Interpretability in Linear Brain Decoding
Improving the interpretability of brain decoding approaches is of primary interest in many neuroimaging studies. Despite extensive studies of this type, at present, there is no formal definition for interpretability of brain decoding models. As a consequence, there is no quantitative measure for evaluating the interpretability of different brain decoding methods. In this paper, we present a simple definition for interpretability of linear brain decoding models. Then, we propose to combine the interpretability and the performance of the brain decoding into a new multi-objective criterion for model selection. Our preliminary results on the toy data show that optimizing the hyper-parameters of the regularized linear classifier based on the proposed criterion results in more informative linear models. The presented definition provides the theoretical background for quantitative evaluation of interpretability in linear brain decoding.
Code (0)
등록된 구현이 없습니다.
Tasks
Brain DecodingModel SelectionMethods 이 논문이 사용한 방법론
Similar Papers 제목 키워드 기반
Interpretability of Multivariate Brain Maps in Brain Decoding: Definition and Quantification
Brain decoding is a popular multivariate approach for hypothesis testing in neuroimaging. It is well known that the brain maps derived from weights of linear classifiers are hard to interpret because of high correlations…
Brain DecodingModel SelectionTwo-sample testingEEG decoding with conditional identification information
Decoding EEG signals is crucial for unraveling human brain and advancing brain-computer interfaces. Traditional machine learning algorithms have been hindered by the high noise levels and inherent inter-person variations…
EEGEeg DecodingFPED: A Functional-Network Prior-Guided Mixture-of-Experts Framework for Interpretable Brain Decoding
Visual image reconstruction from functional Magnetic Resonance Imaging (fMRI) is a fundamental task in brain decoding, providing a crucial pathway for understanding human perceptual mechanisms and developing advanced bra…
Representation LearningImage ReconstructionBrain DecodingMindAligner: Explicit Brain Functional Alignment for Cross-Subject Visual Decoding from Limited fMRI Data
Brain decoding aims to reconstruct visual perception of human subject from fMRI signals, which is crucial for understanding brain's perception mechanisms. Existing methods are confined to the single-subject paradigm due …
Brain DecodingNeuro-Vision to Language: Enhancing Brain Recording-based Visual Reconstruction and Language Interaction
Decoding non-invasive brain recordings is pivotal for advancing our understanding of human cognition but faces challenges due to individual differences and complex neural signal representations. Traditional methods often…
Brain DecodingImage ReconstructionQuestion Answering