🤖 AI Summary
This study addresses the joint four-class classification problem of sleep state (normal vs. sleep-deprived) and eye condition (eyes open vs. closed) using electroencephalography (EEG). The authors propose transforming multi-channel EEG segments into 224×224 grayscale images and employ a lightweight convolutional neural network enhanced with residual connections and Squeeze-and-Excitation channel attention mechanisms. Using only five EEG channels, this approach achieves efficient recognition by integrating image-based EEG representation with channel attention for the first time. After preprocessing involving Z-score normalization, min-max scaling, and center padding, the method attains an average individual accuracy of 83.69% across 35 subjects—outperforming the strongest baseline by 36.03 percentage points and significantly surpassing established models such as EEGNet.
📝 Abstract
Sleep deprivation impairs vigilance and cognitive function, yet jointly identifying the sleep condition (normal vs deprived) and the eye state (open vs closed) from electroencephalography (EEG) remains underexplored. We address this four-class problem with TinyCNNDeep, a lightweight convolutional neural network that combines residual learning with a Squeeze-and-Excitation (SE) attention module. We convert short multi-channel EEG segments from five physiologically relevant channels (Fp1, Fp2, O1, Oz, O2) into 224x224 grayscale images through per-channel Z-score normalization, min-max scaling, and center padding, enabling 2D convolutions to jointly model inter-channel and temporal structure. On a 35-subject dataset recorded under normal-sleep and sleep-deprivation sessions, TinyCNNDeep attains a subject-wise mean accuracy of 83.69%, outperforming the strongest baseline (Random Forest with combined time-frequency features, 47.66%) by 36.03 percentage points, while three established EEG architectures (EEGNet, ShallowConvNet, DeepConvNet) operate near chance. Per-subject analysis quantifies inter-subject variability, and confusion-matrix inspection shows that residual misclassifications concentrate between eyes-closed states across sleep conditions. These results indicate that an image-based EEG representation paired with residual feature extraction and channel attention provides an accurate and computationally efficient framework for multiclass sleep-related EEG classification under a minimal electrode configuration.