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Kabupaten Nias Selatan

Badan Riset dan Inovasi Nasional

Informasi Dataset

07-11-2022

12-08-2024

11c8cdc3-b6d2-4818-b647-bae9dcbec44d

Dataset Serupa
P300 Extraction using nonlinear adaptive filter for EEG based inter-stimulus int...

In this paper, an application of nonlinear adaptive filter for online P300 extra...

EEG-P300 Extraction using Recursive Principal Component Analysis

By extracting specific brain activity from recorded EEG signals and linking it t...

Extraction of Mental Task in Recorded EEG Signal using ICA-JADE Algorithm

An instrument that has special function to record electrical brain activity in s...

Adaptive Principal Component Analysis based Recursive Least Squares for Artifact...

Artifacts or noise sources increase the difficulty in analyzing the EEG and to o...

Comparison of Immobilized Metal Affinity Chromatography Ni-NT A And Co-TALON For...

The purification of recombinant proteins is an important stage in biopharmaceuti...

INFORMASI: Data berikut ini masih dalam proses pemenuhan Prinsip SDI.

A Comparison of Extraction Techniques for the rapid EEG-P300 Signals

Terbatas

In this paper, three different methods for brain signal acquisition are presented. All methods deal with feature extraction method of Electroencephalogram (EEG) based P300 waves. The performance of the three methods is investigated through backpropagation neural network classifier. EEG-P300 recordings provide an important means of brain-computer communication, but their classification accuracy and transfer rate are limited by unexpected signal variations due to artifacts and noises. A comparison of extraction methods (i.e., AAR, JADE, and SOBI) entailing time-series EEG signals is proposed. Finally, the promising results reported here reflect the considerable potential of EEG for the continuous classification of mental states. Advanced Science Letters, Volume 20, Number 1, January 2014 , pp. 80-85(6)

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