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 Coakley et alK. L. Coakley, T. Snelleman, H. Hoos, and O. E. Gundersen, "The embrace of open science: An analysis of a decade of AI research and 56 800 conference papers," Under Review, 2026..
Open Vocabulary Electroencephalography-to-Text Decoding and Zero-Shot Sentiment Classification
Authors: Zhenhailong Wang, Heng Ji5350-5358
AAAI 2022 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Our model achieves a 40.1% BLEU1 score on EEG-To-Text decoding and a 55.6% F1 score on zero-shot EEG-based ternary sentiment classification, which significantly outperforms supervised baselines. |
| Researcher Affiliation | Academia | Zhenhailong Wang, Heng Ji University of Illinois at Urbana-Champaign EMAIL |
| Pseudocode | No | The paper describes its models and pipeline using diagrams (e.g., Figure 2) but does not provide structured pseudocode or algorithm blocks. |
| Open Source Code | Yes | The code is made publicly available for research purpose at https://github.com/Mike Wang WZHL/ EEG-To-Text. |
| Open Datasets | Yes | We use Zu Co (Hollenstein et al. 2018, 2020) datasets, which contain simultaneous EEG and Eye-tracking data recorded from natural reading tasks. |
| Dataset Splits | Yes | And then we split each reading task s data into train, development, test (80%,10%,10%) by unique sentences, that is, the sentences in test set are totally unseen. |
| Hardware Specification | No | The paper does not provide specific hardware details (e.g., GPU/CPU models, memory) used for running its experiments. |
| Software Dependencies | No | The paper mentions using pre-trained models and libraries like BART, BERT, RoBERTa, HuggingFace transformers, and Spacy, but does not provide specific version numbers for these software dependencies. |
| Experiment Setup | No | The paper describes some architectural details like the MTE having "6 layers and 8 attention heads" and the objective function, but it does not provide specific hyperparameter values (e.g., learning rate, batch size, number of epochs) or detailed training configurations. |