3D Input Convolutional Neural Network for SSVEP Classification in Design of Brain Computer Interface for Patient User.

This research was aimed at presenting performance of 3-dimensional input convolutional neural networks for steady-state visual evoked potential classification in a wireless EEG-based brain-computer interface system. Overall performance of a brain-computer interface system depends on information transfer rate. Parameters such as signal classification accuracy rate, signal stimulator structure, and user task completion time affect information transfer rate. In this study, we used 3 types of signal classification methods that are 1-dimensional, 2-dimensional, and 3-dimensional input convolutional neural network. According to online experiment with using 3-dimensional input convolutional neural network, we reached average classification accuracy rate and average information transfer rate as 93.75% and 58.35 bit/min, respectively. This both results significantly higher than the other methods that we used in experiments. Moreover, user task completion time was reduced with using 3-dimensional input convolutional neural network. Our proposed method is novel and state-of-art model for steady-state visual evoked potential classification.

Modeling of Compulsive Behavior Types of Obsessive-Compulsive Disorder Patients by Using the Data Mining Method.

Data mining is a method that is used to find data that are precise, previously uncertain, and logical values from a comprehensive set of information. Data mining is used as a tool for determining the accuracy of classifications of data obtained in the field of bioinformatics by using different algorithm approaches. In this study, the data mining method was used to classify the accuracy of different algorithms and predict the types of compulsive behavior of patients with obsessive compulsive disorder. Data collected from a total of 164 people, 70 males and 94 females, were analyzed. The age range of the people participating in the study was between 7 and 73, and the calculated mean age was 32.4. Data about sociodemographic characteristics, course of disease, treatments, family histories, obsession, and compulsion types of the participants were collected through data collection instruments. Classification algorithm methods found in WEKA software were chosen to process the data. The effect of the types of obsession on the types of compulsion was determined using regression models. The levels of success of the generated models were compared. The results of the study demonstrated the presence of a moderate positive correlation (.35) between these two variables. According to the coefficient of determination, obsession explained 11% of the variance in compulsion. These findings supported the established hypothesis that the effect of the types of obsession was effective on the types of compulsion.