Analysis of EEG signals and data acquisition methods: a review

References

• Abd El-Samie FE, Alotaiby TN, Khalid MI, Alshebeili SA, Aldosari SA. 2018. A review of EEG and MEG epileptic spike detection algorithms. IEEE Acces. 6:60673–26. doi: 10.1109/ACCESS.2018.2875487.
   Web of Science ®Google Scholar
• Akbari H, Sadiq MT, Payan M, Esmaili SS, Baghri H, Bagheri H. 2021 Feb. Depression detection based on geometrical features extracted from sodp shape of EEG signals and binary PSO. Trait Signal. 38(1):13–26. doi: 10.18280/TS.380102.
   Web of Science ®Google Scholar
• Akbari H, Tariq Sadiq M, Jafari N, Too J, Mikaeilvand N, Cicone A, Serra-Capizzano S. 2023. Recognizing seizure using Poincaré plot of EEG signals and graphical features in DWT domain. Bratisl Lek Listy. 124(1):12–24. doi: 10.4149/BLL_2023_002.
   PubMed Web of Science ®Google Scholar
• Almuhammadi WS, Aboalayon KAI, Faezipour M. 2015. Efficient obstructive sleep apnea classification based on EEG signals. Undefined. doi: 10.1109/LISAT.2015.7160186.
   Google Scholar
• Anitha C. 2019. Detection and analysis of drowsiness in human beings using multimodal signalspp. 157–174. doi: 10.1007/978-3-319-93940-7_7.
   Google Scholar
• Anjum MF, Dasgupta S, Mudumbai R, Singh A, Cavanagh JF, Narayanan NS. 2020 Oct. Linear predictive coding distinguishes spectral EEG features of Parkinson’s disease. Parkinsonism Relat Disord. 79:79–85. doi: 10.1016/j.parkreldis.2020.08.001.
   PubMed Web of Science ®Google Scholar
• Bajaj V, Taran S, Khare SK, Sengur A. 2020 Jun. Feature extraction method for classification of alertness and drowsiness states EEG signals. Appl Acoust. 163:107224. doi: 10.1016/j.apacoust.2020.107224.
   Web of Science ®Google Scholar
• Bajaj V, Taran S, Sengur A. 2018 Dec. Emotion classification using flexible analytic wavelet transform for electroencephalogram signals. Health Inf Sci Syst. 6(1):12. doi: 10.1007/s13755-018-0048-y.
   PubMedGoogle Scholar
• Bhattacharjee A, Saha S, Fattah SA, Zhu W-P, Ahmad MO. 2019 May. Sleep apnea detection based on rician modeling of feature variation in multiband EEG signal. IEEE J Biomed Health Inform. 23(3):1066–1074. doi: 10.1109/JBHI.2018.2845303.
   PubMed Web of Science ®Google Scholar
• Boonnak N, Kamonsantiroj S, Pipanmaekaporn L. 2015 Aug. Wavelet Transform Enhancement for Drowsiness Classification in EEG Records Using Energy Coefficient Distribution and neural network. Int J Mach Learn Comput. 5(4):288–293. doi: 10.7763/IJMLC.2015.V5.522.
   Google Scholar
• Boostani R, Sadatnezhad K, Sabeti M. 2009. An efficient classifier to diagnose of schizophrenia based on the EEG signals. Expert Syst Appl. 36(3 PART 2):6492–6499. doi: 10.1016/J.ESWA.2008.07.037.
   Web of Science ®Google Scholar
• Budak U, Bajaj V, Akbulut Y, Atila O, Sengur A. 2019 Sep. An effective hybrid Model for EEG-Based drowsiness detection. IEEE Sens J. 19(17):7624–7631. doi: 10.1109/JSEN.2019.2917850.
   Web of Science ®Google Scholar
• Chang H-H, Moura JMF. 2018 Nov. Biomedical Signal Processing. Undefined. 245–278. doi: 10.1201/9780849338595-8.
   Google Scholar
• Chaturvedi M, Hatz F, Gschwandtner U, Bogaarts JG, Meyer A, Fuhr P, Roth V. 2017 Jan. Quantitative EEG (QEEG) measures differentiate Parkinson’s disease (PD) patients from healthy controls (HC). In Front aging neurosci Vol. 9. JAN; doi: 10.3389/fnagi.2017.00003.
   Google Scholar
• Chaudhary S, Taran S, Bajaj V, Sengur A. 2019 Jun. Convolutional Neural Network Based Approach Towards Motor Imagery Tasks EEG signals classification. IEEE Sensors J. 19(12):4494–4500. doi: 10.1109/JSEN.2019.2899645.
   Web of Science ®Google Scholar
• Chaudhary S, Taran S, Bajaj V, Siuly S. 2020 Apr. A flexible analytic wavelet transform based approach for motor-imagery tasks classification in BCI applications. Comput Methods Programs Biomed. 187:105325. doi: 10.1016/j.cmpb.2020.105325.
   PubMed Web of Science ®Google Scholar
• de Oliveira APS, de Santana MA, Andrade MKS, Gomes JC, Rodrigues MCA, dos Santos WP. 2020 Sep. Early diagnosis of Parkinson’s disease using EEG, machine learning and partial directed coherence. Res Biomed Eng. 36(3):311–331. doi: 10.1007/s42600-020-00072-w.
   Google Scholar
• Devia C, Mayol-Troncoso R, Chile S, Parrini J, Orellana G, Ruiz A, Maldonado PE, Ignacio J. 2019 Jun. EEG classification during scene free-viewing for schizophrenia detection. IEEE Trans Neural Syst Rehabil Eng. 27(6):1193–1199. doi: 10.1109/TNSRE.2019.2913799.
   PubMed Web of Science ®Google Scholar
• Drissi TB, Zayrit S, Nsiri B, and Ammoummou A, “Diagnosis of Parkinson’s disease based on Wavelet Transform and mel frequency cepstral coefficients,” 2019. [Online]. Available: www.ijacsa.thesai.org
   Google Scholar
• El-Dahshan ESA, Bassiouni MM, Khare SK, Tan RS, Rajendra Acharya U. 2024 Apr. ExHyptNet: an explainable diagnosis of hypertension using EfficientNet with PPG signals. Expert Syst Appl. 239:122388. doi: 10.1016/J.ESWA.2023.122388.
   Web of Science ®Google Scholar
• Gadhoumi K, Lina JM, Mormann F, Gotman J. 2016 Feb. Seizure prediction for therapeutic devices: a review. J Neurosci Methods. 260:270–282. doi: 10.1016/J.JNEUMETH.2015.06.010.
   PubMed Web of Science ®Google Scholar
• Galgano M, Toshkezi G, Qiu X, Russell T, Chin L, Zhao L-R. 2017 Jul. Traumatic brain injury. Cell Transplant. 26(7):1118–1130. doi:10.1177/0963689717714102.
   PubMed Web of Science ®Google Scholar
• Garces Correa A and Laciar Leber E, “An automatic detector of drowsiness based on spectral analysis and wavelet decomposition of EEG records,” in 2010 Annual International Conference of the IEEE Engineering in Medicine and Biology, IEEE, Aug. 2010, pp. 1405–1408. doi: 10.1109/IEMBS.2010.5626721.
   Google Scholar
• Garcés Correa A, Orosco L, Laciar E. 2014 Feb. Automatic detection of drowsiness in EEG records based on multimodal analysis. Med Eng Phys. 36(2):244–249. doi: 10.1016/j.medengphy.2013.07.011.
   PubMed Web of Science ®Google Scholar
• Health home | Johns Hopkins Medicine. 2023b Feb 2. [Online]. Available: https://www.hopkinsmedicine.org/health/
   Google Scholar
• Holdgraf C, Appelhoff S, Bickel S, Bouchard K, D’Ambrosio S, David O, Devinsky O, Dichter B, Flinker A, Foster BL, et al. 2019 Jun. iEEG-BIDS, extending the brain imaging data structure specification to human intracranial electrophysiology. Sci Data. 6(1):1–6. doi: 10.1038/s41597-019-0105-7.
   PubMedGoogle Scholar
• Hosseini MP, Hosseini A, Ahi K. 2021. A review on machine learning for EEG Signal Processing in bioengineering. IEEE Rev Biomed Eng. 14:204–218. doi: 10.1109/RBME.2020.2969915.
   PubMedGoogle Scholar
• Ince NF, Goksu F, Tewfik AH, Arica S. 2009 Jul. Adapting subject specific motor imagery EEG patterns in space–time–frequency for a brain computer interface. Biomed Signal Process Control. 4(3):236–246. doi: 10.1016/j.bspc.2009.03.005.
   Web of Science ®Google Scholar
• Jahmunah V, Lih Oh S, Rajinikanth V, Ciaccio EJ, Hao Cheong K, Arunkumar N, Acharya UR. 2019 Sep. Automated detection of schizophrenia using nonlinear signal processing methods. Artif Intell Med. ;100:101698. doi: 10.1016/J.ARTMED.2019.07.006.
   PubMed Web of Science ®Google Scholar
• Jeong DH, Do Kim Y, Song IU, Chung YA, Jeong J. 2016. Wavelet energy and wavelet coherence as eeg biomarkers for the diagnosis of Parkinson’s disease-related dementia and Alzheimer’s disease. Entropy. 18(1):8. doi: 10.3390/e18010008.
   Web of Science ®Google Scholar
• Johannesen JK, Bi J, Jiang R, Kenney JG, Chen C-MA. 2016 Dec. Machine learning identification of EEG features predicting working memory performance in schizophrenia and healthy adults. Neuropsychiatr Electrophysiol. 2(1):3. doi: 10.1186/s40810-016-0017-0.
   PubMedGoogle Scholar
• Kevric J, Subasi A. 2017 Jan. Comparison of signal decomposition methods in classification of EEG signals for motor-imagery BCI system. Biomed Signal Process Control. 31:398–406. doi: 10.1016/j.bspc.2016.09.007.
   Web of Science ®Google Scholar
• Khare SK, Acharya UR. 2023a Oct. Adazd-net: automated adaptive and explainable Alzheimer’s disease detection system using EEG signals. Knowl Based Syst. 278:110858. doi: 10.1016/J.KNOSYS.2023.110858.
   Web of Science ®Google Scholar
• Khare SK, Acharya UR. 2023b Mar. An explainable and interpretable model for attention deficit hyperactivity disorder in children using EEG signals. Comput Biol Med. 155:106676. doi: 10.1016/J.COMPBIOMED.2023.106676.
   PubMed Web of Science ®Google Scholar
• Khare SK, Bajaj V. 2020 Dec. A facile and flexible motor imagery classification using electroencephalogram signals. Comput Methods Programs Biomed. 197:105722. doi: 10.1016/j.cmpb.2020.105722.
   PubMed Web of Science ®Google Scholar
• Khare SK, Bajaj V. 2021a Mar. Entropy-based drowsiness detection using adaptive variational mode decomposition. IEEE Sens J. 21(5):6421–6428. doi: 10.1109/JSEN.2020.3038440.
   Web of Science ®Google Scholar
• Khare SK, Bajaj V. 2021b Jan. An evolutionary optimized variational mode decomposition for emotion recognition. IEEE Sens J. 21(2):2035–2042. doi: 10.1109/JSEN.2020.3020915.
   Web of Science ®Google Scholar
• Khare SK, Bajaj V. 2021c Jul. Time–frequency representation and convolutional neural network-based emotion recognition. IEEE Trans Neural Netw Learn Syst. 32(7):2901–2909. doi: 10.1109/TNNLS.2020.3008938.
   PubMed Web of Science ®Google Scholar
• Khare SK, Bajaj V. 2022 Feb. Optimized tunable Q wavelet transform based drowsiness detection from electroencephalogram signals. IRBM. 43(1):13–21. doi: 10.1016/j.irbm.2020.07.005.
   Web of Science ®Google Scholar
• Khare SK, Bajaj V, Acharya UR. 2021a Apr. Detection of Parkinson’s disease using automated tunable Q wavelet transform technique with EEG signals. Biocybern Biomed Eng. 41(2):679–689. doi: 10.1016/j.bbe.2021.04.008.
   Web of Science ®Google Scholar
• Khare SK, Bajaj V, Acharya UR. 2021b Aug. Pdcnnet: an automatic framework for the detection of Parkinson’s disease using EEG signals. IEEE Sens J. 21(15):17017–17024. doi: 10.1109/JSEN.2021.3080135.
   Web of Science ®Google Scholar
• Khare SK, Bajaj V, Acharya UR. 2021c. SPWVD-CNN for automated detection of schizophrenia patients using EEG signals. IEEE Trans Instrum Meas. 70:1–9. doi: 10.1109/TIM.2021.3070608.
   PubMed Web of Science ®Google Scholar
• Khare SK, Blanes-Vidal V, Nadimi ES, Acharya UR. 2024 Feb. Emotion recognition and artificial intelligence: a systematic review (2014–2023) and research recommendations. Inform Fusion. 102:102019. doi: 10.1016/J.INFFUS.2023.102019.
   Web of Science ®Google Scholar
• Khare SK, Gadre VM, Acharya R. 2023 Nov. ECGPsychNet: an optimized hybrid ensemble model for automatic detection of psychiatric disorders using ECG signals. Physiol Meas. 44(11):115004. doi:10.1088/1361-6579/AD00FF
   Web of Science ®Google Scholar
• Khare SK, Gaikwad NB, Bajaj V. 2022. VHERS: a novel variational mode decomposition and Hilbert transform-based EEG rhythm separation for automatic ADHD detection. IEEE Trans Instrum Meas. 71. doi: 10.1109/TIM.2022.3204076.
   Web of Science ®Google Scholar
• Khare SK, March S, Barua PD, Gadre VM, Acharya UR. 2023 Nov. Application of data fusion for automated detection of children with developmental and mental disorders: a systematic review of the last decade. Inform Fusion. 99:101898. doi: 10.1016/J.INFFUS.2023.101898.
   Web of Science ®Google Scholar
• Kim JW, Lee YS, Han DH, Min KJ, Lee J, Lee K. 2015 Mar. Diagnostic utility of quantitative EEG in un-medicated schizophrenia. Neurosci Lett. 589:126–131. doi: 10.1016/j.neulet.2014.12.064.
   PubMed Web of Science ®Google Scholar
• Krishna AH, Sri AB, Priyanka KYVS, Taran S, Bajaj V. 2019 May. Emotion classification using EEG signals based on tunable‐ Q wavelet transform. IET Sci, Measur & Tech. 13(3):375–380. doi: 10.1049/iet-smt.2018.5237.
   Web of Science ®Google Scholar
• Kuhlmann L, Lehnertz K, Richardson MP, Schelter B, Zaveri HP. 2018 Oct. Seizure prediction - ready for a new era. Nat Rev Neurol. 14(10):618–630. doi: 10.1038/S41582-018-0055-2.
   PubMed Web of Science ®Google Scholar
• Lee RG, Chen CC, Hsiao CC, Wang HW, Wei MS. 2012 Apr. SLEEP APNEA SYNDROME RECOGNITION USING the GREYART NETWORK. Biomed Engi: App, Basis And Comm. 23(3):163–172. doi: 10.4015/S1016237211002505.
   Web of Science ®Google Scholar
• Lin R, Lee R, Tseng C, Zhou H, Chao CF, Jiang J. 2006. A new approach for identifying sleep apnea syndrome using wavelet transform and neural networks. Biomedical Engineering: Applications, Basis and Communications. 18:138–143.
   Web of Science ®Google Scholar
• Lin YP, Wang CH, Jung TP, Wu TL, Jeng SK, Duann JR, Chen JH. 2010. EEG-based emotion recognition in music listening. IEEE Trans Biomed Eng. 57(7):1798–1806. doi: 10.1109/TBME.2010.2048568.
   PubMed Web of Science ®Google Scholar
• Liu G, Zhang Y, Hu Z, Du X, Wu W, Xu C, Wang X, Li S. 2017. Complexity Analysis of Electroencephalogram Dynamics in patients with Parkinson’s disease. Parkinsons Dis. 2017:1–9. doi: 10.1155/2017/8701061.
   Web of Science ®Google Scholar
• Majdalawieh O, Gu J, Bai T, and Cheng G, “Biomedical Signal Processing and Rehabilitation Engineering: a review,” IEEE Pacific RIM Conference on Communications, Computers, and Signal Processing - Proceedings, vol. II, pp. 1004–1007, 2003, doi: 10.1109/PACRIM.2003.1235954.
   Google Scholar
• Mormann F, Andrzejak RG, Elger CE, Lehnertz K. 2007. Seizure prediction: the long and winding road. Brain. 130(Pt 2):314–333. doi: 10.1093/BRAIN/AWL241.
   PubMedGoogle Scholar
• Murugappan M, Alshuaib W, Bourisly AK, Khare SK, Sruthi S, Bajaj V, Gopinath SCB. 2020 Nov. Tunable Q wavelet transform based emotion classification in Parkinson’s disease using electroencephalography. PloS ONE. 15(11):e0242014. doi: 10.1371/journal.pone.0242014.
   PubMed Web of Science ®Google Scholar
• Murugappan M, Ramachandran N, Sazali Y, Murugappan M, Ramachandran N, Sazali Y. 2010 Apr. Classification of human emotion from EEG using discrete wavelet transform. J Biomed Sci Eng. 3(4):390–396. 10.4236/JBISE.2010.34054
   Google Scholar
• Oh SL, Hagiwara Y, Raghavendra U, Yuvaraj R, Arunkumar N, Murugappan M, Rajendra Acharya U. 2020 Aug. A deep learning approach for Parkinson’s disease diagnosis from EEG signals. Neural Comput Appl. 32(15):10927–10933. doi: 10.1007/s00521-018-3689-5.
   Web of Science ®Google Scholar
• Oung QW, Muthusamy H, Basah SN, Lee H, Vijean V. 2018 Feb. Empirical wavelet transform based features for classification of Parkinson’s disease severity. J Med Syst. 42(2):29. doi:10.1007/s10916-017-0877-2
   Web of Science ®Google Scholar
• Parkinson’s disease - symptoms and causes - mayo clinic. Accessed: Feb 2, 2023c. [Online]. Available: https://www.mayoclinic.org/diseases-conditions/parkinsons-disease/symptoms-causes/syc-20376055
   Google Scholar
• Park S-H, Lee D, Lee S-G. 2018 Feb. Filter bank regularized common spatial pattern ensemble for small sample motor imagery classification. IEEE Trans Neural Syst Rehabil Eng. 26(2):498–505. doi:10.1109/TNSRE.2017.2757519
   PubMed Web of Science ®Google Scholar
• Penzel T, Kesper K, Becker HF. 2006 Oct. Biosignal Monitoring and Recording. Information Technology Solutions For Healthcare. 288–301. doi: 10.1007/1-84628-141-5_13.
   Google Scholar
• Pernet CR, Appelhoff S, Gorgolewski KJ, Flandin G, Phillips C, Delorme A, Oostenveld R. 2019. EEG-BIDS, an extension to the brain imaging data structure for electroencephalography. Sci Data. 6:103. doi: 10.1038/s41597-019-0104-8.
   PubMed Web of Science ®Google Scholar
• Péron J, Dondaine T, le Jeune F, Grandjean D, Vérin M. 2012 Feb. Emotional processing in Parkinson’s disease: a systematic review. Mov Disorders. 27(2):186–199. doi:10.1002/mds.24025
   PubMed Web of Science ®Google Scholar
• Piryatinska A, Darkhovsky B, Kaplan A. 2017 Dec. Binary classification of multichannel-EEG records based on the ϵ-complexity of continuous vector functions. Comput Methods Programs Biomed. 152:131–139. doi: 10.1016/j.cmpb.2017.09.001.
   PubMed Web of Science ®Google Scholar
• Raghavendra U, Gudigar A, Paul A, Goutham TS, Inamdar MA, Hegde A, Devi A, Ooi CP, Deo RC, Barua PD, et al. 2023 Sep. Brain tumor detection and screening using artificial intelligence techniques: Current trends and future perspectives. Comput Biol Med. ;163:107063. doi: 10.1016/J.COMPBIOMED.2023.107063.
   PubMed Web of Science ®Google Scholar
• Rajput A, Soni P, Awasthi A. 2017 Jul. Result analysis of noise removal in ECG Signal using wavelet decomposition technique. Int J Comput Appl. 170(4):1–4. doi: 10.5120/IJCA2017914807.
   Google Scholar
• Rasheed K, Qayyum A, Qadir J, Sivathamboo S, Kwan P, Kuhlmann L, O’Brien T, Razi A. 2021. Machine learning for predicting epileptic seizures using EEG signals: a review. IEEE Rev Biomed Eng. 14:139–155. doi: 10.1109/RBME.2020.3008792.
   PubMedGoogle Scholar
• Repovš G. 2010. Repovš G: dealing with noise in EEG Recording and Data Analysis 18 dealing with noise in EEG Recording and Data Analysis Spoprijemanje s šumom pri zajemanju in analizi EEG signala. Informatica Medica Slovenica. 15(1):18–25.
   Google Scholar
• Ruffini G, Ibaez D, Castellano M, Dubreuil-Vall L, Soria-Frisch A, Postuma R, Gagnon JF, Montplaisir J. 2019. Deep learning with EEG spectrograms in rapid eye movement behavior disorder. Front Neurol. 10:806. doi: 10.3389/fneur.2019.00806.
   PubMed Web of Science ®Google Scholar
• Sabeti M, Boostani R, Katebi SD, Price GW. 2007. Selection of relevant features for EEG signal classification of schizophrenic patients. Biomed Signal Process Control. 2(2):122–134. doi: 10.1016/J.BSPC.2007.03.003.
   Web of Science ®Google Scholar
• Sadiq MT, Akbari H, Siuly S, Li Y, Wen P. 2022 May. Alcoholic EEG signals recognition based on phase space dynamic and geometrical features. Chaos Soliton Fract. 158:112036. doi:10.1016/J.CHAOS.2022.112036.
   Web of Science ®Google Scholar
• Sadiq MT, Yu X, Yuan Z, Aziz MZ. 2020 Dec. Motor imagery BCI classification based on novel two-dimensional modelling in empirical wavelet transform. Electron Lett. 56(25):1367–1369. doi:10.1049/EL.2020.2509
   Web of Science ®Google Scholar
• Sadiq MT, Yu X, Yuan Z, Aziz MZ, Siuly S, Ding W. 2021 Aug. Toward the development of versatile brain–computer interfaces. IEEE Trans Artif Intell. 2(4):314–328. doi: 10.1109/TAI.2021.3097307.
   Google Scholar
• Sadiq MT, Yu X, Yuan Z, China X, Aziz MZ, Rehman N. 2022. “Motor imagery BCI classification based on multivariate variational mode decomposition,” IEEE Trans Emerg Top Comput Intell. doi: 10.1109/TETCI.2022.3147030.
   Google Scholar
• Sadiq MT, Yu X, Yuan Z, Zemi R, Rehman AU, Ullah I, Li G, Xiao G. 2019. Motor imagery EEG signals decoding by multivariate empirical wavelet transform-based framework for robust brain-computer interfaces. IEEE Acces. 7:171431–171451. doi: 10.1109/ACCESS.2019.2956018.
   Web of Science ®Google Scholar
• Shahnaz C, Minhaz AT, and Ahamed ST, “Sub-frame based apnea detection exploiting delta band power ratio extracted from EEG signals,” in 2016 IEEE Region 10 Conference (TENCON), IEEE, Nov. 2016, pp. 190–193. doi: 10.1109/TENCON.2016.7847987.
   Google Scholar
• Sharma M, Acharya UR. 2021 Aug. Automated detection of schizophrenia using optimal wavelet-based 1norm features extracted from single-channel EEG. Cogn Neurodyn. 15(4):661–674. doi:10.1007/s11571-020-09655-w
   PubMed Web of Science ®Google Scholar
• Sharma M, Bapodara S, Tiwari J, Acharya UR. 2022 Jan. Automated sleep apnea detection in pregnant women using wavelet-based features. Inform Med Unlocked. 32:101026. doi:10.1016/J.IMU.2022.101026.
   Google Scholar
• Sharma S, Khare SK, Bajaj V, Ansari IA. 2021 Oct. Improving the separability of drowsiness and alert EEG signals using analytic form of wavelet transform. Appl Acoust. 181:108164. doi:10.1016/j.apacoust.2021.108164.
   Web of Science ®Google Scholar
• Sharma PC, Raja R, Vishwakarma SK, Sharma S, Mishra PK, Kushwah VS. 2022 Nov. Analysis of brain signal processing and real-time EEG signal enhancement. Multimed Tools Appl. 81(28):41013–41033. 10.1007/s11042-022-12887-z
   Web of Science ®Google Scholar
• Shim M, Hwang H-J, Kim D-W, Lee S-H, Im C-H. 2016 Oct. Machine-learning-based diagnosis of schizophrenia using combined sensor-level and source-level EEG features. Schizophr Res. 176(2–3):314–319. doi:10.1016/j.schres.2016.05.007
   PubMed Web of Science ®Google Scholar
• Shoeibi A, Ghassemi N, Khodatars M, Moridian P, Alizadehsani R, Zare A, Khosravi A, Subasi A, Rajendra Acharya U, Gorriz JM, et al. 2022 Mar. Detection of epileptic seizures on EEG signals using ANFIS classifier, autoencoders and fuzzy entropies. Biomed Signal Process Control. ;73:103417. doi: 10.1016/J.BSPC.2021.103417.
   Web of Science ®Google Scholar
• Siuly S, Khare SK, Bajaj V, Wang H, Zhang Y. 2020 Nov. A computerized method for automatic detection of schizophrenia using EEG signals. IEEE Trans Neural Syst Rehabil Eng. 28(11):2390–2400. doi:10.1109/TNSRE.2020.3022715
   PubMed Web of Science ®Google Scholar
• Siuly S, Li Y. 2012 Jul. Improving the separability of motor imagery EEG signals using a cross correlation-based least square support vector machine for brain–computer interface. IEEE Trans Neural Syst Rehabil Eng. 20(4):526–538. doi:10.1109/TNSRE.2012.2184838
   PubMed Web of Science ®Google Scholar
• Siuly S, Li Y. 2015 May. Discriminating the brain activities for brain–computer interface applications through the optimal allocation-based approach. Neural Comput Appl. 26(4):799–811. doi:10.1007/s00521-014-1753-3
   Web of Science ®Google Scholar
• Siuly NA, Li Y, Wen P. 2013. Identification of motor imagery tasks through CC-LR algorithm in brain computer interface. Int J Bioinform Res Appl. 9(2):156. doi: 10.1504/IJBRA.2013.052447.
   PubMedGoogle Scholar
• Siuly Y, Li, Wen P(. 2011 Dec. Clustering technique-based least square support vector machine for EEG signal classification. Comput Methods Programs Biomed. 104(3):358–372. doi:10.1016/j.cmpb.2010.11.014
   PubMed Web of Science ®Google Scholar
• Siuly Y, Li, Wen P(. 2014 Mar. Modified CC-LR algorithm with three diverse feature sets for motor imagery tasks classification in EEG based brain–computer interface. Comput Methods Programs Biomed. 113(3):767–780. doi:10.1016/j.cmpb.2013.12.020
   PubMed Web of Science ®Google Scholar
• Siuly HW, Zhang Y. 2016 May. Detection of motor imagery EEG signals employing Naïve Bayes based learning process. Measurement. 86:148–158. doi:10.1016/j.measurement.2016.02.059.
   Web of Science ®Google Scholar
• Soliman S, Fouad AM, Mourad E, Hossam S, Ehab M, Selim S, Darweesh MS. 2022 Nov. Deep learning approaches for epileptic seizure prediction: a review. Institute of Electrical and Electronics Engineers (IEEE). pp. 01–06. 10.1109/niles56402.2022.9942420.
   Google Scholar
• Subasi A. 2019 Jan. Feature extraction and dimension reduction. Practical Guide For Biomedical Signals Analysis Using Machine Learning Techniques. 193–275. 10.1016/B978-0-12-817444-9.00004-0
   Google Scholar
• Sui J et al., “Combination of FMRI-SMRI-EEG data improves discrimination of schizophrenia patients by ensemble feature selection,” in 2014 36th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, IEEE, Aug. 2014, pp. 3889–3892. doi: 10.1109/EMBC.2014.6944473.
   Google Scholar
• Taran S, Bajaj V. 2018 Nov. Drowsiness detection using adaptive hermite decomposition and extreme learning machine for electroencephalogram signals. IEEE Sens J. 18(21):8855–8862. doi:10.1109/JSEN.2018.2869775
   Web of Science ®Google Scholar
• Taran S, Bajaj V. 2019a May. Emotion recognition from single-channel EEG signals using a two-stage correlation and instantaneous frequency-based filtering method. Comput Methods Programs Biomed. 173:157–165. DOI:10.1016/j.cmpb.2019.03.015.
   PubMed Web of Science ®Google Scholar
• Taran S, Bajaj V. 2019b Nov. Motor imagery tasks-based EEG signals classification using tunable-Q wavelet transform. Neural Comput Appl. 31(11):6925–6932. doi:10.1007/s00521-018-3531-0
   Web of Science ®Google Scholar
• Taran S, Bajaj V, Sharma D. 2017a Aug. Robust hermite decomposition algorithm for classification of sleep apnea EEG signals. Electron Lett. 53(17):1182–1184. doi:10.1049/el.2017.1365
   Web of Science ®Google Scholar
• Taran S, Bajaj V, Sharma D. 2017b November. TEO separated AM-FM components for identification of apnea EEG signals. Undefined. 2017:391–395. DOI:10.1109/SIPROCESS.2017.8124571.
   Google Scholar
• Taran S, Bajaj V, Sharma D, Siuly S, Sengur A. 2018 Feb. Features based on analytic IMF for classifying motor imagery EEG signals in BCI applications. Measurement. 116:68–76. DOI:10.1016/j.measurement.2017.10.067.
   Web of Science ®Google Scholar
• Taran S, Bajaj V, Sinha GR, Polat K. 2021 Oct. Detection of sleep apnea events using electroencephalogram signals. Appl Acoust. 181:108137. DOI:10.1016/j.apacoust.2021.108137.
   Web of Science ®Google Scholar
• Tripathy RK, Rajendra Acharya U. 2018. Use of features from RR-time series and EEG signals for automated classification of sleep stages in deep neural network framework. Biocybern Biomed Eng. 38(4):890–902. doi: 10.1016/j.bbe.2018.05.005.
   Web of Science ®Google Scholar
• Tuncer T, Dogan S, Acharya UR. 2020 Jan. Automated detection of Parkinson’s disease using minimum average maximum tree and singular value decomposition method with vowels. Biocybern Biomed Eng. 40(1):211–220. 10.1016/j.bbe.2019.05.006
   Web of Science ®Google Scholar
• Verma NK, Rao LSVS, and Sharma SK, “Motor imagery EEG signal classification on DWT and crosscorrelated signal features,” in 2014 9th International Conference on Industrial and Information Systems (ICIIS), IEEE, Dec. 2014, pp. 1–6. doi: 10.1109/ICIINFS.2014.7036473.
   Google Scholar
• Vishwakarma SK, Sharma PC, Raja R, Roy V, Tomar S. 2020 Oct. An effective cascaded approach for eeg artifacts elimination. International Journal Of Pharmaceutical Research. 12(4):4822–4828. doi:10.31838/IJPR/2020.12.04.653
   Google Scholar
• Wang XW, Nie D, Lu BL. 2011. EEG-based emotion recognition using frequency domain features and support vector machines. Lecture Notes In Comp Sci (Including Subseries Lecture Notes In Arti Inte & Lecture Notes In Bioinfo). 7062(PART 1):734–743. doi: 10.1007/978-3-642-24955-6_87/COVER.
   Google Scholar
• Xu S, Wang Z, Sun J, Zhang Z, Wu Z, Yang T, Xue G, Cheng C. 2020 Jul. Using a deep recurrent neural network with EEG signal to detect Parkinson’s disease. Ann Transl Med. 8(14):874–874. doi: 10.21037/atm-20-5100.
   PubMed Web of Science ®Google Scholar
• Yaacob H, Hossain F, Shari S, Khare SK, Ooi CP, Acharya UR. 2023. Application of artificial intelligence techniques for brain-computer interface in mental fatigue detection: a systematic review (2011-2022). IEEE Acces. 11:74736–74758. doi: 10.1109/ACCESS.2023.3296382.
   Web of Science ®Google Scholar
• Yu X, Aziz MZ, Sadiq MT, Fan Z, Xiao G. 2021. A new framework for automatic detection of motor and mental imagery EEG signals for robust BCI systems. IEEE Trans Instrum Meas. 70:1–12. doi: 10.1109/TIM.2021.3069026.
   PubMed Web of Science ®Google Scholar
• Zhang L, “EEG signals classification using machine learning for the identification and diagnosis of schizophrenia,” in 2019 41st Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), IEEE, Jul. 2019, pp. 4521–4524. doi: 10.1109/EMBC.2019.8857946.
   Google Scholar
• Zhang R, Xu P, Guo L, Zhang Y, Li P, Yao D, Xiong M. 2013 Sep. Z-Score linear discriminant analysis for EEG based brain-computer interfaces. PloS ONE. 8(9):e74433. 10.1371/journal.pone.0074433
   PubMed Web of Science ®Google Scholar
• Zhou J, Wu X, Zeng W. 2015 Dec. Automatic detection of sleep apnea based on EEG detrended fluctuation analysis and support vector machine. J Clin Monit Comput. 29(6):767–772. doi:10.1007/s10877-015-9664-0
   PubMed Web of Science ®Google Scholar