Eye blink is one of the most common artifacts in electroencephalogram (EEG) and significantly affects the performance of the EEG related applications, such as epilepsy recognition, spike detection, encephalitis diagnosis, etc. To achieve an accurate and efficient eye blink detection, a novel unsupervised learning algorithm based on a hybrid thresholding followed with a Gaussian mixture model (GMM) is presented in this paper. The EEG signal is priliminarily screened by a cascaded thresholding method built on the distributions of signal amplitude, amplitude displacement, as well as the cross channel correlation. Then, the channel correlation of the two frontal electrodes (FP1, FP2), the fractal dimension, and the mean of amplitude difference between FP1 and FP2, are extracted to characterize the filtered EEGs. The GMM trained on these features is applied for the eye blink detection. The performance of the proposed algorithm is studied on two EEG datasets collected by the Temple University Hospital (TUH) and the Children's Hospital, Zhejiang University School of Medicine (CHZU), where the datasets are recorded from epilepsy and encephalitis patients, and contain a lot of eye blink artifacts. Experimental results show that the proposed algorithm can achieve the highest detection precision and F1 score over the state-of-the-art methods.

The benign epilepsy with spinous waves in the central temporal region (BECT) is the one of the most common epileptic syndromes in children, that seriously threaten the nervous system development of children. The most obvious feature of BECT is the existence of a large number of electroencephalogram (EEG) spikes in the Rolandic area during the interictal period, that is an important basis to assist neurologists in BECT diagnosis. With this regard, the paper proposes a novel BECT spike detection algorithm based on time domain EEG sequence features and the long short-term memory (LSTM) neural network. Three time domain sequence features, that can obviously characterize the spikes of BECT, are extracted for EEG representation. The synthetic minority oversampling technique (SMOTE) is applied to address the spike imbalance issue in EEGs, and the bi-directional LSTM (BiLSTM) is trained for spike detection. The algorithm is evaluated using the EEG data of 15 BECT patients recorded from the Children's Hospital, Zhejiang University School of Medicine (CHZU). The experiment shows that the proposed algorithm can obtained an average of 88.54% F1 score, 92.04% sensitivity, and 85.75% precision, that generally outperforms several state-of-the-art spike detection methods.

Accurate eye blink artifact detection is essential for electroencephalogram (EEG) analysis and auxiliary analysis of nervous system diseases, especially in the presence of the frontal epileptiform discharges. In this paper, we develop a novel eye blink artifact detection algorithm based on optimally selected multi-dimensional EEG features. Specific efforts have been paid to filtering the frontal epileptiform discharges, where an unsupervised learning exploiting the EEG signal physiological characteristics and smooth nonlinear energy operator (SNEO) based on the K-means clustering has been firstly proposed. Multiple statistical EEG features derived from the frontal electrodes and other electrodes are then extracted to characterize eye blink artifacts. Discriminative feature selection scheme based on the variance filtering and Relief algorithms has been respectively studied, and the average correlation coefficient (ACC) is applied for feature optimization evaluation. The eye blink artifact detection is finally achieved based on the support vector machine (SVM) trained on the optimized EEG features. The effectiveness of the proposed algorithm is demonstrated by experiments carried out on the EEG database of 11 subjects recorded from the Children’s Hospital, Zhejiang University School of Medicine (CHZU). Comparisons to several state-of-the-art (SOTA) eye blink artifact detection methods are also presented.

Rett syndrome (RTT) is a neurodevelopmental disease in children that is mainly caused by mutations in the MeCP2 gene, which codes for a transcriptional regulator. The expression of insulin-like growth factor-1 (IGF-1) is reduced in RTT patients and animal models, and IGF-1 treatment is a promising therapeutic strategy for RTT. However, the mechanism underlying the effects of IGF-1 remains to be further explored. FXYD1 is an auxiliary subunit of Na, K-ATPase. Overexpression of FXYD1 is involved in the pathogenesis of RTT. However, whether IGF-1 exerts its effect through normalizing FXYD1 is completely unknown. To this end, we evaluated the effect of IGF-1 on FXYD1 expression and posttranslational modification in a mouse model of RTT (MeCP2³⁰⁸) using both in vitro and in vivo experiments. The results show that FXYD1 mRNA and phosphorylated protein (p-FXYD1) were significantly elevated in the frontal cortex in RTT mice, compared to wild type. In RTT mice, IGF-1 treatment significantly reduced levels of FXYD1 mRNA and p-FXYD1, in parallel with improvements in behavior, motor coordination, and cognitive function. For mechanistic insight into the effect of IGF-1 on p-FXYD1, we found the decreased phosphorylated forms of PI3K-AKT-mTOR signaling pathway components in the frontal cortex of RTT mice and the normalizing effect of IGF-1 on the phosphorylated forms of these components. Interestingly, blocking the PI3K/AKT pathway by PI3K inhibitor could abolish the effect of IGF-1 on p-FXYD1 level, in addition to the effect of IGF-1 on the phosphorylation of other components in the PI3K/AKT pathway. Thus, our study has provided new insights into the mechanism of IGF-1 treatment for RTT, which appears to involve FXYD1.