PMID- 21939560
OWN - NLM
STAT- MEDLINE
DCOM- 20120119
LR  - 20211020
IS  - 1475-925X (Electronic)
IS  - 1475-925X (Linking)
VI  - 10
DP  - 2011 Sep 23
TI  - Real-time feature extraction of P300 component using adaptive nonlinear principal 
      component analysis.
PG  - 83
LID - 10.1186/1475-925X-10-83 [doi]
AB  - BACKGROUND: The electroencephalography (EEG) signals are known to involve the 
      firings of neurons in the brain. The P300 wave is a high potential caused by an 
      event-related stimulus. The detection of P300s included in the measured EEG 
      signals is widely investigated. The difficulties in detecting them are that they 
      are mixed with other signals generated over a large brain area and their 
      amplitudes are very small due to the distance and resistivity differences in 
      their transmittance. METHODS: A novel real-time feature extraction method for 
      detecting P300 waves by combining an adaptive nonlinear principal component 
      analysis (ANPCA) and a multilayer neural network is proposed. The measured EEG 
      signals are first filtered using a sixth-order band-pass filter with cut-off 
      frequencies of 1 Hz and 12 Hz. The proposed ANPCA scheme consists of four steps: 
      pre-separation, whitening, separation, and estimation. In the experiment, four 
      different inter-stimulus intervals (ISIs) are utilized: 325 ms, 350 ms, 375 ms, 
      and 400 ms. RESULTS: The developed multi-stage principal component analysis 
      method applied at the pre-separation step has reduced the external noises and 
      artifacts significantly. The introduced adaptive law in the whitening step has 
      made the subsequent algorithm in the separation step to converge fast. The 
      separation performance index has varied from -20 dB to -33 dB due to randomness 
      of source signals. The robustness of the ANPCA against background noises has been 
      evaluated by comparing the separation performance indices of the ANPCA with four 
      algorithms (NPCA, NSS-JD, JADE, and SOBI), in which the ANPCA algorithm 
      demonstrated the shortest iteration time with performance index about 0.03. Upon 
      this, it is asserted that the ANPCA algorithm successfully separates mixed source 
      signals. CONCLUSIONS: The independent components produced from the observed data 
      using the proposed method illustrated that the extracted signals were clearly the 
      P300 components elicited by task-related stimuli. The experiment using 350 ms ISI 
      showed the best performance. Since the proposed method does not use down-sampling 
      and averaging, it can be used as a viable tool for real-time clinical 
      applications.
FAU - Turnip, Arjon
AU  - Turnip A
AD  - Department of Cogno-Mechatronics Engineering, Pusan National University, 30 
      Jangjeon-dong, Geumjeong-gu, Busan 609-735, Korea.
FAU - Hong, Keum-Shik
AU  - Hong KS
FAU - Jeong, Myung-Yung
AU  - Jeong MY
LA  - eng
PT  - Journal Article
PT  - Research Support, Non-U.S. Gov't
DEP - 20110923
PL  - England
TA  - Biomed Eng Online
JT  - Biomedical engineering online
JID - 101147518
SB  - IM
MH  - Adult
MH  - Algorithms
MH  - Artifacts
MH  - Brain/physiology
MH  - Brain Mapping/methods
MH  - Computer Simulation
MH  - Electroencephalography/methods
MH  - *Event-Related Potentials, P300
MH  - Humans
MH  - Male
MH  - Neural Networks, Computer
MH  - Nonlinear Dynamics
MH  - Principal Component Analysis/*methods
MH  - Signal Processing, Computer-Assisted
PMC - PMC3749271
EDAT- 2011/09/24 06:00
MHDA- 2012/01/20 06:00
PMCR- 2011/09/23
CRDT- 2011/09/24 06:00
PHST- 2011/05/11 00:00 [received]
PHST- 2011/09/23 00:00 [accepted]
PHST- 2011/09/24 06:00 [entrez]
PHST- 2011/09/24 06:00 [pubmed]
PHST- 2012/01/20 06:00 [medline]
PHST- 2011/09/23 00:00 [pmc-release]
AID - 1475-925X-10-83 [pii]
AID - 10.1186/1475-925X-10-83 [doi]
PST - epublish
SO  - Biomed Eng Online. 2011 Sep 23;10:83. doi: 10.1186/1475-925X-10-83.