Data selection in EEG signals classification

The alcoholism can be detected by analyzing electroencephalogram (EEG) signals. However, analyzing multi-channel EEG signals is a challenging task, which often requires complicated calculations and long execution time. This paper proposes three data selection methods to extract representative data from the EEG signals of alcoholics. The methods are the principal component analysis based on graph entropy (PCA-GE), the channel selection based on graph entropy (GE) difference, and the mathematic combinations channel selection, respectively. For comparison purposes, the selected data from the three methods are then classified by three classifiers: the J48 decision tree, the K-nearest neighbor and the Kstar, separately. The experimental results show that the proposed methods are successful in selecting data without compromising the classification accuracy in discriminating the EEG signals from alcoholics and non-alcoholics. Among them, the proposed PCA-GE method uses only 29.69 % of the whole data and 29.5 % of the computation time but achieves a 94.5 % classification accuracy. The channel selection method based on the GE difference also gains a 91.67 % classification accuracy by using only 29.69 % of the full size of the original data. Using as little data as possible without sacrificing the final classification accuracy is useful for online EEG analysis and classification application design

Analysis of epileptic EEG signals with simple random sampling J48 algorithm

This paper describes the application of a Simple Random Sampling J48 (SRS-J48) model for classification of electroencephalogram (EEG) signals. Decision making is performed in two stages: feature extraction and classification. Eight statistical features are extracted from a two-level sample set model based on SRS technique and then classified by the J48 decision tree algorithm in Weka. The classification accuracy of the SRS-J48 is 16.35% higher than that of J48 according to the five groups of experiment with only 13% execution time on average. Besides, the proposed SRS-J48 algorithm has competitive or even better results on some of the experimental groups than Siuly’s Simple Random Sampling-Least Square-Support Vector Machine (SRS-LS-SVM)

A thermography-based method for fatigue behavior evaluation of coupling beam damper

Under cyclic load, local fatigue damage will occur in the metal damper widely used in the shear wall. This will deteriorate the stiffness of damper and weaken the hysteresis behaviour. The present paper proposed a new and easy method to manufacture kinds of coupling beam dampers. A thermography-based experiment was used to study the energy dissipation and damage accumulation during fatigue process of the metal damper. Based on the temperature variation related to fatigue damage process, the relationship between the plastic deformation and thermal energy dissipation was quantitatively established. Besides, the relationships between the temperature increase to damage accumulation and mechanical load were analyzed systematically

Epileptogenic focus detection in intracranial EEG based on delay permutation entropy

Epileptogenic localization is a critical factor for successful epilepsy surgery. Determining the epileptogenic hippocampus with single channel intracranial electroencephalography (iEEG) recording is beneficial to decrease the risk of infection compared with that based on multi-channel iEEGs. Delay permutation entropy (DPE) methodology is presented in this study to measure iEEG with different delay lag based on focal epileptogenic zone. A total of 1600 20-s epileptic iEEG are evaluated and are used as features to classify epileptogenic and non-epileptogenic zone. Experimental results show that the DPE index of epileptogenic iEEG is significant lower than that of non-epileptogenic hemisphere when delay lag ranges from 5 to 30 (p=0.01). In addition, the accuracy of identifying epileptogenic region with the DPE index is increased when the delay lag between 5 and 25, compared to the performance of the PE index

Corrosion Monitoring and Evaluation of Reinforced Concrete Structures Utilizing the Ultrasonic Guided Wave Technique

Corrosion of reinforced concrete structures has become a major problem worldwide, leading to very high repair costs. A dearth of studies has focused on the corrosion damage evolution of reinforced concrete. In this paper, the ultrasonic guided wave (UGW) technique is adopted to monitor the reinforced concrete corrosion damage evolution process. The properties of different guide wave modes were studied by steel rebar dispersion curves of UGWs through numerical calculation. The availability and validity of the UGW testing-reinforced concrete corrosion damage is proved by corrosion experiment. The experiment shows that the first wave peak value could describe the whole process of steel rebar corrosion. As the corrosion damage level increases, the relative variation for the first UGW peak value increases first and then decreases

Optimization Design of Coupling Beam Metal Damper in Shear Wall Structures

The coupling beam damper is a fundamental energy dissipation component in coupling shear wall structures that directly influences the performance of the shear wall. Here, we proposed a two-fold design method that can give better energy dissipation performance and hysteretic behavior to coupling beam dampers. First, we devised four in-plane yielding coupling beam dampers that have different opening types but the same amount of total materials. Then the geometry parameters of each opening type were optimized to yield the maximum hysteretic energy. The search for the optimal parameter set was realized by implementing the Kriging surrogate model which iterates randomly selected input shape parameters and the corresponding hysteretic energy calculated by the infinite element method. By comparing the maximum hysteretic energy in all four opening types, one type that had the highest hysteresis energy was selected as the optimized design. This optimized damper has the advantages of having a simple geometry and a high dissipation energy performance. The proposed method also provided a new framework for the design of in-plane coupling beam dampers

Analyzing EEG signals using graph entropy based principle component analysis and J48 decision tree

This paper proposed a method using principle component analysis based on graph entropy (PCA-GE) and J48 decision tree on electroencephalogram (EEG) signals to predict whether a person is alcoholic or not. Analysis is performed in two stages: feature extraction and classification. The principle component analysis (PCA) chooses the optimal subset of channels based on graph entropy technique and the selected subset is classified by the J48 decision tree in Weka. K-nearest neighbor (KNN) and support vector machine (SVM) in R package are also used for comparison. Experimental results show that the proposed PCA-GE method is successful in selecting a subset of channels, which contributes to the high accuracy and efficiency in the classification of alcoholics and non-alcoholics

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