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Post-hoc modification of linear models: Combining machine learning with domain information to make solid inferences
Marijn van Vliet1, Riitta Salmelin1
1Department of Neuroscience and Biomedical Engineering, Aalto University, Finland.
Neuroimage
|September 29, 2019
Summary
We introduce a post-hoc modification framework to enhance linear models in neuroimaging by decomposing and adjusting their weight matrices. This method improves model interpretability and boosts decoding accuracy using domain knowledge.
Area of Science:
- Neuroscience
- Machine Learning
- Data Analysis
Background:
- Linear models are crucial for neuroimaging data analysis but are limited by small, noisy datasets.
- Fine-tuning models with domain information is essential when data is insufficient.
Purpose of the Study:
- To present a framework for post-hoc modification of linear models in neuroimaging.
- To enhance model interpretability and control by decomposing and adjusting weight matrices.
- To improve decoding accuracy by integrating prior domain knowledge.
Main Methods:
- Decomposition of the linear model's weight matrix into data covariance, signal of interest, and normalizer.
- Post-hoc modification of these subcomponents to inject prior information and constraints.
- Application to electroencephalography (EEG) data for decoding word associative strength.
Main Results:
- The framework allows for intuitive inspection and modification of model components.
- Post-hoc modification successfully boosted decoding accuracy in ridge and logistic regression models.
- Incorporating spatio-temporal data, N400 information, and cross-participant data improved model performance.
Conclusions:
- Post-hoc modification offers precise control over model fitting, balancing machine learning with domain expertise.
- This approach enhances the performance and interpretability of linear models in neuroimaging.
- The framework is effective for decoding cognitive states, such as associative strength, from EEG data.
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