Directed Graph-based Wireless EEG Sensor Channel Selection Approach for Cognitive Task Classification

TL;DR

This paper introduces a DAG-based channel selection method for wireless EEG sensors to reduce energy consumption while maintaining high classification accuracy in cognitive tasks.

Contribution

The novel approach encodes channel importance in a directed acyclic graph, enabling efficient selection of channels for EEG-based cognitive task classification.

Findings

Reduced channel usage by up to 50%

Achieved classification accuracy of 81%

Demonstrated energy efficiency improvements

Abstract

Wireless electroencephalogram (EEG) sensors have been successfully applied in many medical and computer brain interface classifications. A common characteristic of wireless EEG sensors is that they are low powered devices, and hence an efficient usage of sensor energy resources is critical for any practical application. One way of minimizing energy consumption by the EEG sensors is by reducing the number of EEG channels participating in the classification process. For the purpose of classifying EEG signals, we propose a directed acyclic graph (DAG)-based channel selection algorithm. To achieve this objective, the EEG sensor channels are first realized in a complete undirected graph, where each channel is represented by a node. An edge between any two nodes indicates the collaboration between these nodes in identifying the system state; and the significance of this collaboration is quantified by a weight assigned to the edge. The complete graph is then reduced into a directed acyclic graph that encodes the knowledge of the non-increasing order of the channel ranking for each cognitive task. The channel selection algorithm utilizes this directed graph to find a maximum path such that the total weight of this path satisfies a predefined threshold. It has been demonstrated experimentally that channel utilization has been reduced by 50% in the worst case scenario for a three-state system and an EEG sensor with 14 channels; and the best classification accuracy obtained is 81%.