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Found 8 papers, 1 papers with code
Cepstral Analysis Based Artifact Detection, Recognition and Removal for Prefrontal EEG
The proposed method achieves an accuracy of 99. 62% on the artifact detection task and a 82. 79% accuracy on the 6-category eye movement classification task.
Resilience of Entropy Model in Distributed Neural Networks
As such, in this paper we formulate and investigate the resilience of entropy models to intentional interference (e. g., adversarial attacks) and unintentional interference (e. g., weather changes and motion blur).
LightSleepNet: Design of a Personalized Portable Sleep Staging System Based on Single-Channel EEG
This paper proposed LightSleepNet - a light-weight, 1-d Convolutional Neural Network (CNN) based personalized architecture for real-time sleep staging, which can be implemented on various mobile platforms with limited hardware resources.
Multi-Channel Multi-Domain based Knowledge Distillation Algorithm for Sleep Staging with Single-Channel EEG
The proposed algorithm achieves a state-of-the-art single-channel sleep staging accuracy of 86. 5%, with only 0. 6% deterioration from the state-of-the-art multi-channel model.
A Closed-loop Brain-Machine Interface SoC Featuring a 0.2$μ$J/class Multiplexer Based Neural Network
This work presents the first fabricated electrophysiology-optogenetic closed-loop bidirectional brain-machine interface (CL-BBMI) system-on-chip (SoC) with electrical neural signal recording, on-chip sleep staging and optogenetic stimulation.
Adversarial Machine Learning in Latent Representations of Neural Networks
Our experimental results support our theoretical findings by showing that the compressed latent representations can reduce the success rate of adversarial attacks by 88% in the best case and by 57% on the average compared to attacks to the input space.
Single-channel EEG completion using Cascade Transformer
It is easy for the electroencephalogram (EEG) signal to be incomplete due to packet loss, electrode falling off, etc.
SaleNet: A low-power end-to-end CNN accelerator for sustained attention level evaluation using EEG
This paper proposes SaleNet - an end-to-end convolutional neural network (CNN) for sustained attention level evaluation using prefrontal electroencephalogram (EEG).
