<ul data-eligibleForWebStory="true"><li>A new study introduces AGTCNet, a graph-temporal model for motor imagery EEG classification in brain-computer interface technology.</li><li>AGTCNet leverages graph convolutional attention networks to capture spatiotemporal dependencies in EEG signals effectively.</li><li>The model outperformed existing classifiers, achieving state-of-the-art performance with reduced model size and faster inference time.</li><li>AGTCNet demonstrated high accuracies for subject-independent and subject-specific classifications on various EEG datasets, showcasing its practicality for BCI deployment.</li></ul>

AGTCNet: A Graph-Temporal Approach for Principled Motor Imagery EEG Classification

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