EEGg: Generating Synthetic EEG Signals in Matlab Environment

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

A novel underdetermined source recovery algorithm based on k-sparse component analysis

Sparse component analysis (SCA) is a popular method for addressing underdetermined blind source separation in array signal processing applications. We are motivated by problems that arise in the applications where the sources are densely sparse (i.e. the number of active sources is high and very close to the number of sensors). The separation performance of current underdetermined source recovery (USR) solutions, including the relaxation and greedy families, reduces with decreasing the mixing system dimension and increasing the sparsity level (k). In this paper, we present a k-SCA-based algorithm that is suitable for USR in low-dimensional mixing systems. Assuming the sources is at most (m−1) sparse where m is the number of mixtures; the proposed method is capable of recovering the sources from the mixtures given the mixing matrix using a subspace detection framework. Simulation results show that the proposed algorithm achieves better separation performance in k-SCA conditions compared to state-of-the-art USR algorithms such as basis pursuit, minimizing norm-L1, smoothed L0, focal underdetermined system solver and orthogonal matching pursuit