Notice: The reproducibility variables underlying each score are classified using an automated LLM-based pipeline, validated against a manually labeled dataset. LLM-based classification introduces uncertainty and potential bias; scores should be interpreted as estimates. Full accuracy metrics and methodology are described in [1].
Personalizing EEG-Based Affective Models with Transfer Learning
Authors: Wei-Long Zheng, Bao-Liang Lu
IJCAI 2016 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | The experimental results demonstrate that our proposed subject transfer framework achieves the mean accuracy of 76.31% in comparison with a conventional generic classifier with 56.73% in average. |
| Researcher Affiliation | Academia | 1Center for Brain-like Computing and Machine Intelligence Department of Computer Science and Engineering 2Key Laboratory of Shanghai Education Commission for Intelligent Interaction and Cognitive Engineering 3Brain Science and Technology Research Center Shanghai Jiao Tong University, Shanghai, China |
| Pseudocode | Yes | Algorithm 1 TCA-based Subject Transfer [...] Algorithm 2 KPCA-based Subject Transfer [...] Algorithm 3 TPT-based Subject Transfer |
| Open Source Code | No | The paper refers to a dataset link (http://bcmi.sjtu.edu.cn/ seed/index.html) but does not provide a link or explicit statement about releasing the source code for the described methodology. |
| Open Datasets | Yes | We evaluate the performance of these approaches on an EEG dataset, SEED1, to personalize EEG-based affective models. 1http://bcmi.sjtu.edu.cn/ seed/index.html |
| Dataset Splits | Yes | We adopt a leave-one-subject-out cross validation method for the evaluation. |
| Hardware Specification | No | The paper does not specify any particular hardware used for running the experiments. |
| Software Dependencies | No | The paper mentions 'All the algorithms are implemented in MATLAB' and 'We use the implement of T-SVM in SV M light [Joachims, 1999]' but does not provide specific version numbers for these software components or any other libraries. |
| Experiment Setup | Yes | The regularization parameter µ is set to 1, the same as [Pan et al., 2011]. ... The value of the regularization parameter for TPT is 0.1. To deal with multi-class classification task, we adopt the one vs one strategy to avoid label unbalance problem. |