مروری بر تأثیر اعتیاد به مواد مخدر بر روی عملکرد و ساختار مغز بر مبنای تحلیل سیگنال‌های الکتروانسفالوگرام

عاطفه توبیها ¹ ( دانشکده مهندسی برق- واحد نجف‌آباد، دانشگاه آزاد اسلامی، نجف‌آباد، ایران )
ندا بهزادفر ² ( مرکز تحقیقات پردازش دیجیتال و بینایی ماشین- واحد نجف‌آباد، دانشگاه آزاد اسلامی، نجف‌آباد، ایران )
محمد رضا یوسفی ³ ( مرکز تحقیقات ریزشبکه‌های هوشمند- واحد نجف‌آباد، دانشگاه آزاد اسلامی، نجف‌آباد، ایران )
همایون مهدوی نسب ⁴ ( دانشکده مهندسی برق- واحد نجف‌آباد، دانشگاه آزاد اسلامی، نجف‌آباد، ایران )

تاریخ ارسال : 1402/02/22 تاریخ تایید : 1402/07/12

کلید واژه: اعتیاد, استخراج ویژگی, سیگنال الکتروانسفالوگرام, طیف قدرت,

چکیده مقاله :

اعتیاد به مواد مخدر سبب ایجاد تغییرات ساختاری و عملکردی در مغز انسان می گردد و می توان آن را به عنوان یک بیماری مزمن در نظر گرفت. تاکنون مطالعات مختلفی جهت بررسی اعتیاد بر روی سیگنال الکتروانسفالوگرام (EEG) انجام شده که از نظر روش، شرایط تجربی، نمونه ها و نتایج متفاوت هستند. بررسی مطالعات قبلی و روش های تجربی جهت بررسی بهتر مسائل و چالش های موجود در طراحی مطالعات اعتیاد ضروری است. سیگنال الکتروانسفالوگرام، به عنوان یک ابزار غیرتهاجمی دارای توانایی بالقوه جهت بررسی فعالیت عملکردی و شناختی مغز است. از الکتروانسفالوگرام می توان جهت بررسی ارتباط تغییرات ایجاد شده در مغز در اثر مصرف مواد مخدر استفاده کرد. در این مقاله به بررسی تغییرات ایجاد شده در سیگنال الکتروانسفالوگرام در اثر مصرف مواد و همچنین پس از ترک مواد مخدر اشاره خواهد شد. نتایج نشان می دهد که مصرف مواد مخدر سبب کاهش پردازش توجه و ایجاد اختلالات عملکردی و ناهنجاری های مغزی می گردد. در افراد معتاد افزایش فعالیت زیرباندهای بتا و دومین زیرباند آلفا، تأخیر در رخداد و کاهش دامنه P300 مشاهده شده است. همچنین نسبت توان زیرباند آلفا به تتا در T6 کاهش نشان داده و اختلاف معنا دار در زیرباند دلتا به زیرباند آلفا در نسبت توان مشاهده شده است. یافته ها نشان می دهند که ولع و سابقه مصرف مواد افراد بر توان سیگنال الکتروانسفالوگرام تأثیر می گذارد. فعالیت عصبی در زیرباند آلفای افرادی که اعتیاد را ترک کرده اند نیز به طور معنی داری در لوب پاریتال (BA3 و BA7)، لوب فرونتال (BA4 و BA6) و لوب لیمبیک (BA24) ضعیف تر است.

چکیده انگلیسی :

Drug addiction causes structural and functional changes in the human brain and can be considered a chronic disease. So far, various studies have been performed to examine addiction on the brain signal, which differs in terms of method, experimental conditions, samples, and results. A review of previous studies and experimental methods is necessary to better examine the issues and challenges in the design of addiction studies. The electroencephalogram (EEG) signal, as a non-invasive instrument, has the potential to monitor the functional and cognitive activity of the brain. EEG can be used to examine the relationship between changes in the brain caused by drug use. This article will review the changes in brain signals caused by substance use as well as after quitting drugs. The results show that the use of narcotic drugs reduces attention processing and causes functional disorders and brain abnormalities. In addicted people, an increase in the activity of the beta subunit and the second alpha subunit, a delay in the event, and a decrease in the amplitude of P300 have been observed. Also, the power ratio of alpha to theta subunit has decreased in T6 and a significant difference has been observed in the power ratio between delta subunit and alpha subunit. The findings showed that people's desire and history of drug consumption affect the power of the electroencephalogram signal. The neural activity in the sub-alpha band of people who quit an addiction is also significantly weaker in the parietal (BA3 and BA7), frontal (BA4 and BA6), and limbic. 

منابع و مأخذ:

[1] Y. Olsen, "What is addiction? History, terminology, and core concepts", Medical Clinics of North America, vol. 106, no. 1, pp. 1-12, Jan. 2022 (doi: 10.1016/j.mcna.2021.08.001).

[2] A. Sanna, L. Fattore, P. Badas, G. Corona, M. Diana, "The hypodopaminergic state ten years after: transcranial magnetic stimulation as a tool to test the dopamine hypothesis of drug addiction", Current Opinion in Pharmacology, vol. 56, pp. 61-67, Feb. 2021 (doi: 10.1016/j.coph.2020.11.001).

[3] A.P. Meysamie, B. Faramarzi, K.H. Naieni, "How addicts think about addiction and community problems? ", Tehran University Medical Journal, vol. 64, no. 5, pp.34-43, Jan. 2006.

[4] E.J. Hawkins, J.S. Baer, D.R. Kivlahan, "Concurrent monitoring of psychological distress and satisfaction measures as predictors of addiction treatment retention", Journal of Substance Abuse Treatment, vol. 35, no. 2, pp. 207-216, Sept. 2008 (doi: 10.1016/j.jsat.2007.10.001).

[5] M.Y. Melnikov, "The current evidence levels for biofeedback and neurofeedback interventions in treating depression: A narrative review", Neural Plast, Article Number: 8878857, Feb. 2012 (doi: 10.1155/202­1/887­8857).

[6] C. Sripada, "Impaired control in addiction involves cognitive distortions and unreliable self-control, not compulsive desires and overwhelmed self-control", Behavioural Brain Research, vol. 418, Article Number: 113639, Feb. 2021 (doi: 10.1016/j.bbr.2021.113639).

[7] N. Del Cacho, R. Vila-Badia, A. Butjosa, D. Cuadras, E. Rubio-Abadal, M.J. Rodriguez-Montes, D. Muñoz- Samons, M. Dolz, J. Usall, "Sexual dysfunction in drug-naïve first episode nonaffective psychosis patients. Relationship with prolactin and psychotic symptoms. Gender differences", Psychiatry Research, vol. 289, Article Number: 112985, July 2020 (doi: 10.1016/j.psychres.2020.112985).

[8] B. Hunt, D. Zarate, P. Gill, V. Stavropoulos, "Mapping the links between sexual addiction and gambling disorder: A Bayesian network approach", Psychiatry Research, vol. 327, Article Number: 115366, Sept. 2023 (doi: 10.1016/j.psychres.2023.115366).

[9] M. Camilleri, "New drugs on the horizon for functional and motility gastrointestinal disorders", Gastroent­ero­logy, vol. 161, no. 3, pp. 761-764, May 2021 (doi: 10.1053/j.gastro.2021.04.079).

[10] L. Keefer, C.W. Ko, A.C. Ford, "AGA clinical practice update on management of chronic gastrointestinal pain in disorders of gut–brain interaction: Expert review", Clinical Gastroenterology and Hepatology, vol. 19, no. 12, pp. 2481-2488.e1, Dec. 2021 (doi: 10.1016/j.cgh.2021.07.006).

[11] Y. Zhang, Y. Jia, M. Yang, P. Yang, Y. Tian, A. Xiao, A. Wen, "The impaired disposition of probe drugs is due to both liver and kidney dysfunctions in CCl4-model rats", Environmental Toxicology and Pharmacology, vol. 33, no. 3, pp. 453-458, May 2012 (doi: 10.1016/j.etap.2012.01.002).

[12] N. Agustanti, N.N.M. Soetedjo, F.A. Damara, M.R. Iryaningrum, H. Permana, M.B. Bestari, R. Supriyadi, "The association between metabolic dysfunction-associated fatty liver disease and chronic kidney disease: A systematic review and meta-analysis", Diabetes and Metabolic Syndrome: Clinical Research & Reviews, vol. 17, no. 5, Article Number: 102780, May 2023 (doi: 10.1016/j.dsx.2023.102780).

[13] S. Liang, K. Xue, W. Wang, W. Yu, X. Ma, S. Luo, J. Zhang, X. Sun, X. Luo, F. Liu, Y. Zhang, "Altered brain function and clinical features in patients with first-episode, drug naïve major depressive disorder: A resting-state fMRI study", Psychiatry Research: Neuroimaging, vol. 303, Article Number: 111134, Sept. 2020 (doi: 10.1016/j.pscychresns.2020.111134).

[14] A.B. Konova, S.J. Moeller, R.Z. Goldstein, "Common and distinct neural targets of treatment: Changing brain function in substance addiction", Neuroscience and Biobehavioral Reviews, vol. 37, no. 10, pp. 2806-2817, Dec. 2013 (doi: 10.1016/j.neubiorev.2013.10.002).

[15] E. Bellotti, A.L. Schilling, S.R. Little, P. Decuzzi, "Injectable thermoresponsive hydrogels as drug delivery system for the treatment of central nervous system disorders: A review", Journal of Controlled Release, vol. 329, pp. 16-35, Jan. 2021 (doi: 10.1016/j.jconrel.2020.11.049).

[16] M. Corominas-Roso, I. Ibern, M. Capdevila, R. Ramon, C. Roncero, J. Ramos-Quiroga, "Benefits of EEG-neurofeedback on the modulation of impulsivity in a sample of cocaine and heroin long-term abstinent inmates: A pilot study", International Journal of Offender Therapy and Comparative Criminology, vol. 64, no. 12, pp. 1275-1298. Sept. 2020 (doi: 10.1177/0306624X20904704).

[17] E. C. Warren and A. Kolodny, "Trends in heroin treatment admissions in the united states by race, sex, and age", JAMA Network Open, vol. 4, no. 2, pp. e2036640-e2036640, Feb. 2021 (doi: 10.1001/jamanetw­orko­pen.2­020.36640(.

[18] M. Cañedo, E. Moral, "Risky pleasures and drugged assemblages: Young people’s consumption practices of AOD in Madrid", International Journal of Drug Policy, vol. 49, pp. 102-108, Nov. 2017 (doi: 10.101­6/j.dr­ugpo.2017.08.002).

[19] K. Yuan, W. Qin, M. Dong, J. Liu, J. Sun, P. Liu, Y. Zhang, W. Wang, Y. Wang, Q. Li, L. ZhaoK.M. Deneen Y. Liu, M.S. Gold, J. Tian, "Gray matter deficits and resting-state abnormalities in abstinent heroin-dependent individuals", Neurosci Letters, vol. 482, no. 2, pp. 101-105, Sept. 2010 (doi: 10.1016/j.neule­t.2010.07.005).

[20] J. Kennett, S. Matthews, A. Snoek, "Pleasure and addiction", Frontiers in Psychiatry, vol. 4, Article Number: 117, Spet. 2013 (doi: 10.3389/fpsyt.2013.00117).

[21] B. Tamrazi, J. Almast, "Your brain on drugs: imaging of drug-related changes in the central nervous system", Radiographics, vol. 32, no. 3, pp. 701-719, May/June 2012 (doi: 10.1148/rg.323115115).

[22] J. Borne, R. Riascos, H. Cuellar, D. Vargas, R. Rojas, "Neuroimaging in drug and substance abuse part II: opioids and solvents", Topics in Magnetic Resonance Imaging, vol. 16, no. 3, pp. 239-45, June 2005 (doi: 10.1097/01.rmr.0000192154.34563.6b).

[23] A. Schmidt, M. Walter, H. Gerber, O. Schmid, R. Smieskova, K. Bendfeldt, G.A. Wiesbeck, A. Riecher-Rössler, U.E. Lang, K. Rubia, P. McGuire, S. Borgwardt, "Inferior frontal cortex modulation with an acute dose of heroin during cognitive control", Neuropsychopharmacology, vol. 38, no. 11, pp. 2231–2239, Oct. 2013 (doi: 10.1038/npp.2013.123).

[24] D. Roura-Martínez, P. Díaz-Bejarano, M. Ucha, R.R. Paiva, E. Ambrosio, A. Higuera-Matas, "Comparative analysis of the modulation of perineuronal nets in the prefrontal cortex of rats during protracted withdrawal from cocaine, heroin and sucrose self-administration", Neuropharmacology, vol. 180, Article Number: 108290, Dec. 2020 (doi: 10.1016/j.neuropharm.2020.108290).

[25] M. Dorvashi, N. Behzadfar, G. Shahgholian, "Classification of alcoholic and non-alcoholic individuals based on frequency and non-frequency features of electroencephalogram signal", Iranian Journal of Biomedical Engineering, vol. 14, no. 2, pp. 109-119,  July 2020 (doi: 10.1109/BHI.2012.6211580).

[26] D.D. Joshi, M. Puaud, M. Fouyssac, A. Belin-Rauscent, B. Everitt, D. Belin, "The anterior insular cortex in the rat exerts an inhibitory influence over the loss of control of heroin intake and subsequent propensity to relapse", European Journal of Neuroscience, vol. 52, no. 9, pp. 4115-4126, Nov. 2020 (doi: 10.1111/ejn.­148­89).

[27] J. Wei, L. Wang, J. Zhang, H. Wei, Y. Zhang, X. Cheng, Z. Li, F. Yang, Y. Zhu, "Quantitative susceptibility mapping for drug-addicted human brain", Proceeding of the IEEE/ICSP, pp. 1184-1188, Beijing, China, Aug. 2018, doi: 10.1109/ICSP.2018.8652449.

[28] S. Liu, S. Wang, M. Zhang, Y. Xu, Z. Shao, L. Chen, W. Yang, J. Liu, K. Yuan, "Brain responses to drug cues predict craving changes in abstinent heroin users: A preliminary study", NeuroImage, vol. 237, Article Number: 118169, Aug. 2021 (doi: 10.1016/j.neuroimage.2021.118169).

[29] M.D. Scofield, J.A. Heinsbroek, C.D. Gipson, Y.M. Kupchik, S. Spencer, A.C. Smith, D. Roberts-Wolfe, P.W. Kalivas, "The nucleus accumbens: Mechanisms of addiction across drug classes reflect the importance of glutamate homeostasis", Pharmacological Reviews, vol. 68, no. 3, pp. 816–871, July 2016 (doi: 10.1124/p­r.116­.012484).

[30] C.L. Seifert, S. Magon, T. Sprenger, U.E. Lang, C.G. Huber, N. Denier, M. Vogel, A. Schmidt, E.W. Radue, S. Borgwardt, M. Walter, "Reduced volume of the nucleus accumbens in heroin addiction", European Archives of Psychiatry and Clinical Neuroscience, vol. 265, no. 8, pp. 637-645, Dec. 2015 (doi: 10.10­07/s0­040­6-014-0564-y).

[31] M. Hassanpour-Ezatti, "Comparison of acute effects of heroin and Kerack on sensory and motor activity of honey bees (Apis mellifera) ", Iranian Journal of Basic Medical Sciences, vol. 18, no. 4, pp. 364–369, April 2015 (doi: 10.22038/ijbms.2015.4285).

[32] D. Perekopskiy, A. Afzal, S.N. Jackson, L. Muller, A.S. Woods, E.A. Kiyatkin, "The role of peripheral opioid receptors in triggering heroin-induced brain hypoxia", Scientific Reports, vol. 10, Article number: 833, Jan. 2020 (doi: 10.1038/s41598-020-57768-3).

[33] K. Hua, T. Wang, C. Li, S. Li, X. Ma, C. Li, M. Li, S. Fu, Y. Yin, Y. Wu, M. Liu, K. Yu, J. Fang, P. Wang, G. Jiang, "Abnormal degree centrality in chronic users of codeine-containing cough syrups: A resting-state functional magnetic resonance imaging study", NeuroImage: Clinical, vol. 19, pp. 775-781, 2018 (doi: 10.10­16/j­.nicl.2018.06.003).

[34] A.G. Polunina, D.M. Davydov, A.A. Kozlov, "Brain disintegration in heroin addicts: the natural course of the disease and the effects of methadone treatment", Heroin Addiction and Related Clinical Problems, vol. 9, no. 2, pp. 17-26, June 2007 (doi: 10.1007/s00213-003-1542-7(

[35] N. Behzadfar, H. Soltanian-Zadeh, "Automatic segmentation of brain tumors in magnetic resonance images", Proceedings of the IEEE/EMBS-BHI, pp. 329-332, Hong Kong, China, Jan. 2012 (doi: 10.1109/BHI.201­2.6211580).

[36] P.P. Lunardelo, M.T.H. Fukuda, P.A. Zuanetti, Â.C. Pontes-Fernandes, M.I. Ferretti, S. Zanchetta, "Cortical auditory evoked potentials with different acoustic stimuli: Evidence of differences and similarities in coding in auditory processing disorders", International Journal of Pediatric Otorhinolaryngology, vol. 151, Article Number: 110944, Sept. 2021 (doi: 10.1016/j.ijporl.2021.110944).

[37] N. Behzadfar, S.M.P. Firoozabadi, K. Badie, "Analysis of regularity in the EEG before/after working memory task", Proceeding of the IEEE/ICBME, pp. 1-5, Tehran, Iran, Nov./Dec. 2017 (doi: 10.1109/ICBM­E.2­0­17.8430260).

[38] N. Behzadfar, S.M.P. Firoozabadi, K. Badie, "Low-complexity discriminative feature selection from EEG before and after short-term memory task", Clinical EEG and Neuroscience, vol. 47, no. 4, pp. 291-297, Feb. 2016 (doi: 10.1177/1550059416633951).

[39] X. Kang, I.M.A. Agastya, D.O.D. Handayani, M.H. Kit, A.W.B.A. Rahman,"Electroencephalogram (EEG) dataset with porn addiction and healthy teenagers under rest and executive function task", Data in Brief, vol. 39, Article Number: 107467, Dec. 2021 (doi: 10.1016/j.dib.2021.107467).

[40] A. Mengad, J. Dirkaoui, M. Ertel, M. Chakkouch, F. Elomari, "The Contribution of Numerical EEG Analysis for the Study and Understanding of Addictions with Substances", International Journal of Advanced Computer Science and Applications, vol. 14, no. 5, pp. 326-333, June 2023 (doi: 10.14569/I­JA­CS­­A.202­3.0140534).

[41] N. Marvi, J. Haddadnia, M.R.F. Bordbar, "An automated drug dependence detection system based on EEG", Computers in Biology and Medicine, vol. 158, Article Number: 106853, May 2023 (doi: 10.1016/j.c­omp­biom­ed.2023.106853)

[42] L. Yang, Y. Du, W. Yang, J. Liu, "Machine learning with neuroimaging biomarkers: Application in the dia­g­nosis and prediction of drug addiction," Addiction Biology, vol. 28, no. 2, Article Number: e13267, Feb. 2023 (doi: 10.1111/adb.13267).

[43] E.M. Rad, M. Azarnoosh, M. Ghoshuni, M.M. Khalilzadeh, "Early detection of alzheimer’s disease with nonlinear features of eeg signal and mri images by convolutional neural network", International Clinical Neuroscience Journal, vol. 9, no. 1, Article Number: e20, Jan. 2022 (doi: 10.34172/icnj.2022.20).

[44] H. Hakkak, M.M.K. Zade, M. Azarnoosh, "Analyzing the impact of neuromarketing to promote brand image based on EEG signals", Journal of Biomedical Imaging and Bioengineering, vol. 3, no. 1, pp. 95-105, Jan. 2019.

[45] G.M. Rojas, C. Alvarez, C.E. Montoya, M. Iglesia-Vayá, J.E. Cisternas, M. Gálvez, "Study of resting-state functional connectivity networks using EEG electrodes position as seed", Frontiers in Neuroscience, vol. 12, Article Number: 235, April 2018 (doi: 10.3389/fnins.2018.00235).

[46] T. Mwata-Velu, J.G. Avina-Cervantes, J. Ruiz-Pinales, T.A. Garcia-Calva, E.A. González-Barbosa, J.B. Hurtado-Ramos, J.J. González-Barbosa, "Improving motor imagery EEG classification based on channel selection using a deep learning architecture”, Mathematics, vol. 10, no. 13,  Article Number: 2302, July 2022 (doi: 10.3390/math10132302).

[47] M.A. Vázquez, A. Maghsoudi, I.P. Mariño, "An interpretable machine learning method for the detection of schizophrenia using EEG signals", Frontiers in Systems Neuroscience, vol. 15, Article Number: 652662, May 2021 (doi: 10.3389/fnsys.2021.652662).

[48] A. Subasi, "EEG signal classification using wavelet feature extraction and a mixture of expert model", Expert Systems with Applications, vol. 32, no. 4, pp. 1084-1093, May 2007 (doi: 10.1016/j.esw­a.200­6.0­2.0­0­5).

[49] J. Wang, M. Wang, "Review of the emotional feature extraction and classification using EEG signals", Cognitive Robotics, vol. 1, pp. 29-40, Jam. 2021 (doi: 10.1016/j.cogr.2021.04.001).

[50] A. Al-Saegh, S.A. Dawwd, J.M. Abdul-Jabbar, "Deep learning for motor imagery EEG-based classification: A review", Biomedical Signal Processing and Control, vol. 63, Article Number: 102172, Jan. 2021 (doi: 10.101­6/j.bspc.2020.102172).

[51] M.H.Y. Long, E.H.L. Lim, G.A. Balanza, J.C. Allen, P.L. Purdon, C.L. Bong, "Sevoflurane requirements during electroencephalogram (EEG)-guided vs standard anesthesia care in children: A randomized controlled trial", Journal of Clinical Anesthesia, vol. 81, Article Number: 110913, Oct. 2022 (doi: 10.1016/j.jcli­nan­e.20­2­2.110913).

[52] N. Dashti, M, Khezri, "Recognition of motor imagery based on dynamic features of EEG signals", Journal of Intelligent Procedures in Electrical Technology, vol. 11, no. 43, 13-27, Dec. 2020 (dor: 20.1001.1.23223871­.139­9.11.43.2.5).

[53] M. Dorvashi, N. Behzadfar, G. Shahgholian, "An efficient method for classification of alcoholic and normal electroencephalogram signals based on selection of an appropriate feature", Journal of Medical Signals & Sensors, vol.13, no. 1, pp. 11-20, March 2023 (doi: 10.4103/jmss.jmss_183_21).

[54] H. Ullah, S. Mahmud, R. H. Chowhury, “Identification of brain disorders by sub-band decomposition of EEG signals and measurement of signal to noise ratio”, Indonesian Journal of Electrical Engineering and Computer Science, vol. 4, no. 3, pp. 568- 579, Dec. 2016 (doi: 10.11591/ijeecs.v4.i3.pp568-579).

[55] T.F. Zaidi, O. Farooq, "EEG sub-bands based sleep stages classification using Fourier Synchros­queezed transform features", Expert Systems with Applications, vol. 212, Article Number: 118752, Feb. 2023 (doi: 10.1016/j.eswa.2022.118752).

[56] Z. Fodor, A. Horváth, Z. Hidasi, A.A. Gouw, C.J. Stam, G. Csukly, “EEG Alpha and Beta Band Functional Connectivity and Network Structure Mark Hub Overload in Mild Cognitive Impairment During Memory Maintenance", Frontiers in Aging Neuroscience, vol. 13, Article Number: 680200, Oct. 2021 (doi: 10.338­9/fn­agi.2021.680200).

[57] H.U. Amin, W. Mumtaz, A.R. Subhani, M.N.M. Saad, A.S. Malik, "Classification of EEG signals based on pattern recognition approach", Frontiers in Computational Neuroscience, vol. 11, Article Number: 103, Nov. 2017, doi: 10.3389/fncom.2017.00103.

[58] M. Abo-Zahhad, S. M. Ahmed, S. N. A. Seha, "A new EEG acquisition protocol for biometric identification using eye blinking signals”, International Journal of Intelligent Systems Technologies and Applications, vol. 7, no. 6, pp. 48-54, May 2015 (doi: 10.5815/ijisa.2015.06.05).

[59] A. Miljevic, N.W. Bailey, O.W. Murphy, M.P.N. Perera, P.B. Fitzgerald, "Alterations in EEG functional connectivity in individuals with depression: A systematic review", Journal of Affective Disorders, vol. 328, pp. 287-302, May 2023 (doi: 10.1016/j.jad.2023.01.126).

[60] S.M. Nacy, S.N. Kbah, H.A. Jafer, I. Al-Shaalan, "Controlling a servo motor using EEG signals from the primary motor cortex", American Journal of Biomedical Engineering, vol. 6, no. 5, pp. 139-146, May 2016 (doi:10.5923/j.ajbe.20160605.02).

[61] B.M.S. Inguscio, G. Cartocci, E. Modica, D. Rossi, A.C. Martinez-Levy, P. Cherubino, L. Tamborra, F. Babiloni, "Smoke signals: A study of the neurophysiological reaction of smokers and non-smokers to smoking cues inserted into antismoking public service announcements", International Journal of Psychophysiology, vol. 167, pp. 22-29, Dec. 2021 (doi: 10.1016/j.ijpsycho.2021.06.010).

[62] A.K. Jaiswal, H. Banka, "Local pattern transformation based feature extraction techniques for classification of epileptic EEG signals", Biomedical Signal Processing and Control, vol. 34, pp. 81-92, April 2017 (doi: 10.1016/j.bspc.2017.01.005).

[63] M.A. Herman, M. Roberto, "The Addicted Brain: understanding the neurophysiological mechanisms of addictive disorders", Frontiers in Integrative Neuroscience, vol. 9, no. 45, Article Number: 18, March 2015 (doi: 10.3389/fnint.2015.00018).

[64] A.H. Meghdadi, C. Berka, C. Richard, G. Rupp, S. Smith, M.S. Karić, K. McShea, E. Sones, K. Marinković, T. Marcotte, "EEG event related potentials in sustained, focused and divided attention tasks: Potential biomarkers for cognitive impairment in HIV patients", Clinical Neurophysiology, vol. 132, no. 2, pp. 598-611, Feb. 2021 (doi: 10.1016/j.clinph.2020.11.026).

[65] C. Kamarajan, B. Porjesz, "Advances in electrophysiological research", Alcohol Research, vol. 37, no. 1, pp. 53–87, 2015.

[66] Y. Wang, Z. Fan, M. Wang, J. Liu, S. Xu, Z. Lu, H. Wang, Y. Song, Y. Wang, L. Qu, Y. Li, X. Cai, "Research on the specificity of electrophysiological signals of human acupoints based on the 90-day simulated weightlessness experiment on the ground", IEEE Trans. on Neural Systems and Rehabilitation Engineering, vol. 29, pp. 2164-2172, Oct. 2021 (doi: 10.1109/TNSRE.2021.3120756).

[67] A. Mirifar, J. Beckmann, F. Ehrlenspiel, "Neurofeedback as supplementary training for optimizing athletes’ performance: A systematic review with implications for future research", Neuroscience and Biob­eha­vioral Reviews, vol. 75, pp. 419-432, April 2017 (doi: 10.1016/j.neubiorev.2017.02.005).

[68] H. Wang, J. Liu , Z. Lu, S. Xu,  J. Xie,Y. Dai, G. Xiao,Y. Song, Y. Zhang, L. Qu,X. Cai, "Effects of long-term and acute hindlimb unloading model on neuroelectrophysiological signals of hippocampal interneurons and pyramidal cells using microelectrode arrays", IEEE Access, vol. 8, pp. 198822-198831, Oct. 2020 (doi: 10.1109/ACCESS.2020.3034959).

[69] B. Zou, Y. Liu, M. Guo, Y. Wang, "EEG -based assessment of stereoscopic 3D visual fatigue caused by vergence-accommodation conflict", Journal of Display Technology, vol. 11, no. 12, pp. 1076-1083, Dec. 2015 (doi: 10.1109/JDT.2015.2451087).

[70] M.G. Doborjeh, G.Y. Wang, N.K. Kasabov, R. Kydd, B. Russell, "A spiking neural network methodology and system for learning and comparative analysis of EEG data from healthy versus addiction treated versus addiction not treated subjects", IEEE Trans. on Biomedical Engineering, vol. 63, no. 9, pp. 1830-1841, Sept. 2016 (doi: 10.1109/TBME.2015.2503400).

[71] S. Karimi-Shahraki, M. Khezri, "Identification of attention deficit Hyperactivity disorder patients using wavelet-based features of EEG signals", Journal of Intelligent Procedures in Electrical Technology, vol. 12, no. 47, pp. 1-11, Dec. 2021 (dor: 20.1001.1.23223871.1400.12.3.1.1).

[72] S.B. Emami, N. Nourafza, S. Fekri–Ershad, "A method for diagnosing of Alzheimer's disease using the brain emotional learning algorithm and wavelet feature", Journal of Intelligent Procedures in Electrical Technology, vol. 13, no. 52, pp. 65-78, March 2023 (for: 20.1001.1.23223871.1401.13.52.5.0);

[73] X. Kang, D.O.D. Handayani, P.P. Chong, U.R. Acharya, "Profiling of pornography addiction among children using EEG signals: A systematic literature review", Computers in Biology and Medicine, vol. 125, Article Number: 103970, Oct. 2020 (doi: 10.1016/j.compbiomed.2020.103970).

[74] A. Kermanshahian, M. Khezri, "Evaluation of deep neural networks in emotion recognition using electroencephalography signal patterns", Journal of Intelligent Procedures in Electrical Technolo­gy, vol. 16, no. 64, pp. 31-46, March 2026.

[75] M. Seif, M.R. Yousefi, N. Behzadfar, "EEG spectral power analysis: A comparison between heroin dependent and control groups", Clinical EEG and Neuroscience, vol. 53, no. 4, Article Number: 15500594221089366, March 2022 (doi: 10.1177/15500594221089366).

[76] M. Dorvashi, N. Behzadfar, G. Shahgholian, "Detection of fatigue from electroencephalogram signal during neurofeedback training", Signal and Data Processing, vol. 19, no. 3, pp. 163-174, Dec. 2022 (doi: 10.5254­7/jsdp.19.3.163).

[77] W.A.W Azlan, Y.F. Low, "Feature extraction of electroencephalogram (EEG) signal- A review", Proce­edi­n­g of the IEEE/IECBES, pp. 801-806, Kuala Lumpur, Malaysia, Dec. 2014 (doi: 10.1109/IEC­BE­S.2­01­4.7047620).

[78] M. Bardeci, C.T. Ip, S. Olbrich, "Deep learning applied to electroencephalogram data in mental disorders: A systematic review", Biological Psychology, vol. 162, Article Number: 108117, May 2021 (doi: 10.1016/j.b­iop­sycho.2021.108117).

[79] E. Huang, X. Zheng, Y. Fang, Z. Zhang, "Classification of motor imagery EEG based on time-domain and frequency-domain dual-stream convolutional neural network", Innovation and Research in BioMedical engineering, vol. 43, no. 2, pp. 107-113, April 2022 (doi: 10.1016/j.irbm.2021.04.004).

[80] C. Liu, Y. Fu, J. Yang, X. Xiong, H. Sun, Z. Yu, "Discrimination of motor imagery patterns by electroencephalogram phase synchronization combined with frequency band energy", IEEE/CAA Journal of Automatica Sinica, vol. 4, no. 3, pp. 551-557, 2017 (doi: 10.1109/JAS.2016.7510121).

[81] M. Mazher, A.A. Aziz, A.S. Malik, "Evaluation of rehearsal effects of multimedia content based on EEG using machine learning algorithms", Proceeding of the IEEE/ICIAS, pp. 1-6, Kuala Lumpur, Malaysia, Aug. 2016 (doi: 10.1109/ICIAS.2016.7824134).

[82].N. Anantharaman, A.K. Krishnamurthy, L.L. Feth, "Intensity-weighted average of instantaneous frequency as a model for frequency discrimination",  Journal of the Acoustical Society of America, vol. 94, pp. 723-729, Aug. 1993 (doi: 10.1121/1.406889. PMID: 8370877).

[83] B.R. Greene, S. Faul, W.P. Marnane, G. Lightbody, I. Korotchikova, G.B. Boylan, "A comparison of quantitative EEG features for neonatal seizure detection", Clinical Neurophysiology, vol. 119, no. 6, pp. 1248-1261, June 2008 (doi: 10.1016/j.clinph.2008.02.001).

[84] S.A. Imtiaz, S. Saremi-Yarahmadi, E. Rodriguez-Villegas, "Automatic detection of sleep spindles using Teager energy and spectral edge frequency", Proceeding of the IEEE/BioCAS, pp. 262-265, Rotterdam, Netherlands, Oct./Nov. 2013 (doi: 10.1109/BioCAS.2013.6679689).

[85] K. Kobayashi, N. Mimaki, F. Endoh, T. Inoue, H. Yoshinaga, Y. Ohtsuka, "Amplitude-integrated EEG colored according to spectral edge frequency", Epilepsy Research, vol. 96, no. 3, pp. 276-282, Oct. 2011 (doi: 10.1016/j.eplepsyres.2011.06.012).

[86] Y. Mi, A. Lin, D. Gu, X. Zhang, X. Huang, "Bubble transfer spectral entropy and its application in epilepsy EEG analysis", Communications in Nonlinear Science and Numerical Simulation, vol. 110, Article Number: 106294, July 2022 (doi: 10.1016/j.cnsns.2022.106294).

[87] H. Helakari, J. Kananen, N. Huotari, L. Raitamaa, T. Tuovinen, V. Borchardt, A. Rasila, V. Raatikainen, T. Starck, T. Hautaniemi, T. Myllylä, O. Tervonen, S. Rytky, T. Keinänen, V. Korhonen, V. Kiviniemi, H. Ansakorpi, "Spectral entropy indicates electrophysiological and hemodynamic changes in drug-resistant epilepsy – A multimodal MREG study", NeuroImage: Clinical, vol. 22, Article Number: 101763, 2019 (doi: 10.1016/j.nicl.2019.101763).

[88] A. Buccellato, D. Zang, F. Zilio, J. Gomez-Pilar, Z. Wang, Z. Qi, R. Zheng, Z. Xu, X. Wu, P. Bisiacchi, A.D. Felice, Y. Mao, G. Northoff, "Disrupted relationship between intrinsic neural timescales and alpha peak frequency during unconscious states- A high-density EEG study", NeuroImage, vol. 265, Artoclr Number: 119802, Jan. 2023 (doi: 10.1016/j.neuroimage.2022.119802).

[89] P. Boonyakitanont, A. Lek-uthai, K. Chomtho, J. Songsiri, "A review of feature extraction and performance evaluation in epileptic seizure detection using EEG", Biomedical Signal Processing and Control, vol. 57, Article Number: 101702, March 2020 (doi: 10.1016/j.bspc.2019.101702).

[90] A.K. Singh, S. Krishnan, "Trends in EEG signal feature extraction applications", Frontiers in Artificial Intelligence, vol. 5, Article Number: 1072801, Jan. 2023 (doi: 10.3389/frai.2022.1072801).

[91] D.P. Wulandari, N.G.P. Putriz, Y.K. Suprapto, S.W. Purnami, A.I. Juniani, W.R. Islamiyah, "Epileptic Seizure Detection Based on Bandwidth Features of EEG Signals", Procedia Computer Science, vol. 161, pp. 568-576, 2019 (doi: 10.1016/j.procs.2019.11.157).

[92] J. Zamani, A.B. Naieni. "Best feature extraction and classification algorithms for EEG signals in neur­oma­rketing", Frontiers Biomed Technol, vol. 7, no. 3, pp. 186-191, Dec. 2020 (doi: 10.18502/f­bt.­v7­i3­.4­621).

[93] X. Qin, Y. Zheng, B. Chen, "Extract EEG features by combining power spectral density and correntropy spectral density", Proceeding of the IEEE/CAC, pp. 2455-2459, Hangzhou, China, Nov. 2019 (doi: 10.110­9/CAC­48633.2019.8996873).

[94] A. Bhattacharyya, R.K. Tripathy, L. Garg, R.B. Pachori, "A novel multivariate-multiscale approach for computing EEG spectral and temporal complexity for human emotion recognition", IEEE Sensors Journal, vol. 21, no. 3, pp. 3579-3591, Feb. 2021 (doi: 10.1109/JSEN.2020.3027181).

[95] I.M. Colrain, S. Turlington, F.C. Baker, “Impact of alcoholism on sleep architecture and EEG power spectra in men and women”, Sleep, vol. 32, no. 10, pp. 1341–1352, Oct. 2009 (doi: 10.1093/sleep/3­2.10­.1341).

[96] K. Wang, Y.L. Zhao, S.P. Tan, J.G. Zhang, D. Li, J.X. Chen, L.G. Zhang, X.Y. Yu, D. Zhao, E.F.C. Cheung, B.I Turetsky, R.C. Gur, R.C.K. Chan, "Semantic processing event‐related potential features in patients with schizophrenia and bipolar disorder", PsyCh Journal, vol. 9, no. 2, pp. 247-257, April 2020        (doi: 10.1002/pchj.321).

[97] T. Zeng, S. Li2, L. Wu, Z. Feng, X. Fan, J. Yuan, X. Wang, J. Meng, H. Ma, G. Zeng, C. Kang, J. Yang. "A comparison study of impulsiveness, cognitive function, and P300 components between gamma-hydroxybutyrate and heroin-addicted patients: Preliminary findings", Frontiers in Human Neuroscience, vol. 16, Article Number: 835922, April 2022 (doi: 10.3389/fnhum.2022.835922).

[98] N. Accornero, M. Capozza, L. Pieroni, S. Pro, L. Davì, O. Mecarelli, "EEG mean frequency changes in healthy subjects during prefrontal transcranial direct current stimulation", Journal of Neurophysiology, vol. 112, no. 6, pp. 1367-1375, June 2014 (doi: 10.1152/jn.00088.2014).

[99] P. Zarjam, J. Epps, F. Chen, "Spectral EEG featuresfor evaluating cognitive load", Proceeding of the IEEE/IEMBS, pp. 3841-3844, Boston, MA, USA, Aug. 2011 (doi: 10.1109/IEMBS.2011.6090954).

[100] U.R. Acharya, S.V. Sree, G. Swapna, R.J. Martis, J.S. Suri, "Automated EEG analysis of epilepsy: A review", Knowledge-Based Systems, vol. 45, pp. 147-165, June 2013 (doi: 10.1016/j.kn­osys.20­13.02.014).

[101] V.S. Marks, K.V. Saboo, Ç. Topçu, M. Lech, T.P. Thayib, P. Nejedly, V. Kremen, G.A. Worrell, M.T. Kucewicz, "Independent dynamics of low, intermediate, and high frequency spectral intracranial EEG activities during human memory formation", NeuroImage, vol. 245, Article Number: 118637, Dec. 2021     (doi: 10.1016/j.neuroimage.2021.118637).

[102] U.R. Acharya, H. Fujita, V.K. Sudarshan, S. Bhat, J.E.W. Koh, "Application of entropies for automated diagnosis of epilepsy using EEG signals: A review", Knowledge-Based Systems, Vol. 88, pp. 85-96, Nov. 2015 (doi: 10.1016/j.knosys.2015.08.004).

[103] J.P. Zöllner, A. Strzelczyk, F. Rosenow, R. Kienitz, "Valproate but not levetiracetam slows the EEG alpha peak frequency- A pharmaco-EEG study", Clinical Neurophysiology, vol. 132, no. 6, pp. 1203-1208, June 2021 (doi: 10.1016/j.clinph.2021.02.392).

[104] S. Motamedi-Fakhr, M. Moshrefi-Torbati, M. Hill, C.M. Hill, P.R. White, "Signal processing techniques applied to human sleep EEG signals- A review", Biomedical Signal Processing and Control, vol. 10, pp. 21-33, March 2014 (doi: 10.1016/j.bspc.2013.12.003).

[105] Y. Kan, H. Duan, Y. Bo, Y. Wang, H. Yan, J. Lan, "The effect of acute stress on spatial selectivity in dual-stream emotion induced blindness: The role of cortisol and spontaneous frontal EEG theta/beta ratio", International Journal of Psychophysiology, vol. 183, pp. 71-80, Jan. 2023 (doi: 10.1016/j.ij­psy­ch­o.2022.11.014).

[106] S. Lashkari, M. Azarnoosh, "Optimal feature space selection in detecting epileptic seizure based on recurrent quantification analysis and genetic algorithm", Journal of Intelligent Procedures in Electrical Technology, vol. 7, no. 26, pp. 35-44, July 2016 (dor: 20.1001.1.23223871.1395.7.26.4.5).

[107] C. Dell'Acqua, S. Ghiasi, S. M. Benvenuti, A. Greco, C. Gentili, G. Valenza, "Increased functional connectivity within alpha and theta frequency bands in dysphoria: A resting-state EEG study", Journal of Affective Disorders, vol. 281, pp. 199-207, Feb. 2021 (doi: 10.1016/j.jad.2020.12.015).

[108] X. Xi, S. Pi, Y.  Zhao, H.  Wang, Z. Luo, "Effect of muscle fatigue on the cortical-muscle network: A comb­i­ned electroencephalogram and electromyogram study ", Brain Research, vol. 1752, Article Number: 147221, Feb. 2021 (doi: 10.1016/j.brainres.2020.147221).

[109] D. Koshiyama, K. Kirihara, K. Usui, M. Tada, M. Fujioka, S. Morita, S. Kawakami, M. Yamagishi, H. Sakurada, E. Sakakibara, Y. Satomura, N. Okada, S. Kondo, T. Araki, S. Jinde, K. Kasai, "Resting-state EEG beta band power predicts quality of life outcomes in patients with depressive disorders: A longitudinal investigation", Journal of Affective Disorders, vol. 265, pp. 416-422, 2020 (doi: 10.1016/j.jad.2020.01.030).

[110] N. Khanahmadi, M.R. Yousefi, "Prediction of success in neurofeedback treatment for attention-deficit hyperactivity disorder before starting treatmentgh", Journal of Intelligent Procedures in Electrical Technology, vol. 16, no. 63, pp. 39-60, Dec. 2025

[111] N. Behzadfar, “A brief overview on analysis and feature extraction of electroencephalogram signals”, Signal Processing and Renewable Energy, vol. 6, no. 1, pp. 39-64, March 2022 (dor: 20.1001.1.25­887­327.2022.6.1.3.9).

[112] M. Corominas-Roso, I. Ibern, M. Capdevila, R. Ramon, C. Roncero, J. Ramos-Quiroga, "Benefits of EEG -neurofeedback on the modulation of impulsivity in a sample of cocaine and heroin long-term abstinent inmates: A pilot study", International Journal of Offender Therapy and Comparative Criminology, vol. 64, no. 12, pp. 1275-1298. Sept. 2020 (doi: 10.1177/0306624X20904704).

[113] F. Motlagh, F. Ibrahim, J.M. Menke, R. Rashid, T. Seghatoleslam, H. Habil, "Neuroelect­roph­ys­iological approaches in heroin addiction research: A review of literatures", Journal of Neuroscience Research, vol. 94, pp. 297–309, Jan. 2016 (doi: 10.1002/jnr.23703).

[114] M. Heilig, J. MacKillop, D. Martinez, J. Rehm, L. Leggio, L.J. Vanderschuren, "Addiction as a brain disease revised: why it still matters, and the need for consilience", Neuropsychopharmacology, vol. 46, pp. 1715-1723, Feb. 2021 (doi: 10.1038/s41386-020-00950-y)

[115] N.D. Volkow, J.S. Fowler, G.J. Wang, "The addicted human brain: insights from imaging studies", Journal of Clinical Investigation, vol. 111, no. 10, pp. 1444-1451, May 2003 (doi: 10.1172/JCI18533).

[116] N.D. Volkow, J.S. Fowler, G.J. Wang, "The addicted human brain viewed in the light of imaging studies: brain circuits and treatment strategies", Neuropharmacology, vol. 47, pp. 3-13, 2004 (doi: 10.1016/j.neuro­pha­rm.2004.07.019).

[117] X. Ding, X. Li, M. Xu, Z. He, H. Jiang, "The effect of repetitive transcranial magnetic stimulation on electroencephalography microstates of patients with heroin-addiction", Psychiatry Research: Neuroimaging, vol. 329, Article Number: 111594, March 2023 (doi: 10.1016/j.pscychresns.2023.111594).

[118] T.S. Bel-Bahar, A.A. Khan, R.B. Shaik, M.A. Parvaz, "A scoping review of electroencephalographic (EEG) markers for tracking neurophysiological changes and predicting outcomes in substance use disorder treatment", Frontiers in Human Neuroscience, vol. 16, pp.  1-31, Oct. 2022 (doi: 10.3389/fnhu­m.202­2.99­55­3­4).

[119] A. Tobeiha, N. Behzadfar, M.R. Yousefi-Najafabadi, H. Mahdavi-Nasab, G. Shahgholian, "Choosing the distinguishing frequency feature of people addicted to heroin from healthy while resting", Signal and Data Processing, vol. 19, no. 3, pp. 49-64, Dec. 2022 (doi: 10.52547/jsdp.19.3.49).

[120] C.C. Papageorgiou, I.A. Liappas, E.M. Ventouras, C.C. Nikolaou, E.N. Kitsonas, N.K. Uzunoglu, A.D. Rabavilas, "Long-term abstinence syndrome in heroin addicts: indices of P300 alterations associated with a short memory task", Progress in Neuro-Psychopharmacology and Biological Psychiatry, vol. 28, no. 7, pp. 1109-1115, Nov. 2004 (doi: 10.1016/j.pnpbp.2004.05.049).

[121] N. Ma,Y. Liu, X.M. Fu, N. Li, C.X. Wang, H. Zhang, R.B. Qian, H.S. Xu, X. Hu, D.R. Zhang, "Abnormal brain default-mode network functional connectivity in drug addicts", PloS One, vol. 6, no. 1, Article Number: e16560, Jan. 2011(doi: 10.1371/journal.pone.0016560).

[122] E.M. Kouri, S.E. Lukas, J.H. Mendelson, "P300 assessment of opiate and cocaine users: Effects of detoxi­fication and buprenorphine treatment", Biological Psychiatry, vol. 40, no. 7, pp. 617-628, Oct. 1996 (doi: 10.1016/0006-3223(95)00468-8).

[123] G.Y. Wang, R. Kydd, T.A. Wouldes, M. Jensen, B.R. Russell, "Changes in resting EEG following methadone treatment in opiate addicts", Clinical Neurophysiology, vol. 126, no. 5, pp. 943-950, May 2015 (doi: 10.101­6/j.­clinph.2014.08.021).

[124] Y. Liu, Y. Chen, G. Fraga-González, V. Szpak, J. Laverman, R.W. Wiers, K.R. Ridderinkhof, "Resting-state EEG, substance use and abstinence after chronic use: A systematic review", Clinical EEG and Neuroscience, vol. 53, no. 4, pp. 344-366, July 2022 (doi: 10.1177/15500594221076347).

[125] N. Shourie, M. Firoozabadi, and K. Badie, "Neurofeedback training protocols based on spectral EEG feature subset and channel selection for performance enhancement of novice visual artists", Biomedical Signal Processing and Control, vol. 43, pp. 117-129, May 2018 (doi: 10.1016/j.bsp­c.201­8.02­.017)

[126] E. Shufman, E. Perl, M. Cohen, M. Dickman, D. Gandaku, D. Adler, A. Veler, R. Baramburger, Y. Ginath, "Electro-encephalography spectral analysis of heroin addicts compared with abstainers and normal controls", The Israel journal of psychiatry and related sciences, vol. 33, no. 3, pp. 196-206, 1996.

[127] I.H. Franken, C.J. Stam, V.M. Hendriks, W. Brink, "Electroencephalographic power and coherence analyses suggest altered brain function in abstinent male heroin-dependent patients", Neuropsychobiology, vol. 49, no. 2, pp. 105-110, 2004 (doi: 10.1159/000076419).

[128] J. Luo, R.Yang, W. Yang, C. Duan, Y. Deng, J. Zhang, J. Chen, J. Liu, "Increased amplitude of low-frequency fluctuation in right angular gyrus and left superior occipital gyrus negatively correlated with heroin use", Frontiers in Psychiatry, vol. 11, Article Number: 492, July 2020 (doi: 10.3389/fpsyt.2­020.00492)

[129] D.M. Davydov, A.G. Polunina, "Heroin abusers' performance on the tower of london test relates to the baseline EEG alpha2 mean frequency shifts", Progress in Neuro-Psychopharmacology and Biological Psychiatry, vol. 28, no. 7, pp. 1143-1152, Nov. 2004 (doi: 10.1016/j.pnpbp.2004.06.006).

[130] A.G. Polunina, D.M. Davydov, "EEG spectral power and mean frequencies in early heroin abstine­nce", Progress in Neuro-Psychopharmacology and Biological Psychiatry, vol. 28, no. 1, pp. 73-82, Jan. 2004 (doi: 10.1016/j.pnpbp.2003.09.022).

[131] K. Jurewicz, K. Paluch, E. Kublik, J. Rogala, M. Mikicin, A. Wróbel, "EEG -neurofeedback training of beta band (12–22 Hz) affects alpha and beta frequencies– A controlled study of a healthy population", Neuropsychologia, vol. 108, pp. 13-24, Jan. 2018 (doi: 10.1016/j.neuropsycholo­gia.2017.11.0­2­1­).

[132] I.H. Franken, C.J. Stam, V.M. Hendriks, W.V. Brink, "Neurophysiological evidence for abnormal cognitive processing of drug cues in heroin dependence", Psychopharmacology, vol. 170, no. 2, pp. 205-212, July 2003 (doi: 10.1007/s00213-003-1542-7).

[133] B. Hu, Q. Dong, Y. Hao, Q. Zhao, J. Shen, F. Zheng, "Effective brain network analysis with resting-state EEG data: A comparison between heroin abstinent and non-addicted subjects", Journal of Neural Engineering, vol. 14, no. 4, Article Number: 046002, Aug. 2017 (doi: 10.1088/1741-2552/aa6c6f).

[134] T.M. Sokhadze, R.L. Cannon, D.L. Trudeau, "EEG biofeedback as a treatment for substance use disorders: review, rating of efficacy and recommendations for further research", Journal of Neurotherapy, vol. 12, no. 1, pp. 1-28, Jan. 2008 (doi: 10.1007/s10484-007-9047-5).

[135] A Turnip, K. Esti, M Faizal Amri, A.I. Simbolon, M.A. Suhendra, S. IsKandarand, F.F. Wirakusumah,  "Det­e­ction of drug effects on brain activity using EEG-P300 with similar stimuli", IOP Conference Series: Materials Science and Engineering, vol. 220, no. 1, pp. 1-12 , 2017 (doi: 10.1088/1757-899X/220/1/012042).

[136] F. Motlagh, F. Ibrahim, R. Rashid, T. Seghatoleslam, H. Habil, "Investigation of brain electrophysiological properties among heroin addicts: Quantitative EEG and event-related potentials", Journal of Neuroscience Research, vol. 95, no. 7, pp. 1633-1646, Aug. 2017 (doi: 10.1002/jnr.23988).

[137] P.P. Lunardelo, M.T.H. Fukuda, P.A. Zuanetti, Â.C. Pontes-Fernandes, M.I. Ferretti, S. Zanchetta, "Cortical auditory evoked potentials with different acoustic stimuli: Evidence of differences and similarities in coding in auditory processing disorders", International Journal of Pediatric Otorhinolaryngology, vol. 151, Article Number: 110944, Sept. 2021 (doi: 10.1016/j.ijporl.2021.110944).

[138] K. Race,"Thinking with pleasure: Experimenting with drugs and drug research", International Journal of Drug Policy, vol. 49, pp. 144-149, Nov. 2017 (doi: 10.1016/j.drugpo.2017.07.019).

[139] T.M. Lee, W. Zhou, X. Luo, K.S. Yuen, X. Ruan, X. Weng, "Neural activity associated with cognitive regulation in heroin users: a fMRI study", Neuroscience Letters, vol. 382, no. 3, pp. 211-216, July 2005 (doi: 10.101­6/j.neulet.2005.03.053).

[140] F. Motlagh, F. Ibrahim, R. Rashid, N. Shafiabady, T. Seghatoleslam, H. Habil, "Acute effects of methadone on EEG power spectrum and event-related potentials among heroin dependents", Psychopharmacology, vol. 235, pp. 3273-3288, Nov. 2018 (doi: 10.1007/s00213-018-5035-0).

[141] J. Wang, R. Peng, Q. Liu, H. Peng, "A hybrid classification to detect abstinent heroin-addicted individuals using EEG microstates", IEEE Trans. on Computational Social Systems, vol. 9, no. 3, pp. 700-709, June 2022 (doi: 10.1109/TCSS.2021.3135425).