Advances in Electrical Source Imaging: A Review of the Current Approaches, Applications and Challenges
Abstract
:1. Introduction
2. State-of-the-Art
2.1. Forward Problem
2.2. Inverse Problem
3. ESI-Clinical and Cognitive Research Implementations
3.1. Clinical Applications
3.2. Cognitive Applications
4. ESI Challenges and Limitations
5. Conclusions
Author Contributions
Funding
Institutional Review Board Statement
Informed Consent Statement
Data Availability Statement
Conflicts of Interest
References
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Method | Authors | Advantages | Disadvantages |
---|---|---|---|
Recurrent Neural Network—Long Short-Term Memory | [10] | Extremely fast computation of source estimates, once the training has completed. Can harness the spatio-temporal information of EEG, resulting in more robust solution regarding noise. Great expandability and room for improvement. | Trained for single sources. Requires a lot of time for the training session, even if the model is simple. Worse accuracy than other models presented. |
Convolutional Neural Netrwork | [11] | Simplicity and expandability. Once trained, produces results extremely fast. | Trained on single time points and does not incorporate the temporal information of EEG creating low noise tolerance. Lower accuracy on multisource scenarios. |
Denoising AutoEncoder | [46] | Very high noise tolerance, producing accurate results even with low SNR. Do not require mathematical priors. | Requires a lot of time for training and offline computation of the Leadfield matrix. Susceptible to overfitting due to vanishing gradient for complex scenarios. |
Bayesian Method—Bernouli Laplacian priors | [12] | Near-zero mean localization error. Great recovery and accuracy of dipole locations in low SNR. Sparser solutions. Correct estimation of the amplitude of source currents. | Very high computational cost. Requires accurate head model for high accuracy. |
Bayesian Method—Kalman Filters | [49] | Near-zero mean localization error. Lower computational cost than other spatio-temporal dynamic algorithms, faster than other KF approaches. | Under-estimation of the amplitude of source currents. Requires a priori information for the source covariance matrix. |
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Zorzos, I.; Kakkos, I.; Ventouras, E.M.; Matsopoulos, G.K. Advances in Electrical Source Imaging: A Review of the Current Approaches, Applications and Challenges. Signals 2021, 2, 378-391. https://doi.org/10.3390/signals2030024
Zorzos I, Kakkos I, Ventouras EM, Matsopoulos GK. Advances in Electrical Source Imaging: A Review of the Current Approaches, Applications and Challenges. Signals. 2021; 2(3):378-391. https://doi.org/10.3390/signals2030024
Chicago/Turabian StyleZorzos, Ioannis, Ioannis Kakkos, Errikos M. Ventouras, and George K. Matsopoulos. 2021. "Advances in Electrical Source Imaging: A Review of the Current Approaches, Applications and Challenges" Signals 2, no. 3: 378-391. https://doi.org/10.3390/signals2030024