Abstract
The electroencephalograph (EEG) source imaging (ESI) method is a non-invasive method that provides high temporal resolution imaging of brain electrical activity on the cortex. However, because the accuracy of EEG source imaging is often affected by unwanted signals such as noise or other source-irrelevant signals, the results of ESI are often incongruous with the real sources of brain activities. This study presents a novel ESI method (WPESI) that is based on wavelet packet transform (WPT) and subspace component selection to image the cerebral activities of EEG signals on the cortex. First, the original EEG signals are decomposed into several subspace components by WPT. Second, the subspaces associated with brain sources are selected and the relevant signals are reconstructed by WPT. Finally, the current density distribution in the cerebral cortex is obtained by establishing a boundary element model (BEM) from head MRI and applying the appropriate inverse calculation. In this study, the localization results obtained by this proposed approach were better than those of the original sLORETA approach (OESI) in the computer simulations and visual evoked potential (VEP) experiments. For epilepsy patients, the activity sources estimated by this proposed algorithm conformed to the seizure onset zones. The WPESI approach is easy to implement achieved favorable accuracy in terms of EEG source imaging. This demonstrates the potential for use of the WPESI algorithm to localize epileptogenic foci from scalp EEG signals.
Highlights
S CALP electroencephalograph (EEG) is a non-invasive method to record brain electrical activity from large cohorts of neurons in the cerebral cortex [1], [2]
These results indicate that the WPESI algorithm can be used in poor EEG experimental environments, rather than the OESI method
The localization error of the WPESI method was similar for the 32, 64 and 128 channels of signals, while these montages had significantly lower localization errors than the 19 channels of signals. These results indicate that the WPESI algorithm is hardly affected by the number of channels when the number of channels exceeds a certain value
Summary
S CALP electroencephalograph (EEG) is a non-invasive method to record brain electrical activity from large cohorts of neurons in the cerebral cortex [1], [2]. In addition to EEG, the neural activity of the brain can be explored using many other noninvasive imaging methods, including functional magnetic resonance imaging (fMRI), functional near-infrared spectroscopy (fNIRs), single-photon emission computed tomography (SPECT) and positron emission tomography (PET). These functional imaging methods provide desirable spatial resolution, but fail to capture the real-time neural activities in the cerebral cortex. As long as scalp EEG measurements are available, ESI can estimate brain electrical activity with high temporal resolution by solving the so-called EEG inverse problem
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