Electrical Dipole Source Localization using Hybrid Least Squares Method in combination with ICA

In biomedical engineering, dipole source localization is commonly used to identify brain activities from scalp recorded potentials, which is known as inverse problem of electroencephalography (EEG) source localization. However, this problem is fundamental in biomedical engineering, medicine and neuroscience. The EEG inverse problem is non-linear, in addition, it is ill-posed and the solver can be unstable, i.e. the solution is non-unique and it is highly sensitive to small changes of the measured signal (noise). For solving the EEG inverse problem iterative methods, like Levenberg-Marquardt algorithm, are usually considered. However, these techniques require good initial values and many electrodes N, since a large redundancy supports the finding of the right solution. Therefore, in this paper, a hybrid method of linear and non-linear modelling and least squares approach are proposed to overcome of these problems: the solutions calculated by means of a linear approximation of EEG inverse problems serve as initial values for solving the original non-linear model. In addition, independent component analysis (ICA) is combined with the proposed hybrid least squares method to separate different dipole sources from multiple EEG signals. The performance of the hybrid least squares method with and without ICA is measured in term of root mean square error. The simulation results show that the proposed method can estimate the location of dipole source with acceptable accuracy under high noise condition and small N comparing with linear least squares method considering larger N. Finally, it should be mentioned that the proposed method promises advantages in finding solutions of the EEG inverse problem effectively.

Modulation of the Visual to Auditory Human Inhibitory Brain Network: An EEG Dipole Source Localization Study

Auditory alarms are used to direct people’s attention to critical events in complicated environments. The capacity for identifying the auditory alarms in order to take the right action in our daily life is critical. In this work, we investigate how auditory alarms affect the neural networks of human inhibition. We used a famous stop-signal or go/no-go task to measure the effect of visual stimuli and auditory alarms on the human brain. In this experiment, go-trials used visual stimulation, via a square or circle symbol, and stop trials used auditory stimulation, via an auditory alarm. Electroencephalography (EEG) signals from twelve subjects were acquired and analyzed using an advanced EEG dipole source localization method via independent component analysis (ICA) and EEG-coherence analysis. Behaviorally, the visual stimulus elicited a significantly higher accuracy rate (96.35%) than the auditory stimulus (57.07%) during inhibitory control. EEG theta and beta band power increases in the right middle frontal gyrus (rMFG) were associated with human inhibitory control. In addition, delta, theta, alpha, and beta band increases in the right cingulate gyrus (rCG) and delta band increases in both right superior temporal gyrus (rSTG) and left superior temporal gyrus (lSTG) were associated with the network changes induced by auditory alarms. We further observed that theta-alpha and beta bands between lSTG-rMFG and lSTG-rSTG pathways had higher connectivity magnitudes in the brain network when performing the visual tasks changed to receiving the auditory alarms. These findings could be useful for further understanding the human brain in realistic environments.