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].
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. |