Corticomorphic Hybrid CNN-SNN Architecture for EEG-based Low-footprint
  Low-latency Auditory Attention Detection
Richard Gall Deniz Kocanaogullari Murat Akcakaya Deniz Erdogmus Rajkumar Kubendran
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.