Deep Convolutional Neural Networks for mental load classification based on EEG data

作者：

Highlights：

• Both single-channel and multi-channel CNN models are developed to obtain representation from spatial and temporal information of EEG data.

• A point-wise gated Boltzmann machines component is introduced to our models to improve performance of our CNN models.

• Both our independent and fused models achieve better performance on mental load classification task, and our models contain much less parameters which result in higher efficiency.

摘要

•Both single-channel and multi-channel CNN models are developed to obtain representation from spatial and temporal information of EEG data.•A point-wise gated Boltzmann machines component is introduced to our models to improve performance of our CNN models.•Both our independent and fused models achieve better performance on mental load classification task, and our models contain much less parameters which result in higher efficiency.

论文关键词：Deep learning,Mental load classification,CNNs,EEG

论文评审过程：Received 11 September 2016, Revised 1 December 2017, Accepted 5 December 2017, Available online 8 December 2017, Version of Record 21 December 2017.

论文官网地址：https://doi.org/10.1016/j.patcog.2017.12.002