Suitable ICA algorithm for extracting saccade-related EEG signals

Arao Funase, Motoaki Mouri, Andrzej Cichocki, Ichi Takumi

Результат исследований: Глава в книге, отчете, сборнике статейМатериалы для конференциирецензирование

1 Цитирования (Scopus)


Our goal is to develop a novel BCI based on an eye movements system employing EEG signals on-line. Most of the analysis on EEG signals has been performed using ensemble averaging approaches. However,It is suitable to analyze raw EEG signals in signal processing methods for BCI. In order to process raw EEG signals, we used independent component analysis(ICA). However, we do not know which ICA algorithms have good performance. It is important to check which ICA algorithms have good performance to develop BCIs. Previous paper presented extraction rate of saccade-related EEG signals by five ICA algorithms and eight window size. However, three ICA algorithms, the FastICA, the NG-FICA and the JADE algorithms, are based on 4th order statistic and AMUSE algorithm has an improved algorithm named SOBI.Therefore, we must re-select ICA algorithms. In this paper, we add new algorithms; the SOBI and the MILCA. The SOBI is an improved algorithm based on the AMUSE and uses at least two covariance matrices at different time steps. The MILCA use the independency based on mutual information. Using the Fast ICA, the JADE, the AMUSE, the SOBI, and the MILCA, we extract saccade-related EEG signals and check extracting rates. Secondly, in order to get more robustness against EOG noise, we use improved FastICA with reference signals and check extracting rates.

Язык оригиналаАнглийский
Название основной публикацииNeural Information Processing - 16th International Conference, ICONIP 2009, Proceedings
Число страниц8
ИзданиеPART 1
СостояниеОпубликовано - 2009
Опубликовано для внешнего пользованияДа
Событие16th International Conference on Neural Information Processing, ICONIP 2009 - Bangkok, Таиланд
Продолжительность: 1 дек. 20095 дек. 2009

Серия публикаций

НазваниеLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
НомерPART 1
Том5863 LNCS
ISSN (печатное издание)0302-9743
ISSN (электронное издание)1611-3349


Конференция16th International Conference on Neural Information Processing, ICONIP 2009


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