Mustafa Yal?ın/Beyza Kaya/Nihat La?in/Emre Arı: Purpose: The aim of this study was to analyze stress distribution on four different implant designs and four different qualities of cortical bone surrounding the implant crest modules using three-dimensional finite element analysis. Materials and Methods: Four different implant designs of the same diameter and length (3.75 ± 11.5 mm) were used. Rhinoceros 4.0 3D modeling software was used for preparing the four types of bone quality (D1, D2, D3, and D4). Mandibular second premolar crowns were constructed as prosthetic superstructures of the implants, and a total force of 300 N was vertically applied to the crowns. Stress distribution on dental implants and cortical bone of D1, D2, D3, and D4 bone quality was evaluated. Results: Among all of the implants, the maximum von Mises stress value on cortical bone surrounding the neck region of the implants was found in D4 bone quality, while the minimum stress was detected in D1 bone quality. The stress distribution on the threads of dental implants exhibited the most homogeneous distribution in D1 bone quality, while higher von Mises stress values were observed in the apical blade segment of the implants in D3 and D4 bone quality. Conclusion: It was found that the maximum stress of cortical bone surrounding the implant crest module decreased as the bone density increased. The microthreads on the implant crest module might cause an increase in stress to cortical bone surrounding the neck region of implants.

EEG signals are interpreted, analyzed and classified by many researchers for use in brain–computer interfaces. Although there are many different EEG signal acquisition methods, one of the most interesting is motor imagery signals. Many different signal processing methods, machine learning and deep learning models have been developed for the classification of motor imagery signals. Among these, Convolutional Neural Network models generally achieve better results than other models. Because the size and shape of the data is important for training Convolutional Neural Network models and discovering the right relationships, researchers have designed and experimented with many different input shape structures. However, no study has been found in the literature evaluating the effect of different input shapes on model performance and accuracy. In this study, the effects of different input shapes on model performance and accuracy in the classification of EEG motor imagery signals were investigated, which had not been specifically studied before. In addition, signal preprocessing methods, which take a long time before classification, were not used; rather, two CNN models were developed for training and classification using raw data. Two different datasets, BCI Competition IV 2A and 2B, were used in classification processes. For different input shapes, 53.03–89.29% classification accuracy and 2–23 s epoch time were obtained for 2A dataset, 64.84–84.94% classification accuracy and 4–10 s epoch time were obtained for 2B dataset. This study showed that the input shape has a significant effect on the classification performance, and when the correct input shape is selected and the correct CNN architecture is developed, feature extraction and classification can be done well by the CNN architecture without any signal preprocessing.

Block graft,finite element analysis,stress,symphysis