Salarian Shirin Primary Author mixed material bibliography Bandung ITB Journal Publisher 2021 e English

Artikel Jurnal

hlm : 71-88 Abstract 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. Ilmu Teknik JICTRA 23375787 Perpustakaan Teknik UPI YAI JICTRA V15N1 June 2021 JICTRA4a-005 Perpustakaan FT UPI YAI JICTRA V15N1 June 2021 JICTRA4b-005 Perpustakaan FT UPI YAI JICTRA V15N1 June 2021 JICTRA4c-005 Perpustakaan FT UPI YAI JICTRA V15N1 June 2021 JICTRA_ITB_small.png.png 59051 2023-02-13 15:47:53 2023-02-13 15:47:53 machine generated