Effects of repetitive transcranial magnetic stimulation and transcranial direct current stimulation on posterior alpha wave

Because of the unclear conclusion of the repetitive transcranial magnetic stimulation (rTMS) and transcranial direct current stimulation (tDCS) effects on the posterior electroencephalogram (EEG) alpha wave, this study is aimed at investigating these unclear effects. Transcranial stimulation effects are observed by analyzing a measured EEG at the occipital area between prestimulation and the poststimulation. The EEG alpha power and alpha coherence are calculated and analyzed in terms of the ratio between eyes closed and eyes open periods. The results reveal that alpha power ratio at the individual alpha frequency (IAF) significantly increases after the 1‐Hz rTMS and cathodal tDCS and slightly decreases after the anodal tDCS compared to the control and the sham conditions. The results also show that there is a significant difference between the inhibited and excited conditions. Similarities are observed in the patterns of the alpha coherence ratio and alpha power changes. The alpha coherence increases in the rTMS and cathodal tDCS conditions, and decreases in the anodal tDCS condition but these effects occur only when comparing across the hemispheres (O1–O2 and P3–P4). It can be summarized that the EEG alpha wave can be influenced by the transcranial stimulations. The rTMS and cathodal tDCS seem to facilitate the alpha activity and the anodal tDCS inhibits it. © 2013 Institute of Electrical Engineers of Japan. Published by John Wiley & Sons, Inc.     

Removal of the eye-blink artifacts from EEGs via STF-TS modeling and robust minimum variance beamforming

A novel scheme for the removal of eye-blink (EB) artifacts from electroencephalogram (EEG) signals based on a novel space-time-frequency (STF) model of EEGs and robust minimum variance beamformer (RMVB) is proposed. In this method, in order to remove the artifact, the RMVB is provided with a priori information, namely, an estimation of the steering vector corresponding to the point source EB artifact. The artifact-removed EEGs are subsequently reconstructed by deflation. The a priori knowledge, the vector corresponding to the spatial distribution of the EB factor, is identified using the STF model of EEGs, provided by the parallel factor analysis (PARAFAC) method. In order to reduce the computational complexity present in the estimation of the STF model using the three-way PARAFAC, the time domain is subdivided into a number of segments, and a four-way array is then set to estimate the STF-time/segment (TS) model of the data using the four-way PARAFAC. The correct number of the factors of the STF model is effectively estimated by using a novel core consistency diagnostic- (CORCONDIA-) based measure. Subsequently, the STF-TS model is shown to closely approximate the classic STF model, with significantly lower computational cost. The results confirm that the proposed algorithm effectively identifies and removes the EB artifact from raw EEG measurements.     

Feature selection and classification of high-resolution NMR spectra in the complex wavelet transform domain

Successful identification of the important metabolite features in high-resolution nuclear magnetic resonance (NMR) spectra is a crucial task for the discovery of biomarkers that have the potential for early diagnosis of disease and subsequent monitoring of its progression. Although a number of traditional features extraction/selection methods are available, most of them have been conducted in the original frequency domain and disregarded the fact that an NMR spectrum comprises a number of local bumps and peaks with different scales. In the present study a complex wavelet transform that can handle multiscale information efficiently and has an energy shift-insensitive property is proposed as a method to improve feature extraction and classification in NMR spectra. Furthermore, a multiple testing procedure based on a false discovery rate (FDR) was used to identify important metabolite features in the complex wavelet domain. Experimental results with real NMR spectra showed that classification models constructed with the complex wavelet coefficients selected by the FDR-based procedure yield lower rates of misclassification than models constructed with original features and conventional wavelet coefficients.