DeepSleepNet: A Model for Automatic Sleep Stage Scoring Based on Raw Single-Channel EEG

Information Science, Computer Science, Artificial Intelligence, Medicine, Medical Laboratory Science, Neurology,
sleep stage scoring   deep learning   single-channel EEG   convolutional neural networks   bidirectional LSTMs   feature extraction   automation  

DeepSleepNet: An Automatic Sleep Stage Scoring Model Based on Single-Channel EEG Background Introduction Sleep has a significant impact on human health, and monitoring sleep quality is crucial in medical research and practice. Typically, sleep experts score sleep stages by analyzing various physiological signals such as electroencephalogram (EEG), ...

Multi-Feature Attention Convolutional Neural Network for Motor Imagery Decoding

Engineering, Electrical Engineering, Information Science, Automation, Artificial Intelligence, Medicine, Medical Laboratory Science, Neurology, Life Sciences, Bioengineering,
multi-feature attention   convolutional neural networks   motor imagery   brain-computer interface   lightweight model  

Brain-Computer Interface (BCI) is a communication method that connects the nervous system to the external environment. Motor Imagery (MI) is the cornerstone of BCI research, referring to the internal rehearsal before physical execution. Non-invasive techniques such as Electroencephalography (EEG) can record neural activities with high temporal reso...

Efficient Tensor Decomposition-Based Filter Pruning

Information Science, Automation, Artificial Intelligence,
network compression   tensor decompositions   filter pruning   convolutional neural networks   low-rank approximation  

Background Introduction Network Pruning is a crucial technique for designing efficient Convolutional Neural Network (CNN) models. By reducing memory footprint and computational demands, while maintaining or improving overall performance, it makes deploying CNNs on resource-constrained devices (such as mobile phones or embedded systems) feasible. Th...