Computational NeuroscienceAn iterative subspace denoising algorithm for removing electroencephalogram ocular artifacts

- A method for removing ocular artifacts from electroencephalogram signals.
- The results are compared with conventional independent component analysis methods.
- The results are validated on real and simulated data.
- The method contains a genuine methodology for removing significant electrooculogram artifacts without disturbing the background electroencephalogram.
- The method preserves the dimensionality of the original recordings.

BackgroundElectroencephalogram (EEG) measurements are always contaminated by non-cerebral signals, which disturb EEG interpretability. Among the different artifacts, ocular artifacts are the most disturbing ones. In previous studies, limited improvement has been obtained using frequency-based methods. Spatial decomposition methods have shown to be more effective for removing ocular artifacts from EEG recordings. Nevertheless, these methods are not able to completely separate cerebral and ocular signals and commonly eliminate important features of the EEG.New methodIn a previous study we have shown the applicability of a deflation algorithm based on generalized eigenvalue decomposition for separating desired and undesired signal subspaces. In this work, we extend this idea for the automatic detection and removal of electrooculogram (EOG) artifacts from multichannel EEG recordings. The notion of effective number of identifiable dimensions, is also used to estimate the number of dominant dimensions of the ocular subspace, which enables the precise and fast convergence of the algorithm.ResultsThe method is applied on real and synthetic data. It is shown that the method enables the separation of cerebral and ocular signals with minimal interference with cerebral signals.Comparison with existing method(s)The proposed approach is compared with two widely used denoising techniques based on independent component analysis (ICA).ConclusionsIt is shown that the algorithm outperformed ICA-based approaches. Moreover, the method is computationally efficient and is implemented in real-time. Due to its semi-automatic structure and low computational cost, it has broad applications in real-time EEG monitoring systems and brain-computer interface experiments.