Reducing training requirements through evolutionary based dimension reduction and subject transfer

TL;DR

This paper proposes a methodology combining evolutionary subject transfer and dimension reduction to significantly decrease training time for EEG-based BCI systems, enabling effective use of data from other subjects.

Contribution

It introduces a novel approach that leverages evolutionary algorithms and subject transfer to reduce calibration data requirements in BCI systems.

Findings

Reduction to 40% of target subject data is sufficient for training.

Evolutionary subject transfer improves classifier performance.

Adapting trained systems from other subjects is feasible.

Abstract

Training Brain Computer Interface (BCI) systems to understand the intention of a subject through Electroencephalogram (EEG) data currently requires multiple training sessions with a subject in order to develop the necessary expertise to distinguish signals for different tasks. Conventionally the task of training the subject is done by introducing a training and calibration stage during which some feedback is presented to the subject. This training session can take several hours which is not appropriate for on-line EEG-based BCI systems. An alternative approach is to use previous recording sessions of the same person or some other subjects that performed the same tasks (subject transfer) for training the classifiers. The main aim of this study is to generate a methodology that allows the use of data from other subjects while reducing the dimensions of the data. The study investigates several possibilities for reducing the necessary training and calibration period in subjects and the classifiers and addresses the impact of i) evolutionary subject transfer and ii) adapting previously trained methods (retraining) using other subjects data. Our results suggest reduction to 40% of target subject data is sufficient for training the classifier. Our results also indicate the superiority of the approaches that incorporated evolutionary subject transfer and highlights the feasibility of adapting a system trained on other subjects.