Name
Title | ||
|---|---|---|
A system identification approach to determining listening attention from EEG signals. |
Abstract | ||
|---|---|---|
We still have very little knowledge about how our brains decouple different sound sources, which is known as solving the cocktail party problem. Several approaches; including ERP, time-frequency analysis and, more recently, regression and stimulus reconstruction approaches; have been suggested for solving this problem. In this work, we study the problem of correlating of EEG signals to different sets of sound sources with the goal of identifying the single source to which the listener is attending. Here, we propose a method for finding the number of parameters needed in a regression model to avoid overlearning, which is necessary for determining the attended sound source with high confidence in order to solve the cocktail party problem. |
Year | Venue | Keywords |
|---|---|---|
2016 | European Signal Processing Conference | attention,cocktail party,linear regression (LR),finite impulse response (FIR),multivariable model,sound,EEG |
Field | DocType | ISSN |
Overlearning,Cocktail party effect,Regression,Regression analysis,Computer science,Active listening,Speech recognition,Artificial intelligence,Stimulus (physiology),System identification,Machine learning,Electroencephalography | Conference | 2076-1465 |
Citations | PageRank | References |
0 | 0.34 | 0 |
Authors | ||
3 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Emina Alickovic | 1 | 0 | 0.34 |
| Thomas Lunner | 2 | 3 | 2.59 |
| Fredrik Gustafsson | 3 | 2287 | 281.33 |