Classification of Electroencephalogram Generated by Brain for Analysis of Brain Wave Signals in Students Depression

  • Aditya Dimas Aditya Dimas
Keywords: EEG, emotion, Discrete Wavelet Transform, Artificial Neural Network, backpropagation

Abstract

Electroencephalogram (EEG) is a brain signal processing technique used to detect abnormal brain waves. EEG signal recording uses electrodes placed on the scalp. AD620 is used to obtain high input impedance. The EEG signal is amplified by transmitting the signal into the notch filter, high pass filter and low pass filter to improve the quality of the signal such as eliminating and reducing noise. EEG recording focuses on analyzing alpha waves to determine whether the subject is students who suffering depression. For cases of depression, the lobes of the brain placed electrodes are the Occipital and Parietal lobes of the brain. The selection of participants in this study was based on the results of tests using the Student Health Questionnaire-9 (PHQ-9) method. The results obtained after recording EEG showed there was theta wave shaped abnormally large and abnormal alpha wave who shaped abnormally large.

 

References

Sasikumar Gurumurthy, Vudi Sai Mahit, Rittwika Ghosh (2013), Analysis and simulation of brain signal data by EEG signal processing technique using MATLAB. International Journal of Engineering and Technology (IJET).

M.E. Chandrasiri, R.M.T.M. Dhanapala, W.G.K.G. Kumari, R. Ranaweera (2013), PC Based Electroencephalogram System. IEEE 8th International Conference on Industrial and Information Systems.

Esmeralda C. Djamal & Harijono A. Tjokronegoro (2005), Identifikasi dan Klasifikasi Sinyal EEG Terhadap Rangsangan Suara dengan Ekstraksi Wavelet dan Spektral Daya. Departemen Teknik Fisika ITB.

Yousef Mohammadi, Mojtaba Hajian, Mohammad Hassan Moradi (2019), Discrimination of Depression Levels Using Machine Learning Methods on EEG Signals. 27th Iranian Conference on Electrical Engineering (ICEE2019).

Mojtaba Hajian, Mohammad Hassan Moradi (2017), Quantification of depression disorder using EEG signal. 24th national and 2nd International Iranian Conference on Biomedical Engineering (ICBME), Amirkabir University of Technology, Tehran, Iran. U.

Rajendra Acharya, Vidya K. Sudarshan, Hojjat Adeli (2015), A Novel Depression Diagnosis Index Using Nonlinear Features in EEG Signals. Department of Electronics and Computer Engineering Ngee Ann Polytechnic, 535 Clementi Road Singapore.

Shamla Mantri, Dr. Pankaj Agrawal, Dr. Diptil Patil (2015), An Advanced Design for Depression Analysis through EEG Signal. International Journal of Scientific Engineering and Research (IJSER).

Jian Shen, Shengjie Zhao, Yuan YaoYue Wang, Lei Feng (2017), A novel depression detection method based on pervasive EEG and EEG splitting criterion. IEEE International Conference on Bioinformatics and Biomedicine (BIBM).

Jian Shen, Xiaowei Zhang, Bin Hu, Gang Wang (2019), An Improved Empirical Mode Decomposition of Electroencephalogram Signals for Depression Detection.

Hansu Cai, Jiashuo Han, Yunfei Chen, Xiaocong Sha (2018), A Pervasive Approach to EEG-Based Depression Detection.

Robinsar Parlindungan (2008), Analisis Waktu-Frekuensi (TFA) Gelombang EEG.

Andrew Paul Simms (2014), Reading and Wirelessly Sending EEG Signals Using Arduinos and XBee Radios to Control a Robot. Electrical Engineering University of Arkansas, Fayetteville.

M. Emin Sahin, Yunus Ucar, Feyzullah Temurtas (2016). An Implementation of Analog Portable EEG Signal Extraction System.

Amlan Jyoti, Riku Chutia (2016), Design of Single Channel Portable EEG Signal Acquisition System for Brain Computer Interface Application. International Journal of Biomedical Engineering and Science (IJBES).

Submitted
2022-06-05
Accepted
2022-12-30
How to Cite
Dimas, A. (2022). Classification of Electroencephalogram Generated by Brain for Analysis of Brain Wave Signals in Students Depression. International Journal of Engineering Technology and Natural Sciences, 4(2), 95 - 101. https://doi.org/10.46923/ijets.v4i2.155