6533b82afe1ef96bd128c144
RESEARCH PRODUCT
Non-negative matrix factorization Vs. FastICA on mismatch negativity of children
Igor KalyakinZhilin ZhangHeikki LyytinenFengyu CongTiina Huttunen-scottTapani Ristaniemisubject
business.industrySpeech recognitionMismatch negativityPattern recognitionbehavioral disciplines and activitiesIndependent component analysisElectronic mailMatrix decompositionNon-negative matrix factorizationP3aTime–frequency representationFastICAArtificial intelligencebusinesspsychological phenomena and processesMathematicsdescription
In this presentation two event-related potentials, mismatch negativity (MMN) and P3a, are extracted from EEG by non-negative matrix factorization (NMF) simultaneously. Typically MMN recordings show a mixture of MMN, P3a, and responses to repeated standard stimuli. NMF may release the source independence assumption and data length limitations required by Fast independent component analysis (FastICA). Thus, in theory NMF could reach better separation of the responses. In the current experiment MMN was elicited by auditory duration deviations in 102 children. NMF was performed on the time-frequency representation of the raw data to estimate sources. Support to Absence Ratio (SAR) of the MMN component was utilized to evaluate the performance of NMF and FastICA. To the raw data, FastICA-MMN component, and NMF-MMN component, SARs were 31, 34 and 49dB respectively. NMF outperformed FastICA by 15dB. This study also demonstrates that children with reading disability have larger P3a than control children under NMF.
| year | journal | country | edition | language |
|---|---|---|---|---|
| 2009-06-01 | 2009 International Joint Conference on Neural Networks |