SHAP-AAD: DeepSHAP-Guided Channel Reduction for EEG Auditory Attention Detection

TL;DR

This paper introduces SHAP-AAD, a novel framework combining explainable AI and lightweight neural networks to reduce EEG channels for auditory attention detection, maintaining high accuracy with fewer electrodes.

Contribution

It presents a two-stage approach using DeepSHAP for channel selection and a compact TCN for efficient AAD, significantly reducing electrode count while preserving performance.

Findings

Using 32 channels achieves comparable accuracy to 64 channels.

Even 8 channels can provide satisfactory accuracy.

The method effectively reduces complexity without sacrificing detection performance.

Abstract

Electroencephalography (EEG)-based auditory attention detection (AAD) offers a non-invasive way to enhance hearing aids, but conventional methods rely on too many electrodes, limiting wearability and comfort. This paper presents SHAP-AAD, a two-stage framework that combines DeepSHAP-based channel selection with a lightweight temporal convolutional network (TCN) for efficient AAD using fewer channels.DeepSHAP, an explainable AI technique, is applied to a Convolutional Neural Network (CNN) trained on topographic alpha-power maps to rank channel importance, and the top-k EEG channels are used to train a compact TCN. Experiments on the DTU dataset show that using 32 channels yields comparable accuracy to the full 64-channel setup (79.21% vs. 81.06%) on average. In some cases, even 8 channels can deliver satisfactory accuracy. These results demonstrate the effectiveness of SHAP-AAD in reducing complexity while preserving high detection performance.