Adaptive Template Enhancement for Improved Person Recognition using Small Datasets

TL;DR

This paper introduces an adaptive template enhancement method for EEG-based person recognition, improving classification accuracy especially in noisy and limited data scenarios by transforming feature instances for better class separation.

Contribution

It proposes a novel adaptive template enhancement mechanism that transforms feature-level instances to improve class separation and matching in EEG person recognition tasks.

Findings

Significantly improved classification accuracy in both identification and verification.

Effective performance on noisy EEG data from low-cost sensors.

Robustness demonstrated across different EEG databases.

Abstract

A novel instance-based method for the classification of electroencephalography (EEG) signals is presented and evaluated in this paper. The non-stationary nature of the EEG signals, coupled with the demanding task of pattern recognition with limited training data as well as the potentially noisy signal acquisition conditions, have motivated the work reported in this study. The proposed adaptive template enhancement mechanism transforms the feature-level instances by treating each feature dimension separately, hence resulting in improved class separation and better query-class matching. The proposed new instance-based learning algorithm is compared with a few related algorithms in a number of scenarios. A clinical grade 64-electrode EEG database, as well as a low-quality (high-noise level) EEG database obtained with a low-cost system using a single dry sensor have been used for evaluations in biometric person recognition. The proposed approach demonstrates significantly improved classification accuracy in both identification and verification scenarios. In particular, this new method is seen to provide a good classification performance for noisy EEG data, indicating its potential suitability for a wide range of applications.