Corticomorphic Hybrid CNN-SNN Architecture for EEG-based Low-footprint Low-latency Auditory Attention Detection

TL;DR

This paper introduces a hybrid CNN-SNN model inspired by the auditory cortex that accurately detects auditory attention from EEG data in low-latency, low-power, edge-computing scenarios, using only 8 electrodes.

Contribution

The paper presents a novel corticomorphic hybrid CNN-SNN architecture that improves EEG-based auditory attention detection accuracy and efficiency for edge devices.

Findings

Achieves 91.03% accuracy with 1-second decision windows.

Uses 15% fewer parameters and 57% less memory than traditional CNN models.

Operates effectively with only 8 EEG electrodes near the auditory cortex.

Abstract

In a multi-speaker "cocktail party" scenario, a listener can selectively attend to a speaker of interest. Studies into the human auditory attention network demonstrate cortical entrainment to speech envelopes resulting in highly correlated Electroencephalography (EEG) measurements. Current trends in EEG-based auditory attention detection (AAD) using artificial neural networks (ANN) are not practical for edge-computing platforms due to longer decision windows using several EEG channels, with higher power consumption and larger memory footprint requirements. Nor are ANNs capable of accurately modeling the brain's top-down attention network since the cortical organization is complex and layer. In this paper, we propose a hybrid convolutional neural network-spiking neural network (CNN-SNN) corticomorphic architecture, inspired by the auditory cortex, which uses EEG data along with multi-speaker speech envelopes to successfully decode auditory attention with low latency down to 1 second, using only 8 EEG electrodes strategically placed close to the auditory cortex, at a significantly higher accuracy of 91.03%, compared to the state-of-the-art. Simultaneously, when compared to a traditional CNN reference model, our model uses ~15% fewer parameters at a lower bit precision resulting in ~57% memory footprint reduction. The results show great promise for edge-computing in brain-embedded devices, like smart hearing aids.