- Tysnes, O.-B. and A. Storstein, Epidemiology of Parkinson’s disease. Journal of Neural Transmission, 2017. 124(8): p. 901-905.
- Bhat, S., et al., Parkinson's disease: Cause factors, measurable indicators, and early diagnosis. Computers in biology and medicine, 2018. 102: p. 234-241.
- Oh, S.L., et al., A deep learning approach for Parkinson’s disease diagnosis from EEG signals. Neural Computing and Applications, 2018: p. 1-7.
- Loh, H.W., et al., Application of deep learning models for automated identification of Parkinson’s disease: a review (2011–2021). Sensors, 2021. 21(21): p. 7034.
- Lee, S., et al., A convolutional-recurrent neural network approach to resting-state EEG classification in Parkinsons disease. medRxiv, 2021.
- Emamzadeh-Hashemi, E.A., et al., Deep Transfer Learning for Parkinson’s Disease Monitoring by Image-Based Representation of Resting-State EEG Using Directional Connectivity. Algorithms, 2022. 15(1): p. 5.
- Khare, S.K., V. Bajaj, and U.R. Acharya, PDCNNet: An automatic framework for the detection of Parkinson’s Disease using EEG signals. IEEE Sensors Journal, 2021.
- Chang, K.-H., et al., Evaluating the Different Stages of Parkinson’s Disease Using Electroencephalography With Holo-Hilbert Spectral Analysis. Frontiers in aging neuroscience, 2022. 14.
- Lee, S.-B., et al., Predicting Parkinson's disease using gradient boosting decision tree models with electroencephalography signals. Parkinsonism & Related Disorders, 2022.
- Aljalal, M., et al., Parkinson’s Disease Detection from Resting-State EEG Signals Using Common Spatial Pattern, Entropy, and Machine Learning Techniques. Diagnostics, 2022. 12(5): p. 1033.
- Khoshnevis, S.A. and R. Sankar, Diagnosis of Parkinson’s disease using higher order statistical analysis of alpha and beta rhythms. Biomedical Signal Processing and Control, 2022. 77: p. 103743.
- Khare, S.K., V. Bajaj, and U.R. Acharya, Detection of Parkinson’s disease using automated tunable Q wavelet transform technique with EEG signals. Biocybernetics and Biomedical Engineering, 2021. 41(2): p. 679-689.
- Barua, P.D., et al., Novel automated PD detection system using aspirin pattern with EEG signals. Computers in biology and medicine, 2021. 137: p. 104841.
- Wang, S., et al. An EEG-based approach for Parkinson’s disease diagnosis using capsule network. in 2022 7th International Conference on Intelligent Computing and Signal Processing (ICSP). 2022. IEEE.
- Kamalakannan, N., S.P.S. Balamurugan, and K. Shanmugam, A NOVEL APPROACH FOR THE EARLY DETECTION OF PARKINSON’S DISEASE USING EEG SIGNAL. Technology (IJEET), 2021. 12(5): p. 80-95.
- Guo, G. Diagnosing Parkinson’s Disease Using Multimodal Physiological Signals. in Human Brain and Artificial Intelligence: Second International Workshop, HBAI 2020: Held in Conjunction with IJCAI-PRICAI 2020, Yokohama, Japan, January 7, 2021: Revised Selected Papers. 2021. Springer.
- Silva, G., et al., Parkinson Disease Early Detection using EEG Channels Cross-Correlation. International Journal of Applied Engineering Research, 2020. 15(3): p. 197-203.
- Railo, H., et al., Resting state EEG as a biomarker of Parkinson's disease: Influence of measurement conditions. BioRxiv, 2020.
- Anjum, M.F., et al., Linear predictive coding distinguishes spectral EEG features of Parkinson's disease. Parkinsonism & Related Disorders, 2020. 79: p. 79-85.
- Bhurane, A.A., et al., Diagnosis of Parkinson's disease from electroencephalography signals using linear and self‐similarity features. Expert Systems, 2019: p. e12472.
- Yuvaraj, R., U.R. Acharya, and Y. Hagiwara, A novel Parkinson’s Disease Diagnosis Index using higher-order spectra features in EEG signals. Neural Computing and Applications, 2018. 30(4): p. 1225-1235.
- Liu, G., et al., Complexity analysis of electroencephalogram dynamics in patients with Parkinson’s disease. Parkinson’s Disease, 2017. 2017.
- Chaturvedi, M., et al., Quantitative EEG (QEEG) measures differentiate Parkinson's disease (PD) patients from healthy controls (HC). Frontiers in aging neuroscience, 2017. 9: p. 3.
- Chawla, P., et al., A decision support system for automated diagnosis of Parkinson’s disease from EEG using FAWT and entropy features. Biomedical Signal Processing and Control, 2023. 79: p. 104116.
- Desai, K., Parkinsons Disease Detection via Resting-State Electroencephalography Using Signal Processing and Machine Learning Techniques. arXiv preprint arXiv:2304.01214, 2023.
- Kurbatskaya, A., et al., Machine Learning-Based Detection of Parkinson's Disease From Resting-State EEG: A Multi-Center Study. arXiv preprint arXiv:2303.01389, 2023.
- Suuronen, I., et al., Budget-based classification of Parkinson's disease from resting state EEG. IEEE Journal of Biomedical and Health Informatics, 2023.
- Brown, D.R., S.P. Richardson, and J.F. Cavanagh, An EEG marker of reward processing is diminished in Parkinson’s disease. Brain research, 2020. 1727: p. 146541.
- Ezazi, Y. and P. Ghaderyan, Textural feature of EEG signals as a new biomarker of reward processing in Parkinson’s disease detection. Biocybernetics and Biomedical Engineering, 2022. 42(3): p. 950-962.
- Cavanagh, J.F., et al., The patient repository for EEG data+ computational tools (PRED+ CT). Frontiers in neuroinformatics, 2017. 11: p. 67.
- Shen, M., X. Zhang, and X. Li. Independent component analysis of electroencephalographic signals. in 6th International Conference on Signal Processing, 2002. 2002. IEEE.
- Walsh, M.M. and J.R. Anderson, Learning from experience: event-related potential correlates of reward processing, neural adaptation, and behavioral choice. Neuroscience & Biobehavioral Reviews, 2012. 36(8): p. 1870-1884.
- Cavanagh, J.F., et al., Cognitive states influence dopamine-driven aberrant learning in Parkinson's disease. Cortex, 2017. 90: p. 115-124.
- Ghaderyan, P. and A. Abbasi, A novel cepstral-based technique for automatic cognitive load estimation. Biomedical Signal Processing and Control, 2018. 39: p. 396-404.
- Benesty, J., M.M. Sondhi, and Y. Huang, Springer handbook of speech processing. Vol. 1. 2008: Springer.
- Huang, X., et al., Spoken language processing: A guide to theory, algorithm, and system development. 2001: Prentice hall PTR.
- Vanrell, S.R., D.H. Milone, and H.L. Rufiner, Assessment of homomorphic analysis for human activity recognition from acceleration signals. IEEE journal of biomedical and health informatics, 2017. 22(4): p. 1001-1010.
- Reddy, S.A., et al., EEG analysis of mathematical cognitive function and startle response using single channel electrode. CSI Transactions on ICT, 2020. 8(4): p. 367-376.
- Poorna, S.S., et al. EEG based control using spectral features. in 2018 2nd International Conference on I-SMAC (IoT in Social, Mobile, Analytics and Cloud)(I-SMAC) I-SMAC (IoT in Social, Mobile, Analytics and Cloud)(I-SMAC), 2018 2nd International Conference on. 2018. IEEE.
- Ghaderyan, P. and A. Abbasi, An efficient automatic workload estimation method based on electrodermal activity using pattern classifier combinations. International Journal of Psychophysiology, 2016. 110: p. 91-101.
- de Oliveira, A.P.S., et al., Early diagnosis of Parkinson’s disease using EEG, machine learning and partial directed coherence. Research on Biomedical Engineering, 2020. 36(3): p. 311-331.
- Vapnik, V., The nature of statistical learning theory. 1999: Springer science & business media.
- Abe, S., Support vector machines for pattern classification. Vol. 2. 2005: Springer.
- Bridgelall, R., Tutorial on Support Vector Machines. 2022.
- Beyrami, S.M.G. and P. Ghaderyan, A robust, cost-effective and non-invasive computer-aided method for diagnosis three types of neurodegenerative diseases with gait signal analysis. Measurement, 2020. 156: p. 107579.
- Moghaddam, F., P. Ghaderyan, and M. Shamsi, Diagnosis of Attention Deficit/Hyperactivity Disorder using the analysis of different brain regions connectivity and Dynamic Time Warping method. TABRIZ JOURNAL OF ELECTRICAL ENGINEERING, 2023. 53(3): p. 223-233.
- Sha’abani, M., et al., kNN and SVM classification for EEG: a review. InECCE2019, 2020: p. 555-565.
- Cunningham, P. and S.J. Delany, k-Nearest neighbour classifiers: (with Python examples). arXiv preprint arXiv:2004.04523, 2020.
- Wang, S.-C., Artificial Neural Network, in Interdisciplinary Computing in Java Programming. 2003, Springer US: Boston, MA. p. 81-100.
- Warsito, B., R. Santoso, and H. Yasin. Cascade forward neural network for time series prediction. in Journal of Physics: Conference Series. 2018. IOP Publishing.
- Balakrishnama, S. and A. Ganapathiraju, Linear discriminant analysis-a brief tutorial. Institute for Signal and information Processing, 1998. 18(1998): p. 1-8.
- Narayan, Y., Hb vsEMG signal classification with time domain and Frequency domain features using LDA and ANN classifier. Materials Today: Proceedings, 2021. 37: p. 3226-3230.
- Pourezzat, M. and H. Danandeh Hesar, Development of a New Adaptive Method Based on Empirical Fourier Decomposition for the Diagnosis of Obstructive Sleep Apnea Using Electrocardiogram Signal Analysis. TABRIZ JOURNAL OF ELECTRICAL ENGINEERING, 2023. 53(3): p. 159-170.
- Ezazi, Y. and P. Ghaderyan, Parkinson’s disease detection using EEG signals analysis based on Walsh Hadamard transform. Intelligent Multimedia Processing and Communication Systems (IMPCS), 2021. 2(2): p. 1-8.
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