Abstract
Human brain signals associated with perceptual processes have been shown to be useful for visual target image search. For the purpose of online training, we develop a novel mixed effects evoked response detector, which is capable of combining individual random effects and population fixed effects, for the analysis of neural signatures associated with targets. To avoid numerical problems in high dimensional matrix computations, we develop equivalent dimension reduced expressions for the mixed models. We construct the mixed effects evoked response model using principal component analysis to provide bases for the population model and linear discriminant analysis (LDA) to provide bases for the individual models. In addition, the LDA is adopted for Elecroencephalography channel dimensionality reduction. Data collected at different time and experimental conditions from two subjects performing image search tasks are utilized to assess the quality of the models. We also compare the proposed model with the support vector machine (SVM). The results demonstrate that the mixed models approach the SVM and provide reliable inference on cross session evaluation for the single-trial evoked response detection.
| Original language | English (US) |
|---|---|
| Title of host publication | Proceedings of the 2008 IEEE Workshop on Machine Learning for Signal Processing, MLSP 2008 |
| Pages | 91-96 |
| Number of pages | 6 |
| DOIs | |
| State | Published - 2008 |
| Event | 2008 IEEE Workshop on Machine Learning for Signal Processing, MLSP 2008 - Cancun, Mexico Duration: Oct 16 2008 → Oct 19 2008 |
Other
| Other | 2008 IEEE Workshop on Machine Learning for Signal Processing, MLSP 2008 |
|---|---|
| Country | Mexico |
| City | Cancun |
| Period | 10/16/08 → 10/19/08 |
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ASJC Scopus subject areas
- Artificial Intelligence
- Software
- Electrical and Electronic Engineering
Cite this
Mixed effects models for eeg evoked response detection. / Huang, Yonghong; Erdogmus, Deniz; Pavel, Misha; Mathan, Santosh.
Proceedings of the 2008 IEEE Workshop on Machine Learning for Signal Processing, MLSP 2008. 2008. p. 91-96 4685461.Research output: Chapter in Book/Report/Conference proceeding › Conference contribution
}
TY - GEN
T1 - Mixed effects models for eeg evoked response detection
AU - Huang, Yonghong
AU - Erdogmus, Deniz
AU - Pavel, Misha
AU - Mathan, Santosh
PY - 2008
Y1 - 2008
N2 - Human brain signals associated with perceptual processes have been shown to be useful for visual target image search. For the purpose of online training, we develop a novel mixed effects evoked response detector, which is capable of combining individual random effects and population fixed effects, for the analysis of neural signatures associated with targets. To avoid numerical problems in high dimensional matrix computations, we develop equivalent dimension reduced expressions for the mixed models. We construct the mixed effects evoked response model using principal component analysis to provide bases for the population model and linear discriminant analysis (LDA) to provide bases for the individual models. In addition, the LDA is adopted for Elecroencephalography channel dimensionality reduction. Data collected at different time and experimental conditions from two subjects performing image search tasks are utilized to assess the quality of the models. We also compare the proposed model with the support vector machine (SVM). The results demonstrate that the mixed models approach the SVM and provide reliable inference on cross session evaluation for the single-trial evoked response detection.
AB - Human brain signals associated with perceptual processes have been shown to be useful for visual target image search. For the purpose of online training, we develop a novel mixed effects evoked response detector, which is capable of combining individual random effects and population fixed effects, for the analysis of neural signatures associated with targets. To avoid numerical problems in high dimensional matrix computations, we develop equivalent dimension reduced expressions for the mixed models. We construct the mixed effects evoked response model using principal component analysis to provide bases for the population model and linear discriminant analysis (LDA) to provide bases for the individual models. In addition, the LDA is adopted for Elecroencephalography channel dimensionality reduction. Data collected at different time and experimental conditions from two subjects performing image search tasks are utilized to assess the quality of the models. We also compare the proposed model with the support vector machine (SVM). The results demonstrate that the mixed models approach the SVM and provide reliable inference on cross session evaluation for the single-trial evoked response detection.
UR - http://www.scopus.com/inward/record.url?scp=58049149870&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=58049149870&partnerID=8YFLogxK
U2 - 10.1109/MLSP.2008.4685461
DO - 10.1109/MLSP.2008.4685461
M3 - Conference contribution
AN - SCOPUS:58049149870
SN - 9781424423767
SP - 91
EP - 96
BT - Proceedings of the 2008 IEEE Workshop on Machine Learning for Signal Processing, MLSP 2008
ER -