| Presentation | 2014-06-27 Probabilistic Enhancement of EEG Component Using Prior Distribution of Correlations Between Channels Hayato Maki, Tomoki Toda, Sakriani Sakti, Graham Neubig, Satoshi Nakamura, |
|---|---|
| PDF Download Page |  PDF download Page Link |
| Abstract(in Japanese) | (See Japanese page) |
| Abstract(in English) | The signal-noise ratio of EEG is very low, which presents serious problems for interpretation and analysis of signals from EEG recordings. Synchronous addition of trials to cancel out background noise or rejecting trials contaminated by eye blinks or other artifacts cause substantial data loss. Therefore, a technique to separate each component of EEG observations is in high demand. ICA is widely used for the purpose. It works well when the number of sensors is equal to or larger than the number of sources. However, the assumption is questionable in the context of EEG signal separation because we do not know the effective number of statically independent brain signals contributing to the EEG. In the field of audio source separation, a technique to enhance objective components multi-channel Wiener filters has been proposed. The method assumes that the amplitude of each component follows a complex Gaussian in each slot of the time-frequency domain, and thus the amplitude of an observed signal follows a Gaussian mixture model (GMM). These are estimated using the EM algorithm to maximize the likelihood of an observation signal. Applying this scheme to EEG signal component enhancement, we set a prior distribution to spatial correlation matrices. Compared to previous work, this allows us to reduce the degree of freedom in parameter estimation, improving estimation performance. Finally, an experiment was carried out, which demonstrated the effectiveness of the proposed approach. |
| Keyword(in Japanese) | (See Japanese page) |
| Keyword(in English) | EEG / GMM / Wishart distribution / Wiener filter / MAP estimation / EM algorithm |
| Paper # | NC2014-15,IBISML2014-15 |
| Date of Issue |
| Conference Information | |
| Committee | IBISML |
|---|---|
| Conference Date | 2014/6/18(1days) |
| Place (in Japanese) | (See Japanese page) |
| Place (in English) | |
| Topics (in Japanese) | (See Japanese page) |
| Topics (in English) | |
| Chair | |
| Vice Chair | |
| Secretary | |
| Assistant | |
| Paper Information | |
| Registration To | Information-Based Induction Sciences and Machine Learning (IBISML) |
|---|---|
| Language | JPN |
| Title (in Japanese) | (See Japanese page) |
| Sub Title (in Japanese) | (See Japanese page) |
| Title (in English) | Probabilistic Enhancement of EEG Component Using Prior Distribution of Correlations Between Channels |
| Sub Title (in English) | |
| Keyword(1) | EEG |
| Keyword(2) | GMM |
| Keyword(3) | Wishart distribution |
| Keyword(4) | Wiener filter |
| Keyword(5) | MAP estimation |
| Keyword(6) | EM algorithm |
| 1st Author's Name | Hayato Maki |
| 1st Author's Affiliation | Graduate School of Information Science, Nara Institute of Science and Technology() |
| 2nd Author's Name | Tomoki Toda |
| 2nd Author's Affiliation | / Graduate School of Information Science, Nara Institute of Science and Technology |
| 3rd Author's Name | Sakriani Sakti |
| 3rd Author's Affiliation | Graduate School of Information Science, Nara Institute of Science and Technology |
| 4th Author's Name | Graham Neubig |
| 4th Author's Affiliation | Graduate School of Information Science, Nara Institute of Science and Technology |
| 5th Author's Name | Satoshi Nakamura |
| 5th Author's Affiliation | |
| Date | 2014-06-27 |
| Paper # | NC2014-15,IBISML2014-15 |
| Volume (vol) | vol.114 |
| Number (no) | 105 |
| Page | pp.pp.- |
| #Pages | 6 |
| Date of Issue | |