Klasifikasi Aktivitas Mental Dari Data Eeg Menggunakan Neural Network Dan Fuzzy Particle Swarm Optimization Dengan Cross-Mutated Operator - Classification Of Mental Activities From Eeg Data Using Neural Network And Fuzzy Particle Swarm Optimization With Cross-Mutated Operator

Sakur, Stendy B (2015) Klasifikasi Aktivitas Mental Dari Data Eeg Menggunakan Neural Network Dan Fuzzy Particle Swarm Optimization Dengan Cross-Mutated Operator - Classification Of Mental Activities From Eeg Data Using Neural Network And Fuzzy Particle Swarm Optimization With Cross-Mutated Operator. Masters thesis, Institut Teknologi Sepuluh Nopember.

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Abstract

Brain-computer interface (BCI) is a system that can transform electrical activities in the brain to different mental activities into the control signals. Electroencephalogram (EEG) based BCI is the result of the measurement of the electrical activity of the brain waves generated by the cerebral cortex. In general, a neural network is widely used as a method of multi-class classification, but very slow in reaching convergence or the solution can be trapped in a local minimum. Evolutionary algorithm is proposed to optimize the search weights of the neural network, including the Particle Swarm Optimization (PSO). The fundamental issue is the speed of PSO is less stable that need improvement in all components of the particle. The component consists of the weight of the inertia of the previous speed, knowledge of individual and group knowledge. The component has a great influence in achieving the level of convergence, so it is important to note at the time of optimization weighting. The purpose of this study is to propose a new strategy on methods Improved Particle Swarm Optimization (IPSO) which uses Modified Evolutionary Direction Operator with Adaptive Inertia weights. Where Fuzzy Inference System is used as adaptive inertia weight in optimizing the weighting of Neural Network. The strategy is expected to improve the accuracy. The results show, for classification first subject is 54.20%, second subject of 58.40% and 50.80% for subject third with the average accuracy of 54.48%. Where the increase for first subject is 1.26%, second subject is 12.63% and the third subject 0.80%. Thus the proposed method is Fuzzy Modified Evolutionary Direction operator with cross-mutated operation is better than the previous method. ======================================================================================================================== Brain-computer interface (BCI) is a system that can transform electrical activities in the brain to different mental activities into the control signals. Electroencephalogram (EEG) based BCI is the result of the measurement of the electrical activity of the brain waves generated by the cerebral cortex. In general, a neural network is widely used as a method of multi-class classification, but very slow in reaching convergence or the solution can be trapped in a local minimum. Evolutionary algorithm is proposed to optimize the search weights of the neural network, including the Particle Swarm Optimization (PSO). The fundamental issue is the speed of PSO is less stable that need improvement in all components of the particle. The component consists of the weight of the inertia of the previous speed, knowledge of individual and group knowledge. The component has a great influence in achieving the level of convergence, so it is important to note at the time of optimization weighting. The purpose of this study is to propose a new strategy on methods Improved Particle Swarm Optimization (IPSO) which uses Modified Evolutionary Direction Operator with Adaptive Inertia weights. Where Fuzzy Inference System is used as adaptive inertia weight in optimizing the weighting of Neural Network. The strategy is expected to improve the accuracy. The results show, for classification first subject is 54.20%, second subject of 58.40% and 50.80% for subject third with the average accuracy of 54.48%. Where the increase for first subject is 1.26%, second subject is 12.63% and the third subject 0.80%. Thus the proposed method is Fuzzy Modified Evolutionary Direction operator with cross-mutated operation is better than the previous method.

Item Type: Thesis (Masters)
Additional Information: RTIf 621.398 1 Sak k
Uncontrolled Keywords: Brain-computer interface, Electroencephalogram, Artificial Neural Network, Particle Swarm Optimization, Fuzzy Inertia Weight, Evolutionary Direction Operator, Cross-mutated Operation, Brain-computer interfaces, Electroencephalogram, arificial Neural Network, Particle Swarm Optimization, Fuzzy Inertia Weight, Evolutionary Direction Operator, Cross-mutated operation
Subjects: Q Science > QP Physiology > Q376.5 Electroencephalography (EEG)
Divisions: Faculty of Information Technology > Informatics Engineering
Depositing User: ansi aflacha
Date Deposited: 21 Nov 2019 09:00
Last Modified: 21 Nov 2019 09:00
URI: http://repository.its.ac.id/id/eprint/71958

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