Non-negative matrix factorization with α-divergence
Pattern Recognition Letters
The WEKA data mining software: an update
ACM SIGKDD Explorations Newsletter
Regularized tensor discriminant analysis for single trial EEG classification in BCI
Pattern Recognition Letters
Nonnegative Matrix and Tensor Factorizations: Applications to Exploratory Multi-way Data Analysis and Blind Source Separation
LVA/ICA'12 Proceedings of the 10th international conference on Latent Variable Analysis and Signal Separation
ICONIP'12 Proceedings of the 19th international conference on Neural Information Processing - Volume Part III
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In this study, we use features extracted by Nonnegative Tensor Factorization (NTF) from event-related potentials (ERPs) to discriminate healthy children and children with attention deficit (AD). The peak amplitude of an ERP has been extensively used to discriminate different groups of subjects for the clinical research. However, such discriminations sometimes fail because the peak amplitude may vary severely with the increased number of subjects and wider range of ages and it can be easily affected by many factors. This study formulates a framework, using NTF to extract features of the evoked brain activities from time-frequency represented ERPs. Through using the estimated features of a negative ERP-mismatch negativity, the correct rate on the recognition between health children and children with AD approaches to about 76%. However, the peak amplitude did not discriminate them. Hence, it is promising to apply NTF for diagnosing clinical children instead of measuring the peak amplitude.