Electroencephalography (EEG) is a non-invasive measurement method for brain activity. Due to its safety, high resolution, and hypersensitivity to dynamic changes in brain neural signals, EEG has aroused much interest in scientific research and medical fields. This article reviews the types of EEG signals, multiple EEG signal analysis methods, and the application of relevant methods in the neuroscience field and for diagnosing neurological diseases. First, three types of EEG signals, including time-invariant EEG, accurate event-related EEG, and random event-related EEG, are introduced. Second, five main directions for the methods of EEG analysis, including power spectrum analysis, time–frequency analysis, connectivity analysis, source localization methods, and machine learning methods, are described in the main section, along with different sub-methods and effect evaluations for solving the same problem. Finally, the application scenarios of different EEG analysis methods are emphasized, and the advantages and disadvantages of similar methods are distinguished. This article is expected to assist researchers in selecting suitable EEG analysis methods based on their research objectives, provide references for subsequent research, and summarize current issues and prospects for the future.

Ensemble empirical-mode decomposition (EEMD) is a novel adaptive time-frequency analysis method, which is particularly suitable for extracting useful information from noisy nonlinear or nonstationary data. Unfortunately, since the EEMD is highly compute-intensive, the method does not apply in real-time applications on top of commercial-off-the-shelf computers. Aiming at this problem, a parallelized EEMD method has been developed using general-purpose computing on the graphics processing unit (GPGPU), namely, G-EEMD. A spectral entropy facilitated by G-EEMD was, therefore, proposed to analyze the EEG data for estimating the depth of anesthesia (DoA) in a real-time manner. In terms of EEG data analysis, G-EEMD has dramatically improved the run-time performance by more than 140 times compared to the original serial EEMD implementation. G-EEMD also performs far better than another parallelized implementation of EEMD bases on conventional CPU-based distributed computing technology despite the latter utilizes 16 high-end computing nodes for the same computing task. Furthermore, the results obtained from a pharmacokinetics/pharmacodynamic (PK/PD) model analysis indicate that the EEMD method is slightly more effective than its precedent alternative method (EMD) in estimating DoA, the coefficient of determination R ² by EEMD is significantly higher than that by EMD (p <; 0.05, paired Mest) and the prediction probability P _k by EEMD is also slighter higher than that by EMD (p <; 0.2, paired t-test).

In this survey, we review different text mining techniques to discover various textual patterns from the social networking sites. Social network applications create opportunities to establish interaction among people leading to mutual learning and sharing of valuable knowledge, such as chat, comments, and discussion boards. Data in social networking websites is inherently unstructured and fuzzy in nature. In everyday life conversations, people do not care about the spellings and accurate grammatical construction of a sentence that may lead to different types of ambiguities, such as lexical, syntactic, and semantic. Therefore, analyzing and extracting information patterns from such data sets are more complex. Several surveys have been conducted to analyze different methods for the information extraction. Most of the surveys emphasized on the application of different text mining techniques for unstructured data sets reside in the form of text documents, but do not specifically target the data sets in social networking website. This survey attempts to provide a thorough understanding of different text mining techniques as well as the application of these techniques in the social networking websites. This survey investigates the recent advancement in the field of text analysis and covers two basic approaches of text mining, such as classification and clustering that are widely used for the exploration of the unstructured text available on the Web.

Artifact removal has been an open critical issue for decades in tasks centering on EEG analysis. Recent deep learning methods mark a leap forward from the conventional signal processing routines; however, those in general still suffer from insufficient capabilities 1) to capture potential temporal dependencies embedded in EEG and 2) to adapt to scenarios without a priori knowledge of artifacts. This study proposes an approach (namely DuoCL ) to deep artifact removal with a dual-scale CNN (Convolutional Neural Network)-LSTM (Long Short-Term Memory) model, operating on the raw EEG in three phases: 1) Morphological Feature Extraction , a dual-branch CNN utilizes convolution kernels of two different scales to learn morphological features (individual sample); 2) Feature Reinforcement , the dual-scale features are then reinforced with temporal dependencies (inter-sample) captured by LSTM; and 3) EEG Reconstruction , the resulting feature vectors are finally aggregated to reconstruct the artifact-free EEG via a terminal fully connected layer. Extensive experiments have been performed to compare DuoCL to six state-of-the-art counterparts (e.g., 1D-ResCNN and NovelCNN). DuoCL can reconstruct more accurate waveforms and achieve the highest $SNR$ & correlation ( $CC$ ) as well as the lowest error ( $RRMSE_{t}$ & $RRMSE_{f}$ ). In particular, DuoCL holds potentials in providing a high-quality removal of unknown and hybrid artifacts.

Brain big data empowered by intelligent analysis provide an unrivalled opportunity to probe the dynamics of the brain in disorder. A typical example is to identify evolving synchronization patterns from multivariate electroencephalography (EEG) routinely superimposed with intensive noise in epilepsy research and practice. Under the circumstance of insufficient a priori knowledge of subject dependency on domain problem, it becomes even more important to adaptively classify the synchronization dynamics to accurately characterize the intrinsic nature of seizure activities represented by the EEG. This study first measures the global Maximal Information coefficient (MIC) of all EEG data channels to form a time sequence of correlation matrices. A lightweight VGGNet is designed to adapt to the need to prune massive EEG datasets. The VGGNet characterizes the synchronization dynamics captured in the correlation matrices and then automatically identifies the seizure states of the EEG. Experiments are performed over the CHB-MIT scalp EEG dataset to evaluate the proposed approach. Seizure states can be identified with an accuracy, sensitivity, and specificity of 98.13% ± 0.24%, 98.85% ± 0.51%, and 97.47% ± 0.36%, respectively; the resulting performance is superior to those of most existing methods over the same dataset. The approach directly applies to raw EEG analysis, which holds great potential for handling brain big data.