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All Journal Mechatronics, Electrical Power, and Vehicular Technology Fibusi (Jurnal Online Fisika) ELKOMIKA: Jurnal Teknik Energi Elektrik, Teknik Telekomunikasi, & Teknik Elektronika Teknologi Indonesia Journal of Applied Data Sciences Automotive Experiences Indonesian Journal of Electronics, Electromedical Engineering, and Medical Informatics
Arjon Turnip
Technical Implementation Unit For Instrumentation Development, Indonesian Institute Of Sciences
Aerospace Engineering Automotive Engineering Chemical Engineering, Chemistry & Bioengineering Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Electrical & Electronics Engineering Energy Engineering Health Professions Materials Science & Nanotechnology Mechanical Engineering Physics

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Journal : Mechatronics, Electrical Power, and Vehicular Technology

 1 
An Experiment of Ocular Artifacts Elimination from EEG Signals using ICA and PCA Methods Turnip, Arjon; R. Setiawan, Iwan; Junaidi, Edy; Nguyen, Le Hoa
Journal of Mechatronics, Electrical Power and Vehicular Technology Vol 5, No 2 (2014)
Publisher : Research Centre for Electrical Power and Mechatronics, Indonesian Istitutes of Sciences

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (1785.199 KB) | DOI: 10.14203/j.mev.2014.v5.129-138

Abstract

In the modern world of automation, biological signals, especially Electroencephalogram (EEG) is gaining wide attention as a source of biometric information. Eye-blinks and movement of the eyeballs produce electrical signals (contaminate the EEG signals) that are collectively known as ocular artifacts. These noise signals are required to be separated from the EEG signals to obtain the accurate results. This paper reports an experiment of ocular artifacts elimination from EEG signal using blind source separation algorithm based on independent component analysis and principal component analysis. EEG signals are recorded on three conditions, which are normal conditions, closed eyes, and blinked eyes. After processing, the dominant frequency of EEG signals in the range of 12-14 Hz either on normal, closed, and blinked eyes conditions is obtained. 
Autoregressive Integrated Adaptive Neural Networks Classifier for EEG-P300 Classification Soetraprawata, Demi; Turnip, Arjon
Journal of Mechatronics, Electrical Power and Vehicular Technology Vol 4, No 1 (2013)
Publisher : Research Centre for Electrical Power and Mechatronics, Indonesian Istitutes of Sciences

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (494.761 KB) | DOI: 10.14203/j.mev.2013.v4.1-8

Abstract

Brain Computer Interface has a potency to be applied in mechatronics apparatus and vehicles in the future. Compared to the other techniques, EEG is the most preferred for BCI designs. In this paper, a new adaptive neural network classifier of different mental activities from EEG-based P300 signals is proposed. To overcome the over-training that is caused by noisy and non-stationary data, the EEG signals are filtered and extracted using autoregressive models before passed to the adaptive neural networks classifier. To test the improvement in the EEG classification performance with the proposed method, comparative experiments were conducted using Bayesian Linear Discriminant Analysis. The experiment results show that the all subjects achieve a classification accuracy of 100%.
The Performance of EEG-P300 Classification using Backpropagation Neural Networks Turnip, Arjon; Soetraprawata, Demi
Journal of Mechatronics, Electrical Power and Vehicular Technology Vol 4, No 2 (2013)
Publisher : Research Centre for Electrical Power and Mechatronics, Indonesian Istitutes of Sciences

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (276.701 KB) | DOI: 10.14203/j.mev.2013.v4.81-88

Abstract

Electroencephalogram (EEG) recordings signal provide an important function of brain-computer communication, but the accuracy of their classification is very limited in unforeseeable signal variations relating to artifacts. In this paper, we propose a classification method entailing time-series EEG-P300 signals using backpropagation neural networks to predict the qualitative properties of a subject’s mental tasks by extracting useful information from the highly multivariate non-invasive recordings of brain activity. To test the improvement in the EEG-P300 classification performance (i.e., classification accuracy and transfer rate) with the proposed method, comparative experiments were conducted using Bayesian Linear Discriminant Analysis (BLDA). Finally, the result of the experiment showed that the average of the classification accuracy was 97% and the maximum improvement of the average transfer rate is 42.4%, indicating the considerable potential of the using of EEG-P300 for the continuous classification of mental tasks.
An Experiment of Ocular Artifacts Elimination from EEG Signals using ICA and PCA Methods Arjon Turnip; Iwan R. Setiawan; Edy Junaidi; Le Hoa Nguyen
Journal of Mechatronics, Electrical Power and Vehicular Technology Vol 5, No 2 (2014)
Publisher : National Research and Innovation Agency

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.14203/j.mev.2014.v5.129-138

Abstract

In the modern world of automation, biological signals, especially Electroencephalogram (EEG) is gaining wide attention as a source of biometric information. Eye-blinks and movement of the eyeballs produce electrical signals (contaminate the EEG signals) that are collectively known as ocular artifacts. These noise signals are required to be separated from the EEG signals to obtain the accurate results. This paper reports an experiment of ocular artifacts elimination from EEG signal using blind source separation algorithm based on independent component analysis and principal component analysis. EEG signals are recorded on three conditions, which are normal conditions, closed eyes, and blinked eyes. After processing, the dominant frequency of EEG signals in the range of 12-14 Hz either on normal, closed, and blinked eyes conditions is obtained. 
Autoregressive Integrated Adaptive Neural Networks Classifier for EEG-P300 Classification Demi Soetraprawata; Arjon Turnip
Journal of Mechatronics, Electrical Power and Vehicular Technology Vol 4, No 1 (2013)
Publisher : National Research and Innovation Agency

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.14203/j.mev.2013.v4.1-8

Abstract

Brain Computer Interface has a potency to be applied in mechatronics apparatus and vehicles in the future. Compared to the other techniques, EEG is the most preferred for BCI designs. In this paper, a new adaptive neural network classifier of different mental activities from EEG-based P300 signals is proposed. To overcome the over-training that is caused by noisy and non-stationary data, the EEG signals are filtered and extracted using autoregressive models before passed to the adaptive neural networks classifier. To test the improvement in the EEG classification performance with the proposed method, comparative experiments were conducted using Bayesian Linear Discriminant Analysis. The experiment results show that the all subjects achieve a classification accuracy of 100%.
The Performance of EEG-P300 Classification using Backpropagation Neural Networks Arjon Turnip; Demi Soetraprawata
Journal of Mechatronics, Electrical Power and Vehicular Technology Vol 4, No 2 (2013)
Publisher : National Research and Innovation Agency

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.14203/j.mev.2013.v4.81-88

Abstract

Electroencephalogram (EEG) recordings signal provide an important function of brain-computer communication, but the accuracy of their classification is very limited in unforeseeable signal variations relating to artifacts. In this paper, we propose a classification method entailing time-series EEG-P300 signals using backpropagation neural networks to predict the qualitative properties of a subject’s mental tasks by extracting useful information from the highly multivariate non-invasive recordings of brain activity. To test the improvement in the EEG-P300 classification performance (i.e., classification accuracy and transfer rate) with the proposed method, comparative experiments were conducted using Bayesian Linear Discriminant Analysis (BLDA). Finally, the result of the experiment showed that the average of the classification accuracy was 97% and the maximum improvement of the average transfer rate is 42.4%, indicating the considerable potential of the using of EEG-P300 for the continuous classification of mental tasks.
Co-Authors Aditya Rio Prabowo Amri, M Faizal Antonius Suhartomo Aris Munandar Bagus Kumbara, Bagus Danasasmita, Febrianti Santiardi Darmawan, Farhan Demi Soetraprawata Demi Soetraprawata Demi Soetraprawata Edy Junaidi Edy Junaidi Efendi, Syahril Endra Joelianto Erwin Sitompul Hanandita, Hanif Setya Hariyadi , Herman Mawengkang Hilman Syaeful Alam Iwan R. Setiawan Iwan R. Setiawan Kusumandari, Dwi Esti Kusumandari, Dwi Esti Le Hoa Nguyen Le Hoa Nguyen Lenggana, Bhre Wangsa Nadiya, Ulfah Nendi Suhendi Syafei Poltak Sihombing Rahmawati, Annida Shelly Iskandar Simbolon, Artha Ivonita Siti Aminah Sobana Ubaidillah Ubaidillah Waslaluddin MT Weber, Gerhard Wilhelm Wibowo, Jony Winaryo