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.

Seizure onset detection and epileptic preictal prediction based on electroencephalogram (EEG) signals have been a challenge problem in the research community. In this brief, a novel epileptic states classification algorithm based on the multichannel EEGs representation using multiple hand-crafted features, the feature fusion and transfer learning (TL) with multiple pre-trained deep neural networks (DNNs), the discriminative feature extraction and epileptic state classification with a hierarchical neural network (HNN), is developed. The mean amplitude spectrum (MAS), mean power spectral density (MPSD) and wavelet packet features (WPFs) are firstly derived and fused into an image feature for multichannel EEGs representation. Then, 5 classical pre-trained DNNs are directly adopted as feature extractors on the fused image feature. A 7-layer fully-connected (FC) HNN is finally constructed for discriminative feature learning and epileptic state classification. The effectiveness is demonstrated through experiments on the CHB-MIT and the iNeuro epilepsy EEG databases.

Epilepsy ictal detection based on scalp electroencephalograms (EEGs) has been comprehensively studied in the past decades. But few attentions have been paid to the preictal classification. In this article, a comprehensive study on epileptic state classification based on deep transfer learning (TL) is presented. The main contributions include: 1) the subband mean amplitude spectrum (MAS) map that characterizes the typical rhythms of brain activities is extracted for EEG representation; 2) five representative deep neural networks (DNNs) pretrained on ImageNet are applied for EEG feature TL; and 3) a 7-layer hierarchical neural network (HNN) that consists of three fully connected (Fc) and three dropout layers followed by a Softmax layer is developed to perform the epileptic state probability learning and classification. Experiments on the benchmark CHB-MIT and iNeuro EEG databases that contain several different types of seizures show that the proposed algorithm achieves the highest overall accuracies of 96.97% and 87.87% on the 5-state epileptic classification, respectively, that outperforms many existing state-of-the-art methods presented in this article.

The scalp electroencephalogram (EEG) has been extensively studied for epileptic signal classification in the past, but little attention has been paid to the data imbalance among different epileptic states. It is well known that, in general, the duration of seizure onset is less than several minutes or even shorter. This will result in an imbalance problem when comparing to the durations of the preictal and interictal states. In this article, a novel epileptic classification and seizure detection algorithm for imbalanced data is proposed. The wavelet packet decomposition (WPD)-based statistical features (SFs) of multichannel EEGs are first extracted for representation. Then, the K-means synthetic minority oversampling technique ( K-means SMOTE) is applied for data balancing. A blending algorithm that consists of random forests (RFs), extremely randomized trees (Extra-Trees), and gradient boosting decision trees (GBDTs) is finally adopted for feature learning and epileptic signal classification. The developed algorithm provides an average accuracy of 89.49% and 83.90% on the Children's Hospital Boston (CHB)-MIT and iNeuro databases, respectively. For the patient-specific classification experiment on the iNeuro database, the proposed algorithm achieves the highest average accuracy of 92.68%.

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.