The studies implemented with Electroencephalogram (EEG) signals are progressing very rapidly and brain computer interfaces (BCI) and disease determinations are carried out at certain success rates thanks to new methods developed in this field. The effective use of these signals, especially in disease detection, is very important in terms of both time and cost. Currently, in general, EEG studies are used in addition to conventional methods as well as deep learning networks that have recently achieved great success. The most important reason for this is that in conventional methods, increasing classification accuracy is based on too many human efforts as EEG is being processed, obtaining the features is the most important step. This stage is based on both the time-consuming and the investigation of many feature methods. Therefore, there is a need for methods that do not require human effort in this area and can learn the features themselves. Based on that, two-dimensional (2D) frequency-time scalograms were obtained in this study by applying Continuous Wavelet Transform to EEG records containing five different classes. Convolutional Neural Network structure was used to learn the properties of these scalogram images and the classification performance of the structure was compared with the studies in the literature. In order to compare the performance of the proposed method, the data set of the University of Bonn was used. The data set consists of five EEG records containing healthy and epilepsy disease which are labeled as A, B, C, D, and E. In the study, A-E and B-E data sets were classified as 99.50%, A-D and B-D data sets were classified as 100% in binary classifications, A-D-E data sets were 99.00% in triple classification, A-C-D-E data sets were 90.50%, B-C-D-E data sets were 91.50% in quaternary classification, and A-B-C-D-E data sets were in the fifth class classification with an accuracy of 93.60%.

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Emotion plays an important role in human interaction. People can explain their emotions in terms of word, voice intonation, facial expression, and body language. However, brain–computer interface (BCI) systems have not reached the desired level to interpret emotions. Automatic emotion recognition based on BCI systems has been a topic of great research in the last few decades. Electroencephalogram (EEG) signals are one of the most crucial resources for these systems. The main advantage of using EEG signals is that it reflects real emotion and can easily be processed by computer systems. In this study, EEG signals related to positive and negative emotions have been classified with preprocessing of channel selection. Self-Assessment Manikins was used to determine emotional states. We have employed discrete wavelet transform and machine learning techniques such as multilayer perceptron neural network (MLPNN) and k-nearest neighborhood (kNN) algorithm to classify EEG signals. The classifier algorithms were initially used for channel selection. EEG channels for each participant were evaluated separately, and five EEG channels that offered the best classification performance were determined. Thus, final feature vectors were obtained by combining the features of EEG segments belonging to these channels. The final feature vectors with related positive and negative emotions were classified separately using MLPNN and kNN algorithms. The classification performance obtained with both the algorithms are computed and compared. The average overall accuracies were obtained as 77.14 and 72.92% by using MLPNN and kNN, respectively.

The effectiveness of various therapeutic methods on bone fracture has been demonstrated in several studies. In the present study, we tried to evaluate the effect of local low-magnitude, high-frequency vibration (LMHFV) on rat tibia fracture in comparison with pulsed electromagnetic fields (PEMF) during the healing process. Mid-diaphysis tibiae fractures were induced in 30 Sprague-Dawley rats. The rats were assigned into groups such as control (CONT), LMHFV (15 min/day, 7 days/week), and PEMF (3.5 h/day, 7 days/week) for a three-week treatment. Nothing was applied to control group. Radiographs, serum osteocalcin levels, and stereological bone analyses of the three groups were compared. The X-rays of tibiae were taken 21 days after the end of the healing process. PEMF and LMHFV groups had more callus formation when compared to CONT group; however, the difference was not statistically significant (P = 0.375). Serum osteocalcin levels were elevated in the experimental groups compared to CONT (P ≤ 0.001). Stereological tests also showed higher osteogenic results in experimental groups, especially in LMHFV group. The results of the present study suggest that application of direct local LMHFV on fracture has promoted bone formation, showing great potential in improving fracture outcome. Bioelectromagnetics. 38:339-348, 2017. © 2017 Wiley Periodicals, Inc.

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