We realase the datasets and code for our ACL-2022 Findings's paper: Cross-Modal Cloze Task: A New Task to Brain-to-Word Decoding.

The fMRI60_CMC and fMRI180_CMC datasets can be found at the file folder datasets.

Format of data:

• word_stimuli.txt contains the stimulus words that used in the fMRI experiments.
• sentences.txt contains the stimulus word and its corresponding sentence, seperated by |. Format: word | sentence.
• synonyms.txt contains the stimulus word and its synonyms, seperated by a white space. Format: word syn1 syn2.
• fMRI.txt contains the link that realeased the fMRI data evoked by word stimuli, need to be preprocessed to 1 fMRI image per word per subject.

To generate contexts for the stimulus words:

python utils/generate_contexts.py --data fMRI180

The main steps for running the code are as follows:

Step 1: Voxel selection

We use the matlab code trainVoxelwiseTargetPredictionModels.m to compute the informative score of voxels for each subject.

Step 2: Cross-modal mapping

The file cross_modal_mapping.py contains all the code for training a regression model to map fMRI voxels to word embeddings.

Step 3: Feature fusion and predict

• The file bert_baseline.py corresponds to the baseline mentioned in our paper.
• The file bert_fusion.py corresponds to our proposed method.

Note: You need to change all paths for file reading and writing based on how you organize your data and code.