Magnetoencephalographic artifact identification and automatic removal based on independent component analysis and categorization approaches

A K-means multivariate approach for clustering independent components from magnetoencephalographic data

“…In addition, we acknowledge that kurtosis, skewness, entropy and degree of clustering seem to have a lower impact on the final performance in this case. Nevertheless, in agreement with previous work (Rong and Contreras-Vidal, 2006), in which these parameters are used for identifying artefact ICs, we maintain all the global spatial parameters to consider the possibility of incorrectly including artefact ICs in the data.…”

“…In the literature, some methods have been proposed to cluster MEG and EEG independent components (Himberg et al, 2004;Hyvarinen, 2011;Rong and Contreras-Vidal, 2006), and clustering can be applied to isolate artefacts from MEG signals (Rong and Contreras-Vidal, 2006; EEGLAB, http://sccn.ucsd.edu/eeglab). Moreover, clustering is needed also when analyzing the statistical reliability of MEG-independent components across multiple separations on the same session (Himberg et al, 2004) or across sessions and subjects (Hyvarinen, 2011).…”

Simultaneous Scalp Electroencephalography (EEG), Electromyography (EMG), and Whole-body Segmental Inertial Recording for Multi-modal Neural Decoding

“…These technologies are being incorporated into wireless EEG systems which may reduce the effect of motion artifacts 19 . Integration of novel hardware notwithstanding, this protocol provides a unique opportunity to continue developing novel algorithms for motion and EMG artifact rejection because every segment of the body has been instrumented 16 . We plan to study time-series correlation and frequency domain coherence between EEG, EMG, and segment motion to develop robust artifact rejection paradigms applicable to currently available EEG setups.…”

Signal Processing for Non-Invasive Brain Biomarkers of Sensorimotor Performance and Brain Monitoring

“…Recently, several automatic artifact detection and removal methods have been introduced (Delorme et al, 2001;Rong & Contreras-Vidal, 2006). For example, the functionally similar independent components could be automatically categorized using neural network with respect to a set of features such as spatial maps, spectral properties, and higher-order statistics (Rong & Contreras-Vidal, 2006).…”

“…For example, the functionally similar independent components could be automatically categorized using neural network with respect to a set of features such as spatial maps, spectral properties, and higher-order statistics (Rong & Contreras-Vidal, 2006).…”