Independent Component Analysis Technology Research Articles

Facial muscle activity contaminates EEG signal at rest: evidence from frontalis and temporalis motor units

Objective. In order to reach electroencephalography (EEG) electrodes on the scalp, synchronized activity of neurons needs to pass thorough several tissue layers, including the skull and muscles covering the scalp. The contamination of EEG signal by temporalis and frontalis muscles has been well documented for voluntary muscle contraction even at low contraction levels. The extent of myogenic contamination during postural and/or rest activity of the temporalis and frontalis remains an impediment for EEG research. Approach. In this study, we first aimed to observe involuntary, continuous motor unit activity of the frontalis muscle at rest and evaluate motor unit level frontalis interference on the EEG electrodes. Second, we compared motor unit interference from the frontalis before and after artefact pruning via an independent component analysis (ICA) algorithm. Main results. We demonstrated that motor unit activity of the frontalis muscle produces interference potentials on the frontal electrodes at rest and the interference was significantly reduced after ICA on the frontal electrodes, but not completely eliminated. Likewise, the temporalis interference at rest was significantly smaller after ICA on the fronto-temporal electrodes, but not completely removed. Significance. We documented the existence of resting involuntary activity of the temporalis and frontalis muscles underneath EEG electrodes and the removal of the EEG signal from their contiguous interference is not possible even after the use of ICA technology. We recommend that EEG researchers readdress the definition of ‘rest’ for EEG recordings and the ICA experts should extend their electromyography removal strategies to motor unit level interference.

Reverse-Analysis of S-Box for GIFT-Like Algorithms Based on Independent Component Analysis Technology

Reverse-Analysis of S-Box for GIFT-Like Algorithms Based on Independent Component Analysis Technology

Visual evoked functional connectivity before and after smoking cessation based on functional magnetic resonance imaging

To explore differences in visual information processing for smoking addicts between before and after smoking cessation based on functional magnetic resonance imaging technology. Methods: Seventeen volunteers, with heavy dependence on nicotine, agreed to give up smoking and to complete the test. The primary visual network and secondary visual network were extracted by independent component analysis technology. The region of interest was defined as the region with significant difference between before and after smoking cessation. The functional connectivity analysis was carried out based on the selected region of interest. Results: After the subjects quitting smoking for 14 days, the increased functional connectivity was observed not only within secondary visual network but also between 2 interested networks; however, no functional connectivity was changed within primary visual network. Conclusion: The ability of visual information processing for smoking addicts could be significantly enhanced after 14 days of smoking cessation.