EEGg: Generating Synthetic EEG Signals in Matlab Environment
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Abstract
Purpose: Utilizing Electroencephalogram (EEG) is more than at any time in history, therefore we have introduced an open-source MATLAB function to provide simulated EEG which is as equivalent as viable to empirical EEG in a user-friendly way with ground truth that is not accessible in real EEG records.
This function should be versatile due to the requirements such as the number and orientation of sources, various noises, mode of activation function, and different anatomical structures.
Materials and Methods: We indicate all phases, modes, and formulas which constitute EEGg, EEG generator. This function supports selecting main sources locations and orientation, choosing SNR with white Gaussian noise, electrode numbers, and mode of activation functions. Also, users have the option to use automatic or partly automatic, or fully automatic EEG construction in EEGg. This function is ready to use at https://github.com/Avayekta/EEG.
Results: EEGg is designed with several parameters that users have chosen. Hence, users can choose different variables to inspect the time and frequency aspects of synthetic EEG.
Conclusion: EEGg is a multi-purpose and comprehensive function to mimic EEG but with ground-truth EEG data and adjustable parameters.
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25- Claude Bedard, Helmut Kroeger, and Alain Destexhe, "Does the 1/f frequency scaling of brain signals reflect self organized critical states?" Physical review letters, Vol. 97 (No. 11), p. 118102, (2006)
2- Suprijanto Suprijanto, Masitoh Masitoh, Finda Dwi Putri, and Ayu G Risangtuni, "Future Trend of EEG Source Localization for Quantifying Repetitive Motor Activity as feedback for Rehabilitation Device." in 2021 International Conference on Instrumentation, Control, and Automation (ICA), (2021): IEEE, pp. 12-15.
3- Takehiko Yamanashi et al., "Topological data analysis (TDA) enhances bispectral EEG (BSEEG) algorithm for detection of delirium." Scientific reports, Vol. 11 (No. 1), pp. 1-9, (2021).
4- Siqi Cai, Peiwen Li, Enze Su, Qi Liu, and Longhan Xie, "A Neural-Inspired Architecture for EEG-Based Auditory Attention Detection." IEEE Transactions on Human Machine Systems, (2022).
5- Rasmus L Kæseler, Tim Warburg Johansson, Lotte NS Andreasen Struijk, and Mads Jochumsen, "Feature and Classification Analysis for Detection and Classification of Tongue Movements From Single-Trial Pre-Movement EEG." IEEE Transactions on Neural Systems and Rehabilitation Engineering, Vol. 30pp. 678-87, (2022).
6- Amjed S Al-Fahoum and Ausilah A Al-Fraihat, "Methods of EEG signal features extraction using linear analysis in frequency and time-frequency domains." International Scholarly Research Notices, Vol. 2014(2014).
7- Paul L Nunez and Ramesh Srinivasan, Electric fields of the brain: the neurophysics of EEG. Oxford University Press, USA, (2006).
8- Munsif Ali Jatoi, Nidal Kamel, Aamir Saeed Malik, Ibrahima Faye, and Tahamina Begum, "A survey of methods used for source localization using EEG signals." Biomedical Signal Processing and Control, Vol. 11pp. 42-52, (2014).
9- Nasser Samadzadehaghdam, Bahador Makkiabadi, Sadegh Masjoodi, Mohammad Mohammadi, and Fahimeh Mohagheghian, "A new linearly constrained minimum variance beamformer for reconstructing EEG sparse sources." International Journal of Imaging Systems and Technology, Vol. 29 (No. 4), pp. 686-700, (2019).
10- E Giraldo, AJ den Dekker, and G Castellanos-Dominguez, "Estimation of dynamic neural activity using a Kalman filter approach based on physiological models." in 2010 Annual International Conference of the IEEE Engineering in Medicine and Biology, (2010): IEEE, pp. 2914-17.
11- Stefan Haufe, Ryota Tomioka, Guido Nolte, Klaus-Robert Müller, and Motoaki Kawanabe, "Modeling sparse connectivity between underlying brain sources for EEG/MEG." IEEE transactions on biomedical engineering, Vol. 57 (No. 8), pp. 1954-63, (2010).
12- Stefan Haufe, Vadim V Nikulin, Klaus-Robert Müller, and Guido Nolte, "A critical assessment of connectivity measures for EEG data: a simulation study." Neuroimage, Vol. 64pp. 120-33, (2013).
13- Geertjan Huiskamp, "Interindividual variability of skull conductivity: an EEG-MEG analysis." International Journal of Bioelectromagnetism, Vol. 10 (No. 1), pp. 25-30, (2008).
14- Arnaud Delorme and Scott Makeig, "EEGLAB: an open source toolbox for analysis of single-trial EEG dynamics including independent component analysis." Journal of neuroscience methods, Vol. 134 (No. 1), pp. 9-21, (2004).
15- Sheng-Hsiou Hsu, Tim Mullen, Tzyy-Ping Jung, and Gert Cauwenberghs, "Online recursive independent component analysis for real-time source separation of high-density EEG." in 2014 36th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, (2014): IEEE, pp. 3845-48.
16- Anderson S Oliveira, Bryan R Schlink, W David Hairston, Peter König, and Daniel P Ferris, "Induction and separation of motion artifacts in EEG data using a mobile phantom head device." Journal of neural engineering, Vol. 13 (No. 3), p. 036014, (2016).
17- Laurens R Krol, Juliane Pawlitzki, Fabien Lotte, Klaus Gramann, and Thorsten O Zander, "SEREEGA: Simulating event-related EEG activity." Journal of neuroscience methods, Vol. 309pp. 13-24, (2018).
18- Alessandra Anzolin, Jlenia Toppi, Manuela Petti, Febo Cincotti, and Laura Astolfi, "SEED-G: simulated EEG data generator for testing connectivity algorithms." Sensors, Vol. 21 (No. 11), p. 3632, (2021).
19- Anthony T Herdman, "SimMEEG software for simulating event-related MEG and EEG data with underlying functional connectivity." Journal of neuroscience methods, Vol. 350p. 109017, (2021).
20- Jussi T Lindgren, Adrien Merlini, Anatole Lecuyer, and Francesco P Andriulli, "simBCI—a framework for studying BCI methods by simulated EEG." IEEE Transactions on Neural Systems and Rehabilitation Engineering, Vol. 26 (No. 11), pp. 2096-105, (2018).
21- Robert Oostenveld, Pascal Fries, Eric Maris, and Jan Mathijs Schoffelen, "FieldTrip: open source software for advanced analysis of MEG, EEG, and invasive electrophysiological data." Computational intelligence and neuroscience, Vol. 2011(2011).
22- Eduardo Lattari et al., "Corticomuscular coherence behavior in fine motor control of force: a critical review." Rev. Neurol, Vol. 51 (No. 10), pp. 610-23, (2010).
23- Hailong Liu, Jue Wang, Chongxun Zheng, and Ping He, "Study on the effect of different frequency bands of EEG signals on mental tasks classification." in 2005 IEEE Engineering in Medicine and Biology 27th Annual Conference, (2006): IEEE, pp. 5369-72.
24- Manuela Petti et al., "Effect of inter-trials variability on the estimation of cortical connectivity by Partial Directed A. Yektaeian Vaziri, et al. 381 FBT, Vol. 10, No. 3 (Summer 2023) 370-381 Coherence." in 2015 37th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), (2015): IEEE, pp. 3791-94.
25- Claude Bedard, Helmut Kroeger, and Alain Destexhe, "Does the 1/f frequency scaling of brain signals reflect self organized critical states?" Physical review letters, Vol. 97 (No. 11), p. 118102, (2006)
| Files | ||
| Issue | Vol 10 No 3 (2023) | |
| Section | Technical Note | |
| DOI | https://doi.org/10.18502/fbt.v10i3.13165 | |
| Keywords | ||
| Simulated Neuro-Electrical Data Ground-Truth Networks Electroencephalography Simulation Evaluation Brain-Computer Interface | ||
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How to Cite
1.
Yektaeian Vaziri A, Makkiabadi B, Samadzadehaghdam N. EEGg: Generating Synthetic EEG Signals in Matlab Environment. Frontiers Biomed Technol. 2023;10(3):370-381.
