flig 0.1.0

Frequency-domain model analysis package

FLEX: Frequency Layer Explanation

Overview

This repository contains the implementation of FLEX (Frequency Layer Explanation), a method designed to explain the predictions of deep neural networks (DNNs) for sleep stage classification using EEG data. FLEX combines Integrated Gradients (IG) with a frequency-domain transform (via the Real Fast Fourier Transform (RFFT)) to provide frequency-based attribution scores.

The method is particularly useful for understanding how different frequency components of EEG signals influence the predictions of a DNN, enhancing model interpretability in the context of sleep research.

We will always talk about EEG Data in Sleep Research here, but in general this method can be used for a frequency-wise understanding of a model classifying time-series data.

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Features

• RFFT Transformation: Input EEG signals are transformed into the frequency domain using the RFFT.
• iRFFT Transformation: The inverse RFFT (iRFFT) is implemented as the first layer in the DNN to process frequency-domain inputs.
• Integrated Gradients Attribution: Captum's IG method is used to compute relevance scores for frequency bands, providing insights into the features contributing to the model's predictions.

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Definition of FLEX

Let F be our model (DNN) and x be our input (EEG-Data). Then with $\bar{F} = F \circ iRFFT$ and $\bar{x} = RFFT(x)$ we get $$FLEX_i(F,x) = IG_i(\bar{F},\bar{x})$$, where $FLEX(F,x) = (FLEX_1(F,x), ..., FLEX_n(F,x))$ with $x \in R^n$.

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Installation

Requirements

• Python 3.8+
• Required libraries:
• numpy
• torch
• captum

Install Dependencies

You can install the required Python libraries using pip:

pip install numpy torch captum