A Novel Local Ablation Approach for Explaining Multimodal Classifiers

Ca, Ellis; Da, Carbajal; Zhang, R; Mse, Sendi; Miller, Robyn L; Calhoun,; Wang, Hong

doi:10.1101/2021.06.10.447986

Cited by 6 publications

(4 citation statements)

References 13 publications

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“…We introduce a global ablation approach that is uniquely adapted for the electrophysiology domain (Ellis et al, 2021b). We then present a local ablation approach (Ellis et al, 2021c) and show how GBFA methods can be used for local insight into multimodal classifiers (Ellis et al, 2021a). With our local methods, we identify subjectlevel differences in modality importance that support the viability of the methods for personalized biomarker identification.…”

Section: Limitations Of Global Explanations and Proposal Of Novel Loc...mentioning

confidence: 95%

“…Because of this, they can also be analyzed on a subject-specific level that paves the way for the identification of personalized biomarkers. Furthermore, local explanations can be used to examine the degree to which demographic and clinical variables affect the patterns learned by a classifier for specific classes and features (Ellis et al, 2021c), which is a capacity that has not previously been exploited in multimodal classification. Local methods have been applied in a couple multimodal classification studies.…”

Section: Limitations Of Global Explanations and Proposal Of Novel Loc...mentioning

confidence: 99%

“…We developed a local ablation approach for insight into modality importance (Ellis et al, 2021c). Our novel ablation approach is similar to the global approach described in the previous section (1).…”

Section: Description Of Novel Local Ablation Approachmentioning

confidence: 99%

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Novel methods for elucidating modality importance in multimodal electrophysiology classifiers

Ellis

Sendi

Zhang

et al. 2023

Front. Neuroinform.

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IntroductionMultimodal classification is increasingly common in electrophysiology studies. Many studies use deep learning classifiers with raw time-series data, which makes explainability difficult, and has resulted in relatively few studies applying explainability methods. This is concerning because explainability is vital to the development and implementation of clinical classifiers. As such, new multimodal explainability methods are needed.MethodsIn this study, we train a convolutional neural network for automated sleep stage classification with electroencephalogram (EEG), electrooculogram, and electromyogram data. We then present a global explainability approach that is uniquely adapted for electrophysiology analysis and compare it to an existing approach. We present the first two local multimodal explainability approaches. We look for subject-level differences in the local explanations that are obscured by global methods and look for relationships between the explanations and clinical and demographic variables in a novel analysis.ResultsWe find a high level of agreement between methods. We find that EEG is globally the most important modality for most sleep stages and that subject-level differences in importance arise in local explanations that are not captured in global explanations. We further show that sex, followed by medication and age, had significant effects upon the patterns learned by the classifier.DiscussionOur novel methods enhance explainability for the growing field of multimodal electrophysiology classification, provide avenues for the advancement of personalized medicine, yield unique insights into the effects of demographic and clinical variables upon classifiers, and help pave the way for the implementation of multimodal electrophysiology clinical classifiers.

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Section: Limitations Of Global Explanations and Proposal Of Novel Loc...mentioning

confidence: 95%

Section: Limitations Of Global Explanations and Proposal Of Novel Loc...mentioning

confidence: 99%

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Novel methods for elucidating modality importance in multimodal electrophysiology classifiers

Ellis

Sendi

Zhang

et al. 2023

Front. Neuroinform.

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“…While this application can improve model performance, applying traditional explainability methods to raw time-series samples makes it very difficult to know what time or frequency features are extracted by classifiers and to draw global conclusions about the importance of extracted features (9). It should be noted that this difficulty is not applicable to identifying spatial importance (10) or modality importance (11)(12)(13)(14) in multichannel or multimodal classification, respectively. However, this difficulty is applicable when trying to understand the temporal and spectral features extracted by classifiers.…”

Section: Introductionmentioning

confidence: 99%

A Systematic Approach for Explaining Time and Frequency Features Extracted by CNNs from Raw EEG Data

Ellis

Miller

Calhoun

2022

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In recent years, the use of convolutional neural networks (CNNs) for raw electroencephalography (EEG) analysis has grown increasingly common. However, relative to earlier machine learning and deep learning methods with manually extracted features, CNNs for raw EEG analysis present unique problems for explainability. As such, a growing group of methods have been developed that provide insight into the spectral features learned by CNNs. However, spectral power is not the only important form of information within EEG, and the capacity to understand the roles of specific multispectral waveforms identified by CNNs could be very helpful. In this study, we present a novel model visualization-based approach that adapts the traditional CNN architecture to increase interpretability and combines that inherent interpretability with a systematic evaluation of the model via a series of novel explainability methods. Our approach evaluates the importance of spectrally distinct first-layer clusters of filters before examining the contributions of identified waveforms and spectra to cluster importance. We evaluate our approach within the context of automated sleep stage classification and find that, for the most part, our explainability results are highly consistent with clinical guidelines. Our approach is the first to systematically evaluate both waveform and spectral feature importance in CNNs trained on EEG data.

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A Gradient-based Spectral Explainability Method for EEG Deep Learning Classifiers

Miller

et al. 2021

Preprint

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The automated feature extraction capabilities of deep learning classifiers have promoted their broader application to EEG analysis. In contrast to earlier machine learning studies that used extracted features and traditional explainability approaches, explainability for classifiers trained on raw data is particularly challenging. As such, studies have begun to present methods that provide insight into the spectral features learned by deep learning classifiers trained on raw EEG. These approaches have two key shortcomings. (1) They involve perturbation, which can create out-of-distribution samples that cause inaccurate explanations. (2) They are global, not local. Local explainability approaches can be used to examine how demographic and clinical variables affected the patterns learned by the classifier. In our study, we present a novel local spectral explainability approach. We apply it to a convolutional neural network trained for automated sleep stage classification. We apply layer-wise relevance propagation to identify the relative importance of the features in the raw EEG and subsequently examine the frequency domain of the explanations to determine the importance of each canonical frequency band locally and globally. We then perform a statistical analysis to determine whether age and sex affected the patterns learned by the classifier for each frequency band and sleep stage. Results showed that δ, β, and γ were the overall most important frequency bands. In addition, age and sex significantly affected the patterns learned by the classifier for most sleep stages and frequency bands. Our study presents a novel spectral explainability approach that could substantially increase the level of insight into classifiers trained on raw EEG.

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A Novel Local Ablation Approach for Explaining Multimodal Classifiers

Cited by 6 publications

References 13 publications

Novel methods for elucidating modality importance in multimodal electrophysiology classifiers

Novel methods for elucidating modality importance in multimodal electrophysiology classifiers

A Systematic Approach for Explaining Time and Frequency Features Extracted by CNNs from Raw EEG Data

A Gradient-based Spectral Explainability Method for EEG Deep Learning Classifiers

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