most citedEEG-based Cross-Subject Driver Drowsiness Recognition with an Interpretable Convolutional Neural Network

178 citations

researchers with a paper here

Counting papers with at most 25 authors, so collaboration author lists do not stand in for the people here.

collaborating institutions

4 papers

cs.NE2021★ 36 cited

Subject-Independent Drowsiness Recognition from Single-Channel EEG with an Interpretable CNN-LSTM model

Jian Cui, Zirui Lan, Tianhu Zheng +4

For EEG-based drowsiness recognition, it is desirable to use subject-independent recognition since conducting calibration on each subject is time-consuming. In this paper, we propo…

eess.SP2021★ 178 cited

EEG-based Cross-Subject Driver Drowsiness Recognition with an Interpretable Convolutional Neural Network

Jian Cui, Zirui Lan, Olga Sourina +1

In the context of electroencephalogram (EEG)-based driver drowsiness recognition, it is still challenging to design a calibration-free system, since EEG signals vary significantly…

eess.SP2021★ 142 cited

A Compact and Interpretable Convolutional Neural Network for Cross-Subject Driver Drowsiness Detection from Single-Channel EEG

Jian Cui, Zirui Lan, Yisi Liu +4

Driver drowsiness is one of main factors leading to road fatalities and hazards in the transportation industry. Electroencephalography (EEG) has been considered as one of the best…

cs.CV2020

Shape retrieval of non-rigid 3d human models

David Pickup, Xianfang Sun, Paul L Rosin +28

3D models of humans are commonly used within computer graphics and vision, and so the ability to distinguish between body shapes is an important shape retrieval problem. We extend…