Applications and challenges of wearable electroencephalogram signals in depression recognition and personalized music intervention_Journal of Biomedical Engineering

Authors：

 CUI Xingran , QIN Zeguang , GAO Zhilin , WAN Wang , GU Zhongze

School of Biological Science and Medical Engineering, Southeast University, Nanjing 210096, P. R. China;

Corresponding author：

CUI Xingran, Email: cuixr@seu.edu.cn

Keywords：

Wearable electroencephalogram signals; Depression recognition; Personalized intervention; Brain-computer music interface

DOI：

10.7507/1001-5515.202210065

Video：

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Rapid and accurate identification and effective non-drug intervention are the worldwide challenges in the field of depression. Electroencephalogram (EEG) signals contain rich quantitative markers of depression, but whole-brain EEG signals acquisition process is too complicated to be applied on a large-scale population. Based on the wearable frontal lobe EEG monitoring device developed by the authors’ laboratory, this study discussed the application of wearable EEG signal in depression recognition and intervention. The technical principle of wearable EEG signals monitoring device and the commonly used wearable EEG devices were introduced. Key technologies for wearable EEG signals-based depression recognition and the existing technical limitations were reviewed and discussed. Finally, a closed-loop brain-computer music interface system for personalized depression intervention was proposed, and the technical challenges were further discussed. This review paper may contribute to the transformation of relevant theories and technologies from basic research to application, and further advance the process of depression screening and personalized intervention.

Citation： CUI Xingran, QIN Zeguang, GAO Zhilin, WAN Wang, GU Zhongze. Applications and challenges of wearable electroencephalogram signals in depression recognition and personalized music intervention. Journal of Biomedical Engineering, 2023, 40(6): 1093-1101. doi: 10.7507/1001-5515.202210065 Copy

1.	Lu J, Xu X, Huang Y, et al. Prevalence of depressive disorders and treatment in China: a cross-sectional epidemiological study. Lancet Psychiatry, 2021, 8(11): 981-990.
2.	World Health Organization (WHO). Depression. (2023-03-31)[2023-05-09]. https: //www.who.int/news-room/fact-sheets/detail/depression.
3.	Hopwood M. Anxiety symptoms in patients with major depressive disorder: commentary on prevalence and clinical implications. Neurology and Therapy, 2023, 12(Suppl 1): 5-12.
4.	Dev A, Roy N, Islam M K, et al. Exploration of EEG-based depression biomarkers identification techniques and their applications: A systematic review. IEEE Access, 2022, 10: 16756-16781.
5.	Gao Z, Cui X, Wan W, et al. Signal quality investigation of a new wearable frontal lobe EEG device. Sensors (Basel), 2022, 22(5): 1898.
6.	Sawangjai P, Hompoonsup S, Leelaarporn P, et al. Consumer grade EEG measuring sensors as research tools: a review. IEEE Sensors Journal, 2019, 20(8): 3996-4024.
7.	Ruiz N A L, Del Ángel D S, Olguín H J, et al. Neuroprogression: the hidden mechanism of depression. Neuropsychiatric Disease and Treatment, 2018, 14: 2837-2845.
8.	Schmaal L, Pozzi E, C Ho T, et al. ENIGMA MDD: seven years of global neuroimaging studies of major depression through worldwide data sharing. Translational psychiatry, 2020, 10(1): 172.
9.	de Aguiar Neto F S, Rosa J L G. Depression biomarkers using non-invasive EEG: a review. Neuroscience & Biobehavioral Reviews, 2019, 105: 83-93.
10.	Kappel S L, Rank M L, Toft H O, et al. Dry-contact electrode ear-EEG. IEEE Transactions on Biomedical Engineering, 2019, 66(1): 150-158.
11.	Wan W, Cui X, Gao Z, et al. Frontal EEG-based multi-level attention states recognition using dynamical complexity and extreme gradient boosting. Frontiers in Human Neuroscience, 2021, 15: 673955.
12.	Gao Z, Cui X, Wan W, et al. Long-range correlation analysis of high frequency prefrontal electroencephalogram oscillations for dynamic emotion recognition. Biomedical Signal Processing and Control, 2022, 72: 103291.
13.	Heidlmayr K, Kihlstedt M, Isel F. A review on the electroencephalography markers of Stroop executive control processes. Brain and Cognition, 2020, 146: 105637.
14.	Jiang X, Bian G B, Tian Z. Removal of artifacts from EEG signals: a review. Sensors (Basel), 2019, 19(5): 987.
15.	Cong F. Blind source separation//Hu L, Zhang Z. EEG Signal Processing and Feature Extraction, Singapore: Springer, 2019: 117-140.
16.	Stergiadis C, Kostaridou V D, Klados M A. Which BSS method separates better the EEG signals? A comparison of five different algorithms. Biomedical Signal Processing and Control, 2022, 72: 103292.
17.	Cheng J, Li L, Li C, et al. Remove diverse artifacts simultaneously from a single-channel EEG based on SSA and ICA: a semi-simulated study. IEEE Access, 2019, 7: 60276-60289.
18.	Kumaravel V P, Kartsch V, Benatti S, et al. Efficient artifact removal from low-density wearable EEG using artifacts subspace reconstruction//2021 43rd Annual International Conference of the IEEE Engineering in Medicine & Biology Society (EMBC). IEEE, 2021: 333-336.
19.	Lin I M, Chen T C, Lin H Y, et al. Electroencephalogram patterns in patients comorbid with major depressive disorder and anxiety symptoms: proposing a hypothesis based on hypercortical arousal and not frontal or parietal alpha asymmetry. Journal of Affective Disorders, 2021, 282: 945-952.
20.	Bachmann M, Päeske L, Kalev K, et al. Methods for classifying depression in single channel EEG using linear and nonlinear signal analysis. Computer Methods and Programs in Biomedicine, 2018, 155: 11-17.
21.	杜雪云, 王淑君, 张轩, 等. 慢性失眠伴焦虑抑郁患者认知功能障碍的γ振荡研究. 中华行为医学与脑科学杂志, 2020, 29(6): 523-528.
22.	Lee P F, Kan D P X, Croarkin P, et al. Neurophysiological correlates of depressive symptoms in young adults: a quantitative EEG study. Journal of Clinical Neuroscience, 2018, 47: 315-322.
23.	Koo P C, Berger C, Kronenberg G, et al. Combined cognitive, psychomotor and electrophysiological biomarkers in major depressive disorder. European Archives of Psychiatry and Clinical Neuroscience, 2019, 269(7): 823-832.
24.	Knociková J A, Petrásek T. Quantitative electroencephalographic biomarkers behind major depressive disorder. Biomedical Signal Processing and Control, 2021, 68: 102596.
25.	Ahmadlou M, Adeli H, Adeli A. Fractality analysis of frontal brain in major depressive disorder. International Journal of Psychophysiology, 2012, 85(2): 206-211.
26.	Faust O, Ang P C A, Puthankattil S D, et al. Depression diagnosis support system based on EEG signal entropies. Journal of Mechanics in Medicine and Biology, 2014, 14(3): 1450035.
27.	Čukić M, Stokić M, Radenković S, et al. Nonlinear analysis of EEG complexity in episode and remission phase of recurrent depression. International Journal of Methods in Psychiatric Research, 2020, 29(2): e1816.
28.	贾凤南, 汤浩, 刘晓雪, 等. 单双相抑郁障碍前额叶-纹状体通路gamma频段效能连接差异. 中国神经精神疾病杂志, 2020, 46(3): 149-153.
29.	Wan Z, Zhang H, Huang J, et al. Single-channel EEG-based machine learning method for prescreening major depressive disorder. International Journal of Information Technology & Decision Making, 2019, 18(5): 1579-1603.
30.	Cai H, Qu Z, Li Z, et al. Feature-level fusion approaches based on multimodal EEG data for depression recognition. Information Fusion, 2020, 59: 127-138.
31.	Newson J J, Thiagarajan T C. EEG frequency bands in psychiatric disorders: a review of resting state studies. Frontiers in Human Neuroscience, 2019, 12: 521.
32.	Vuust P, Heggli O A, Friston K J, et al. Music in the brain. Nature Reviews Neuroscience, 2022, 23(5): 287-305.
33.	Ding Y, Zhang Y, Zhou W, et al. Neural correlates of music listening and recall in the human brain. Journal of Neuroscience, 2019, 39(41): 8112-8123.
34.	Tsekoura K, Foka A. Classification of EEG signals produced by musical notes as stimuli. Expert Systems with Applications, 2020, 159: 113507.
35.	Deuel T A, Pampin J, Sundstrom J, et al. The encephalophone: a novel musical biofeedback device using conscious control of electroencephalogram (EEG). Frontiers in Human Neuroscience, 2017, 11: 213.
36.	Daly I, Williams D, Malik A, et al. Personalised, multi-modal, affective state detection for hybrid brain-computer music interfacing. IEEE Transactions on Affective Computing, 2018, 11(1): 111-124.
37.	Ehrlich S K, Agres K R, Guan C, et al. A closed-loop, music-based brain-computer interface for emotion mediation. PloS One, 2019, 14(3): e0213516.

1. Lu J, Xu X, Huang Y, et al. Prevalence of depressive disorders and treatment in China: a cross-sectional epidemiological study. Lancet Psychiatry, 2021, 8(11): 981-990.
2. World Health Organization (WHO). Depression. (2023-03-31)[2023-05-09]. https: //www.who.int/news-room/fact-sheets/detail/depression.
3. Hopwood M. Anxiety symptoms in patients with major depressive disorder: commentary on prevalence and clinical implications. Neurology and Therapy, 2023, 12(Suppl 1): 5-12.
4. Dev A, Roy N, Islam M K, et al. Exploration of EEG-based depression biomarkers identification techniques and their applications: A systematic review. IEEE Access, 2022, 10: 16756-16781.
5. Gao Z, Cui X, Wan W, et al. Signal quality investigation of a new wearable frontal lobe EEG device. Sensors (Basel), 2022, 22(5): 1898.
6. Sawangjai P, Hompoonsup S, Leelaarporn P, et al. Consumer grade EEG measuring sensors as research tools: a review. IEEE Sensors Journal, 2019, 20(8): 3996-4024.
7. Ruiz N A L, Del Ángel D S, Olguín H J, et al. Neuroprogression: the hidden mechanism of depression. Neuropsychiatric Disease and Treatment, 2018, 14: 2837-2845.
8. Schmaal L, Pozzi E, C Ho T, et al. ENIGMA MDD: seven years of global neuroimaging studies of major depression through worldwide data sharing. Translational psychiatry, 2020, 10(1): 172.
9. de Aguiar Neto F S, Rosa J L G. Depression biomarkers using non-invasive EEG: a review. Neuroscience & Biobehavioral Reviews, 2019, 105: 83-93.
10. Kappel S L, Rank M L, Toft H O, et al. Dry-contact electrode ear-EEG. IEEE Transactions on Biomedical Engineering, 2019, 66(1): 150-158.
11. Wan W, Cui X, Gao Z, et al. Frontal EEG-based multi-level attention states recognition using dynamical complexity and extreme gradient boosting. Frontiers in Human Neuroscience, 2021, 15: 673955.
12. Gao Z, Cui X, Wan W, et al. Long-range correlation analysis of high frequency prefrontal electroencephalogram oscillations for dynamic emotion recognition. Biomedical Signal Processing and Control, 2022, 72: 103291.
13. Heidlmayr K, Kihlstedt M, Isel F. A review on the electroencephalography markers of Stroop executive control processes. Brain and Cognition, 2020, 146: 105637.
14. Jiang X, Bian G B, Tian Z. Removal of artifacts from EEG signals: a review. Sensors (Basel), 2019, 19(5): 987.
15. Cong F. Blind source separation//Hu L, Zhang Z. EEG Signal Processing and Feature Extraction, Singapore: Springer, 2019: 117-140.
16. Stergiadis C, Kostaridou V D, Klados M A. Which BSS method separates better the EEG signals? A comparison of five different algorithms. Biomedical Signal Processing and Control, 2022, 72: 103292.
17. Cheng J, Li L, Li C, et al. Remove diverse artifacts simultaneously from a single-channel EEG based on SSA and ICA: a semi-simulated study. IEEE Access, 2019, 7: 60276-60289.
18. Kumaravel V P, Kartsch V, Benatti S, et al. Efficient artifact removal from low-density wearable EEG using artifacts subspace reconstruction//2021 43rd Annual International Conference of the IEEE Engineering in Medicine & Biology Society (EMBC). IEEE, 2021: 333-336.
19. Lin I M, Chen T C, Lin H Y, et al. Electroencephalogram patterns in patients comorbid with major depressive disorder and anxiety symptoms: proposing a hypothesis based on hypercortical arousal and not frontal or parietal alpha asymmetry. Journal of Affective Disorders, 2021, 282: 945-952.
20. Bachmann M, Päeske L, Kalev K, et al. Methods for classifying depression in single channel EEG using linear and nonlinear signal analysis. Computer Methods and Programs in Biomedicine, 2018, 155: 11-17.
21. 杜雪云, 王淑君, 张轩, 等. 慢性失眠伴焦虑抑郁患者认知功能障碍的γ振荡研究. 中华行为医学与脑科学杂志, 2020, 29(6): 523-528.
22. Lee P F, Kan D P X, Croarkin P, et al. Neurophysiological correlates of depressive symptoms in young adults: a quantitative EEG study. Journal of Clinical Neuroscience, 2018, 47: 315-322.
23. Koo P C, Berger C, Kronenberg G, et al. Combined cognitive, psychomotor and electrophysiological biomarkers in major depressive disorder. European Archives of Psychiatry and Clinical Neuroscience, 2019, 269(7): 823-832.
24. Knociková J A, Petrásek T. Quantitative electroencephalographic biomarkers behind major depressive disorder. Biomedical Signal Processing and Control, 2021, 68: 102596.
25. Ahmadlou M, Adeli H, Adeli A. Fractality analysis of frontal brain in major depressive disorder. International Journal of Psychophysiology, 2012, 85(2): 206-211.
26. Faust O, Ang P C A, Puthankattil S D, et al. Depression diagnosis support system based on EEG signal entropies. Journal of Mechanics in Medicine and Biology, 2014, 14(3): 1450035.
27. Čukić M, Stokić M, Radenković S, et al. Nonlinear analysis of EEG complexity in episode and remission phase of recurrent depression. International Journal of Methods in Psychiatric Research, 2020, 29(2): e1816.
28. 贾凤南, 汤浩, 刘晓雪, 等. 单双相抑郁障碍前额叶-纹状体通路gamma频段效能连接差异. 中国神经精神疾病杂志, 2020, 46(3): 149-153.
29. Wan Z, Zhang H, Huang J, et al. Single-channel EEG-based machine learning method for prescreening major depressive disorder. International Journal of Information Technology & Decision Making, 2019, 18(5): 1579-1603.
30. Cai H, Qu Z, Li Z, et al. Feature-level fusion approaches based on multimodal EEG data for depression recognition. Information Fusion, 2020, 59: 127-138.
31. Newson J J, Thiagarajan T C. EEG frequency bands in psychiatric disorders: a review of resting state studies. Frontiers in Human Neuroscience, 2019, 12: 521.
32. Vuust P, Heggli O A, Friston K J, et al. Music in the brain. Nature Reviews Neuroscience, 2022, 23(5): 287-305.
33. Ding Y, Zhang Y, Zhou W, et al. Neural correlates of music listening and recall in the human brain. Journal of Neuroscience, 2019, 39(41): 8112-8123.
34. Tsekoura K, Foka A. Classification of EEG signals produced by musical notes as stimuli. Expert Systems with Applications, 2020, 159: 113507.
35. Deuel T A, Pampin J, Sundstrom J, et al. The encephalophone: a novel musical biofeedback device using conscious control of electroencephalogram (EEG). Frontiers in Human Neuroscience, 2017, 11: 213.
36. Daly I, Williams D, Malik A, et al. Personalised, multi-modal, affective state detection for hybrid brain-computer music interfacing. IEEE Transactions on Affective Computing, 2018, 11(1): 111-124.
37. Ehrlich S K, Agres K R, Guan C, et al. A closed-loop, music-based brain-computer interface for emotion mediation. PloS One, 2019, 14(3): e0213516.

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