Deep learning on real-world EEG data achieves 90.7% accuracy in predicting driver intentions up to one second before maneuvers, with best performance from the TSCeption model.
Deep learning with convolutional neural networks for EEG decoding and visualization,
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DSAINet proposes a dual-scale attentive interaction network that outperforms baselines on multiple EEG tasks using the same hyperparameters and only 77K parameters.
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Mind2Drive: Predicting Driver Intentions from EEG in Real-world On-Road Driving
Deep learning on real-world EEG data achieves 90.7% accuracy in predicting driver intentions up to one second before maneuvers, with best performance from the TSCeption model.
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DSAINet: An Efficient Dual-Scale Attentive Interaction Network for General EEG Decoding
DSAINet proposes a dual-scale attentive interaction network that outperforms baselines on multiple EEG tasks using the same hyperparameters and only 77K parameters.