GDL

Journal from gdlhub / 2021-08-02 19:41:43
Oleh : Shirin Salarian, Amir Shahab Shahabi, ITB
Dibuat : 2021-08-02, dengan 0 file

Keyword : brain-computer interface, EEG signal, P300 component, recurrent neural network, twin support vector machine
Url : http://journals.itb.ac.id/index.php/jictra/article/view/13854
Sumber pengambilan dokumen : Web

The brain-computer interface is considered one of the main tools for implementing and designing smart medical software. The analysis of brain signal data, called EEG, is one of the main tasks of smart medical diagnostic systems. While EEG signals have many components, one of the most important brain activities pursued is the P300 component. Detection of this component can help detect abnormalities and visualize the movement of organs of the body. In this research, a new method for processing EEG signals is proposed with the aim of detecting the P300 component. Major features were extracted from the BCI Competition IV EEG data set in a number of steps, i.e. normalization with the purpose of noise reduction using a median filter, feature extraction using a recurrent neural network, and classification using Twin Support Vector Machine. Then, a series of evaluation criteria were used to validate the proposed approach and compare it with similar methods. The results showed that the proposed approach has high accuracy.

Deskripsi Alternatif :

The brain-computer interface is considered one of the main tools for implementing and designing smart medical software. The analysis of brain signal data, called EEG, is one of the main tasks of smart medical diagnostic systems. While EEG signals have many components, one of the most important brain activities pursued is the P300 component. Detection of this component can help detect abnormalities and visualize the movement of organs of the body. In this research, a new method for processing EEG signals is proposed with the aim of detecting the P300 component. Major features were extracted from the BCI Competition IV EEG data set in a number of steps, i.e. normalization with the purpose of noise reduction using a median filter, feature extraction using a recurrent neural network, and classification using Twin Support Vector Machine. Then, a series of evaluation criteria were used to validate the proposed approach and compare it with similar methods. The results showed that the proposed approach has high accuracy.

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