2018/08/08 by Yuqi Cui, Dongrui Wu, Cui, Yuqi +1
Computer Science · Neuroscience · Psychology · #EEG and Brain-Computer Interfaces #FOS: Computer and information sciences #Gaze Tracking and Assistive Technology #Human-Computer Interaction (cs.HC) #Machine Learning (cs.LG) #Sleep and Work-Related Fatigue #cs.HC #cs.LG
paper · pdf · doi:10.48550/arxiv.1809.00929
arxiv created 2018/08/08 · openalex publication_date 2018/08/08 · arxiv updated 2018/09/05 · openalex created_date 2019/06/27 · openalex updated_date 2026/07/28
Deep learning, including convolutional neural networks (CNNs), has started finding applications in brain-computer interfaces (BCIs). However, so far most such approaches focused on BCI classification problems. This paper extends EEGNet, a 3-layer CNN model for BCI classification, to BCI regression, and also utilizes a novel spectral meta-learner for regression (SMLR) approach to aggregate multiple EEGNets for improved performance. Our model uses the power spectral density (PSD) of EEG signals as the input. Compared with raw EEG inputs, the PSD inputs can reduce the computational cost significantly, yet achieve much better regression performance. Experiments on driver drowsiness estimation from EEG signals demonstrate the outstanding performance of our approach.