# Improved EEG Event Classification Using Differential Energy

arXiv: https://arxiv.org/abs/1801.02477 · HTML: https://mlsanity.com/paper/1801.02477/

## Key facts

- Cited by 32 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 3 Jan 2018.

## Built on by (most cited first, 30 of 32)

- [NeuroLM: A Universal Multi-task Foundation Model for Bridging the Gap between Language and EEG Signals](https://mlsanity.com/paper/2409.00101/) (2024)
- [EEG2Rep: Enhancing Self-supervised EEG Representation Through Informative Masked Inputs](https://mlsanity.com/paper/2402.17772/) (2024)
- [EEG-FM-Bench: A Comprehensive Benchmark for the Systematic Evaluation and Diagnostic Analyses of EEG Foundation Models](https://mlsanity.com/paper/2508.17742/) (2025)
- [BIOT: Cross-data Biosignal Learning in the Wild](https://mlsanity.com/paper/2305.10351/) (2023)
- [Tokenizing Single-Channel EEG with Time-Frequency Motif Learning](https://mlsanity.com/paper/2502.16060/) (2025)
- [NeuralBench: A Unifying Framework to Benchmark NeuroAI Models](https://mlsanity.com/paper/2605.08495/) (2026)
- [Uni-NTFM: A Unified Foundation Model for EEG Signal Representation Learning](https://mlsanity.com/paper/2509.24222/) (2025)
- [OTIS: Learning High-Quality Time Series Features With Tiny Encoders](https://mlsanity.com/paper/2410.07299/) (2024)
- [THD-BAR: Topology Hierarchical Derived Brain Autoregressive Modeling for EEG Generic Representations](https://mlsanity.com/paper/2511.13733/) (2025)
- [OmniEEG-Bench: A Standardized Evaluation Benchmark for EEG Foundation Models](https://mlsanity.com/paper/2606.00815/) (2026)
- [BioSerenity-E1: a self-supervised EEG model for medical applications](https://mlsanity.com/paper/2503.10362/) (2025)
- [EEGDM: EEG Representation Learning via Generative Diffusion Model](https://mlsanity.com/paper/2508.14086/) (2025)
- [NeuroWeaver: An Autonomous Evolutionary Agent for Exploring the Programmatic Space of EEG Analysis Pipelines](https://mlsanity.com/paper/2602.13473/) (2026)
- [Are foundation models useful feature extractors for electroencephalography analysis?](https://mlsanity.com/paper/2502.21086/) (2025)
- [ALFEE: Adaptive Large Foundation Model for EEG Representation](https://mlsanity.com/paper/2505.06291/) (2025)
- [SingLEM: Single-Channel Large EEG Model](https://mlsanity.com/paper/2509.17920/) (2025)
- [Pretraining on Sleep Data Improves non-Sleep Biosignal Tasks](https://mlsanity.com/paper/2605.02500/) (2026)
- [KAST-BAR: Knowledge-Anchored Semantically-Dynamic Topology Brain Autoregressive Modeling for Universal Neural Interpretation](https://mlsanity.com/paper/2605.13133/) (2026)
- [EEGDM: Learning EEG Representation with Latent Diffusion Model](https://mlsanity.com/paper/2508.20705/) (2025)
- [STAMP: Spatial-Temporal Adapter with Multi-Head Pooling](https://mlsanity.com/paper/2511.10848/) (2025)
- [A Multi-dimensional Framework for Evaluating Generalization in EEG Foundation Models](https://mlsanity.com/paper/2605.28563/) (2026)
- [Nested Deep Learning Model Towards A Foundation Model for Brain Signal Data](https://mlsanity.com/paper/2410.03191/) (2024)
- [KnowEEG: Explainable Knowledge Driven EEG Classification](https://mlsanity.com/paper/2505.00541/) (2025)
- [IEFS-GMB: Gradient Memory Bank-Guided Feature Selection Based on Information Entropy for EEG Classification of Neurological Disorders](https://mlsanity.com/paper/2509.15259/) (2025)
- [Making Conformal Predictors Robust in Healthcare Settings: a Case Study on EEG Classification](https://mlsanity.com/paper/2602.19483/) (2026)
- [NeuroNarrator: A Generalist EEG-to-Text Foundation Model for Clinical Interpretation via Spectro-Spatial Grounding and Temporal State-Space Reasoning](https://mlsanity.com/paper/2603.16880/) (2026)
- [Test-Time Adaptation for EEG Foundation Models: A Systematic Study under Real-World Distribution Shifts](https://mlsanity.com/paper/2604.16926/) (2026)
- [Visualizing the Invisible: Generative Visual Grounding Empowers Universal EEG Understanding in MLLMs](https://mlsanity.com/paper/2605.18172/) (2026)
- [Continuous-Latent Predictive Modeling with Semantic Alignment for EEG-Language Foundation Models](https://mlsanity.com/paper/2608.11656/) (2026)
- [Parameter-Efficient Self-Supervised Adaptation for EEG-FM under Fixed Computational Budgets](https://mlsanity.com/paper/2608.24727/) (2026)

## Abstract (arXiv)

Feature extraction for automatic classification of EEG signals typically relies on time frequency representations of the signal. Techniques such as cepstral-based filter banks or wavelets are popular analysis techniques in many signal processing applications including EEG classification. In this paper, we present a comparison of a variety of approaches to estimating and postprocessing features. To further aid in discrimination of periodic signals from aperiodic signals, we add a differential energy term. We evaluate our approaches on the TUH EEG Corpus, which is the largest publicly available EEG corpus and an exceedingly challenging task due to the clinical nature of the data. We demonstrate that a variant of a standard filter bank-based approach, coupled with first and second derivatives, provides a substantial reduction in the overall error rate. The combination of differential energy and derivatives produces a 24% absolute reduction in the error rate and improves our ability to discriminate between signal events and background noise. This relatively simple approach proves to be comparable to other popular feature extraction approaches such as wavelets, but is much more computationally efficient.