Concentration Level Prediction Classification Based On IQ Using Backpropagation ANN T. Henny Febriana Harumy 1, Indri Sulistianingsih2 Faculty of Computer Science Universitas Pembangunan Panca Budi Jl. Jend. Gatot Subroto Km. 4,5 Sei Sikambing, 20122, Medan, Sumatera Utara, Indonesia
Abstract — Intelligence quotient is the figure which describes a person's intelligence level were compared with each other in a population. A classification method with back propagation algorithm can perform classification on the level of concentration of the human brain. This classification is based on IQ components. It is a particular section to record brain activity. This analysis uses five samples used were taken at random to obtain three types of results, low, normal and high. After a process, it generated the output performance of 0.0344 and 0.9854 as the first rank with the highest IQ level. The high IQ level has the concentration of an 88% accuracy rate. Keywords — Backpropagation, IQ, ANN, Classification I. INTRODUCTION For several years, people who are looking for a non-muscular channel between the brain and the outside world so that they can control the device by thinking [1][4]. With the production of the sophisticated bio instruments to record and amplify signals as well as the personal computer easy and powerful. This dream comes true, and Brain Computer Interface (BCI) has been developed. BCI is a type of communication system that translates the brain activity into commands, allowing users to control computer applications specifically or other devices just by her way of thinking. Electrodes acquired the signals from the brain on the scalp and processed to extract specific features which reflect the intentions of the user. These features are then translated into commands that operate application or device. It must be developed and maintained a good correlation between the intentions of the user and features a signal used by BCI. BCI should select and extract the features that the user can control and translate these features into the command device correctly and efficiently. For that, the brain activity to be monitored. In this case, there are various techniques used to achieve it. Some BCI is based on pattern recognition classification approach. In the second method the user must learn to organize themselves to respond EEG yum. For example, by changing the amplitude rhythm. The different components of the EEG signals have been widely shown to have a measurable correlation with the activity of the brain involved in the particular mental tasks. The signal can be decoded in real-time to commands that operate a computer screen or other device. Successful operation requires that encode the user commands in these signals obtained an order from the signal. Thus, users and BCI systems need to adapt to one another both in the beginning and kept so as to ensure stable performance. For this application, BCI can challenge other classical communication devices; they must be reliable, fast, and provide efficient solutions. BCI currently has the maximum information transfer rate of 10-29 bits/min. This limited capacity can be useful for people who are severely disabled to prevent them using conventional augmentative communication methods. However, many possible applications of BCI technology may require a degree of classification accuracy and a higher transfer information. In this paper, the authors propose a method of classification @IJRTER-2016, All Rights Reserved
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