RESUMO
Simultaneous recording of electroencephalogram (EEG)-functional magnetic resonance imaging (fMRI) plays an important role in scientific research and clinical field due to its high spatial and temporal resolution. However, the fusion results are seriously influenced by ballistocardiogram (BCG) artifacts under MRI environment. In this paper, we improve the off-line constrained independent components analysis using real-time technique (rt-cICA), which is applied to the simulated and real resting-state EEG data. The results show that for simulated data analysis, the value of error in signal amplitude (Er) obtained by rt-cICA method was obviously lower than the traditional methods such as average artifact subtraction ( <0.005). In real EEG data analysis, the improvement of normalized power spectrum (INPS) calculated by rt-cICA method was much higher than other methods ( <0.005). In conclusion, the novel method proposed by this paper lays the technical foundation for further research on the fusion model of EEG-fMRI.
RESUMO
@# Neurofeedback may improve cognitive function and behaviour by regulating the cerebral neural activities. Neurofeedback works as a kind of therapy for patients with Parkinson's disease, primarily based on electroencephalogram signals and hemodynamic signals. It regulates abnormal neuronal rhythm oscillations in the cortical-spinal system by electroencephalogram neurofeedback and sensory motor rhythm as the main adjustment frequency band, and modulates activation in the cortical-basal ganglia-thalamic-cortical network of brain with the supplementary motor area as the target area via functional magnetic resonance imaging neurofeedback. Neurofeedback effectively improves the motor symptoms of Parkinson's patients, both dynamic and static. However, the number of samples is limited in these researches, and the assessments before and after neurofeedback training are not yet comprehensive. Neurofeedback technology might be a potential effective rehabilitation for patients with Parkinson's disease.