A Technique Based on Chaos for Brain Computer Interfacing

TL;DR

This paper introduces a chaos-based feature extraction method for EEG signals in Brain Computer Interfaces, achieving high classification accuracy for motor imagery tasks using novel chaotic indices and machine learning classifiers.

Contribution

It proposes using chaotic indices like Lyapunov exponent and correlation dimension for EEG classification in BCI, enhancing accuracy over traditional methods.

Findings

Achieved 95.5% accuracy in classifying motor imagery tasks.

Demonstrated effectiveness of chaotic features in EEG signal classification.

Utilized multi-layer Perceptron and KM-SVM classifiers for improved results.

Abstract

A user of Brain Computer Interface (BCI) system must be able to control external computer devices with brain activity. Although the proof-of-concept was given decades ago, the reliable translation of user intent into device control commands is still a major challenge. There are problems associated with classification of different BCI tasks. In this paper we propose the use of chaotic indices of the BCI. We use largest Lyapunov exponent, mutual information, correlation dimension and minimum embedding dimension as the features for the classification of EEG signals which have been released by BCI Competition IV. A multi-layer Perceptron classifier and a KM- SVM(support vector machine classifier based on k-means clustering) is used for classification process, which lead us to an accuracy of 95.5%, for discrimination between two motor imagery tasks.