# Addressing Class Imbalance in Classification Problems of Noisy Signals by using Fourier Transform Surrogates

arXiv: https://arxiv.org/abs/1806.08675 · HTML: https://mlsanity.com/paper/1806.08675/

## Key facts

- Cited by 10 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 20 Jun 2018.

## Built on by (most cited first, 10 of 10)

- [Data augmentation for learning predictive models on EEG: a systematic comparison](https://mlsanity.com/paper/2206.14483/) (2022)
- [CADDA: Class-wise Automatic Differentiable Data Augmentation for EEG Signals](https://mlsanity.com/paper/2106.13695/) (2021)
- [Deep invariant networks with differentiable augmentation layers](https://mlsanity.com/paper/2202.02142/) (2022)
- [Is Limited Participant Diversity Impeding EEG-based Machine Learning?](https://mlsanity.com/paper/2503.13497/) (2025)
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- [Enhancing Cardiovascular Disease Prediction through Multi-Modal Self-Supervised Learning](https://mlsanity.com/paper/2411.05900/) (2024)
- [FEMBA on the Edge: Physiologically-Aware Pre-Training, Quantization, and Deployment of a Bidirectional Mamba EEG Foundation Model on an Ultra-low Power Microcontroller](https://mlsanity.com/paper/2603.26716/) (2026)
- [Distributionally Robust Cross Subject EEG Decoding](https://mlsanity.com/paper/2308.11651/) (2023)
- [ART: Artifact Removal Transformer for Reconstructing Noise-Free Multichannel Electroencephalographic Signals](https://mlsanity.com/paper/2409.07326/) (2024)
- [Synthetic Data Generation for Brain-Computer Interfaces: Overview, Benchmarking, and Future Directions](https://mlsanity.com/paper/2603.12296/) (2026)

## Abstract (arXiv)

Randomizing the Fourier-transform (FT) phases of temporal-spatial data generates surrogates that approximate examples from the data-generating distribution. We propose such FT surrogates as a novel tool to augment and analyze training of neural networks and explore the approach in the example of sleep-stage classification. By computing FT surrogates of raw EEG, EOG, and EMG signals of under-represented sleep stages, we balanced the CAPSLPDB sleep database. We then trained and tested a convolutional neural network for sleep stage classification, and found that our surrogate-based augmentation improved the mean F1-score by 7%. As another application of FT surrogates, we formulated an approach to compute saliency maps for individual sleep epochs. The visualization is based on the response of inferred class probabilities under replacement of short data segments by partial surrogates. To quantify how well the distributions of the surrogates and the original data match, we evaluated a trained classifier on surrogates of correctly classified examples, and summarized these conditional predictions in a confusion matrix. We show how such conditional confusion matrices can qualitatively explain the performance of surrogates in class balancing. The FT-surrogate augmentation approach may improve classification on noisy signals if carefully adapted to the data distribution under analysis.