ترکیب فیلتر ذره ترتیبی و شکل‌دهنده پرتو برای مکانیابی منابع اخلالگر مغزی

[1] R. Chin Fatt, G. Fonzo and et al, “Effect of intrinsic patterns of functional brain connectivity in moderating antidepressant treatment response in major depression,” The American Jurnal of Psychiatric 177:2, February 2020.

[2] O’Reilly, J.D. Lewis, M. Elsabbagh, “Is functional brain connectivity atypical in autism? A systematic review of EEG and MEG studies,” Plos One; 0175870, May 2017.

[3] J. Perkins, M. A. Stokes and et al, “Increased left hemisphere impairment in high-functioning autism: a tract based spatial statistics study,” Psychiatry Research: Neuroimaging 2014; 224:119- 123, 2014.

[4] Liu, Y. Sheng and et al, “Improved ASD classification using dynamic functional connectivity and multi-task feature selection,” Elsevier; Vol 138, 82-87, October 2020.

[5] Š. Holiga and et al, “Patients with autism spectrum disorders display reproducible functional connectivity alterations,” Science Translational Medicine, Vol. 11, Issue 481, eaat9223, 2019.

[6] Imperatori, B. Farina and et al, “Default mode network alterations in individuals with high-trait-anxiety: An EEG functional connectivity study,” Elsevier; Vol 246, 611-618, March 2019.

[7] Karamzadeh, A. Medvedev and et al, “Capturing dynamic patterns of task-based functional connectivity with EEG,” Elsevier; Vol 66, 311-317, February 2013.

[8] Imperatori, B. Farina and et al, “Aberrant EEG functional connectivity and EEG power spectra in resting state post-traumatic stress disorder: A sLORETA study,” Elsevier; Vol 102, 10-17, October 2014.

[9] E. Whitton, S. Deccy and et al, “Electroencephalography source functional connectivity reveals abnormal high-frequency communication among large-scale functional networks in depression,” Elsevier; Vol 3, 50-58, January 2018.

Haputhanthri and et al, “An EEG based channel optimized classification approach for autism spectrum disorder,” IEEE Moratuwa Engineering Research Conference (MERCon), 2019.

Askari, S. K. Setarehdan and et al, “Modeling the connections of brain regions in children with autism using cellular neural networks and electroencephalography analysis.” Elsevier; Vol 89, 40-50, July 2018.

A Ali, A.R Syafeeza, A. S Jaafar, M.K Mohd Fitri Alif “Autism spectrum disorder classification on electroencephalogram signal using deep learning algorithm,” IAES International Journal of Artificial Intelligence (IJ-AI); Vol. 9, No. 1, 91-99, March 2020.

Wang, H. El-Fiqi and et al, “Convolutional Neural Networks Using Dynamic Functional Connectivity for EEG-Based Person Identification in Diverse Human States,” IEEE Transactions on Information Forensics and Security; Vol 14, Issue: 12, Dec. 2019.

E. Vissers, M. X. Cohen and H. M. Geurts, “Brain connectivity and high functioning autism: a promising path of research that needs refiened models,” Neuroscience and Biobehavioral Reviews; 36:604-625, 2012.

http://sciencemission.com/news archive/ “can brain connectivity be used as a biomarker for autism?” March 2019.

Zarghami, H. S. Mir and H. Al-Nashash, “Transfer-Function-Based calibration of sparse eeg systems for brain source localization,” IEEE Sensors Journal, VOL. 15, NO. 3, March 2015.

Costa, H. Batatia and et al, “Sparse EEG Source Localization using Bernoulli Laplacian Priors,” IEEE Transactions on Biomedical Engineering; TBME-00633, 2015.

Nguyen, T. Potter and et al, “EEG source imaging guided by spatiotemporal specific fmri: toward an understanding of dynamic cognitive processes,” Neural Plasticity; Vol 2016, Article ID 4182483, 2016.

Becker, L. Albera and et al, “Brain-Source Imaging: From sparse to tensor models,” Signal Processing Magazine, IEEE, vol. 32, pp. 100–112, 2015.

Grech, T. Cassar and et al, “Review on solving the inverse problem in EEG source analysis,” Journal of NeuroEngineering and Rehabilitation; 5-25, 2008.

Noriega, “A neural model to study sensory abnormalities and multisensory effects in autism,” IEEE Transactions on Neural Systems and Rehabilitation Engineering; 23(2)199-209, 2014.

I.Papageorgiou andA. Kannappan, “Fuzzy cognitive map ensemble learning paradigm to solve classification problems: application to autism identification,” Applied Soft Computing; 12:3798-3809, 2012.

Huang, J. Shih and et al, “Commonalities and differences among vectorized beamformers in electromagnetic source imaging,” Brain Topography, vol. 16, no. 3, pp. 139–158, 2004.

V. Vliet, N. Chumerin and et al, “Single-trial ERP component analysis using a spatio-temporal LCMV beamformer,” IEEE Transactions on Biomedical Engineering, 0018-9294 (c), 2015.

Popescu, E. Popescu, T. Chan, S. Blunt and J. D. Lewine, “Spatiotemporal reconstruction of bilateral auditory steady-state responses using MEG beamformers,” IEEE Trans. Biomed. Eng., vol. 55, no. 3, pp. 1092– 1102, Mar. 2008.

V. Veen, W. V. Drongelen, M. Yuchtman and A. Suzuki, “Localization of brain electrical activity via linearly constrained minimum variance spatial filter,” IEEE Trans. Biomed. Eng., vol. 44, no. 9, pp. 867–880, Sep. 1997.

Georgieva and et al, "A Beamformer-Particle Filter Framework for Localization of Correlated EEG Sources," IEEE Journal of Biomedical and Health Informatics, VOL. 20, NO. 3, MAY 2016.

Yuan, T. Liu and et al, “An EEG and fMRI study of motor imagery: Negative correlation of bold and EEG activity in primary motor cortex,” Neuroimage, vol. 49, pp. 2596–2606, 2010.

Doucet and A.M. Johansen, Atutorial on particle filtering and smoothing: Fifteen years later, in Handbook of Nonlinear Filtering. Oxford, U.K.: Oxford Univ. Press, pp. 656–704, 2009.

Galka, O. Yamashita, T. Ozaki, R. Biscay and P. Valde, "A solution to the dynamical inverse problem of eeg generation using spatiotemporal kalman filtering," Inverse Problems, pp. 435–453, 2004.

Şengül, U. Baysal, “an extended kalman filtering approach for the estimation of human head tissue conductivities by using EEG data: a simulation study”, Physiological Measurement, Volume 33, Number 4, 2012.

Arulampalam, S. Maskell, N. Gordon, and T. Clapp, "A tutorial on particle filters for online nonlinear/non-gaussian bayesian tracking," IEEE Trans. Signal Process., vol. 50, no. 2, pp. 174–188, Feb. 2002.

R. Mohseni, S. Sanei and et al, "A beamforming particle filter for EEG dipole source localization," IEEE International Conference on Acoustics, Speech and Signal Processing, (ICASSP) 2009.

Salu, L. Cohen, D. Rose, S. Sato, C.Kufta and M.Hallet, “An improved method for localizing electric brain dipoles,” IEEE Trans. Biomed. Eng., vol. 37, no. 7, pp. 699–705, Jul. 1990.

Qiu and et al, “A survey of motion-based multitarget tracking methods,” Progress in Electromagnetics Research B, Vol. 62, 195–223, 2015.

Miao, J. J. Zhang, C. Chakrabarti and A. Papandreou-Suppappola, “Efficient bayesian tracking of multiple sources of neural activity: algorithms and real-time FPGA implementation,” IEEE Transactions on Signal Processing, VOL. 61, NO. 3, February 1, 2013.

Amrouche, A. Khenchaf andD. Berkani, "Multiple target tracking using track before detect algorithm", International Conference on Electromagnetics in Advanced Applications (ICEAA), 2017.

N. Ito and S. Godsill, “A multi-target track-before-detect particle filter using superpositional data in non-gaussian noise,” IEEE Signal Processing Letters, Vol. 27, 2020.

https://www.cs.colostate.edu/eeg/main/data/1989_Keirn_and_Aunon.

http://neuroimage.usc.edu/brainstorm.

C. Mosher, R. M. Leahy and P. S. Lewis, “EEG and MEG: Forward Solutions for Inverse Methods,” IEEE Transactions on Biomedical Engineering, VOL. 46, NO. 3, March 1999.

Ali Aroudi and Simon Doclo, “Cognitive-driven binaural lcmv beamformer using eeg-based auditory attention decoding,” IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), 2019.

D. Pascual-Marqui, “Standardized low-resolution brain electromagnetic tomography (sLORETA): technical details,” Methods Find Exp Clin Pharmacol; 24 Suppl D: 5-12. PMID: 12575463, 2002.