Decoding EEG by Visual-guided Deep Neural Networks
Authors: Zhicheng Jiao, Haoxuan You, Fan Yang, Xin Li, Han Zhang, Dinggang Shen
IJCAI 2019 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Performance of our framework is evaluated and compared with state-of-the-art methods on two public datasets: (1) Image Net subset [Spampinato et al., 2017; Kavasidis et al., 2017]; (2) Face and object [Kaneshiro et al., 2015]. Results listed in this table show that our visual-guided frameworks outperform LDA and LSTM, among which the Res Net101 guided classification method achieves a new state-of-the-art result, with our method improving the performance of the EEG classification stage. |
| Researcher Affiliation | Collaboration | 1Department of Radiology and BRIC, University of North Carolina at Chapel Hill, USA 2BNRist, KLISS, School of Software, Tsinghua University, China |
| Pseudocode | No | The paper does not contain structured pseudocode or algorithm blocks. |
| Open Source Code | No | The paper does not provide concrete access to source code for the methodology described. |
| Open Datasets | Yes | Performance of our framework is evaluated and compared with state-of-the-art methods on two public datasets: (1) Image Net subset [Spampinato et al., 2017; Kavasidis et al., 2017]; (2) Face and object [Kaneshiro et al., 2015]. |
| Dataset Splits | Yes | The independent separations of EEG signal datasets are 80% for training, 10% for validation, and 10 % for testing. |
| Hardware Specification | Yes | In this research, we perform the deep learning experiments on a Titan V graphics card provided by the NVIDIA Academic Program of GPU Grant Program. |
| Software Dependencies | No | Our models are based on the deep learning toolkit of Tensor Flow [Abadi et al., 2016]. No version number is specified. |
| Experiment Setup | Yes | Training strategy of our classification network is Adam. Structure of our EEG classification net in cognitive domain is the same form as that of Alex Net... Parameters of D and G are listed in Table 3 and Table 4... FCN for the visual-consistent term λ (Lper + Lsem) is pretrained on VOC2012 dataset [Everingham et al., 2010], and λ is set to 0.5. |