambridge University Press,

[ 32] Delgado-Bonal, Alfonso, and Alexander Marshak. "Approximate entropy and sample entropy:

A comprehensive tutorial." Entropy 21.6 (2019): 541.

[ 33] Wu, Yue, et al. "Local Shannon entropy measure with statistical tests for image

randomness." Information Sciences 222 (2013): 323-342.

[ 34] Fazan, Frederico Sassoli, et al. "Changes in the complexity of heart rate variability with exercise

training measured by multiscale entropy-based measurements." Entropy 20.1 (2018): 47.

[ 35] Azami, Hamed, and Javier Escudero. "Amplitude-and fluctuation-based dispersion

entropy." Entropy 20.3 (2018): 210.

[ 36] Akbari, Hesam, et al. "Depression Detection Based on Geometrical Features Extracted from

SODP Shape of EEG Signals and Binary PSO." Traitement du Signal 38.1 (2021).

[ 37] Akbari, Hesam, Sedigheh Ghofrani, and S. Ghofrani. "Fast and accurate classification F and

NF EEG by using SODP and EWT." Int J Image Graph Signal Process 11.11 (2019): 29-35.

[ 38] Singh, Ardra, and A. Amalin Prince. "FPGA implementation of second-order difference plot for

epileptic seizure detection in EEG signals." 2015 Annual IEEE India Conference (INDICON).

IEEE, 2015.

[ 39] Nachar, Nadim. "The Mann-Whitney U: A test for assessing whether two independent samples

come from the same distribution." Tutorials in quantitative Methods for Psychology 4.1 (2008):

-20.

[ 40] Elgendi, Mohamed, et al. "Optimization of EEG frequency bands for improved diagnosis of

Alzheimer disease." 2011 Annual International Conference of the IEEE Engineering in Medicine

and Biology Society. IEEE, 2011.

[ 41] Salankar, Nilima, et al. "EEG based alcoholism detection by oscillatory modes decomposition

second order difference plots and machine learning." Biocybernetics and Biomedical

Engineering 42.1 (2022): 173-186.

[ 42] Deng, Wu, et al. "A novel intelligent diagnosis method using optimal LS-SVM with improved

PSO algorithm." Soft Computing 23.7 (2019): 2445-2462.

Chomboon, Kittipong, et al. "An empirical study of distance metrics for k-nearest neighbor

algorithm." Proceedings of the 3rd international conference on industrial application engineering.

[ 44] Freund, Yoav, and Llew Mason. "The alternating decision tree learning algorithm." icml. Vol.

1999.