Name
Title | ||
|---|---|---|
Benefits Of Multi-Domain Feature Of Mismatch Negativity Extracted By Non-Negative Tensor Factorization From Eeg Collected By Low-Density Array |
Abstract | ||
|---|---|---|
Through exploiting temporal, spectral, time-frequency representations, and spatial properties of mismatch negativity (MMN) simultaneously, this study extracts a multi-domain feature of MMN mainly using non-negative tensor factorization. In our experiment, the peak amplitude of MMN between children with reading disability and children with attention deficit was not significantly different, whereas the new feature of MMN significantly discriminated the two groups of children. This is because the feature was derived from multi-domain information with significant reduction of the heterogeneous effect of datasets. |
Year | DOI | Venue |
|---|---|---|
2012 | 10.1142/S0129065712500256 | INTERNATIONAL JOURNAL OF NEURAL SYSTEMS |
Keywords | Field | DocType |
EEG, event-related potential, mismatch negativity, multi-domain feature, non-negative tensor factorization | Mismatch negativity,Pattern recognition,Computer science,Event-related potential,Speech recognition,Multi domain,Artificial intelligence,Tensor factorization,Reading disability,Amplitude,Electroencephalography,Low density | Journal |
Volume | Issue | ISSN |
22 | 6 | 0129-0657 |
Citations | PageRank | References |
17 | 0.94 | 29 |
Authors | ||
8 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Fengyu Cong | 1 | 151 | 24.72 |
| Anh Huy Phan | 2 | 828 | 51.60 |
| Qibin Zhao | 3 | 905 | 68.65 |
| Tiina Huttunen-Scott | 4 | 33 | 2.67 |
| Jukka Kaartinen | 5 | 18 | 1.30 |
| Tapani Ristaniemi | 6 | 522 | 76.89 |
| Heikki Lyytinen | 7 | 70 | 7.67 |
| Andrzej Cichocki | 8 | 5228 | 508.42 |