Improved EEG Event Classification Using Differential Energy

TL;DR

This paper introduces a differential energy feature for EEG classification, significantly reducing error rates and improving discrimination between signal events and noise, while maintaining computational efficiency.

Contribution

The study presents a novel combination of differential energy and derivatives in EEG feature extraction, outperforming traditional methods in accuracy and efficiency.

Findings

24% reduction in error rate with differential energy and derivatives

Comparable performance to wavelet-based features

More computationally efficient than existing approaches

Abstract

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.