Brain Decoding
14 papers with code • 2 benchmarks • 2 datasets
Motor Brain Decoding is fundamental task for building motor brain computer interfaces (BCI).
Progress in predicting finger movements based on brain activity allows us to restore motor functions and improve rehabilitation process of patients.
Benchmarks
These leaderboards are used to track progress in Brain Decoding
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Datasets
BCI Competition IV: ECoG to Finger Movements
Stanford ECoG library: ECoG to Finger Movements
Most implemented papers
Deep learning-based electroencephalography analysis: a systematic review
kylemath/DeepEEG
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To help the field progress, we provide a list of recommendations for future studies and we make our summary table of DL and EEG papers available and invite the community to contribute.
Deep brain state classification of MEG data
SMehrkanoon/Deep-brain-state-classification-of-MEG-data
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The experimental results of cross subject multi-class classification on the studied MEG dataset show that the inclusion of attention improves the generalization of the models across subjects.
Functional Magnetic Resonance Imaging data augmentation through conditional ICA
Advances in computational cognitive neuroimaging research are related to the availability of large amounts of labeled brain imaging data, but such data are scarce and expensive to generate.
Interpretability of Multivariate Brain Maps in Brain Decoding: Definition and Quantification
In this paper, first, we present a theoretical definition of interpretability in brain decoding; we show that the interpretability of multivariate brain maps can be decomposed into their reproducibility and representativeness.
Does the brain represent words? An evaluation of brain decoding studies of language understanding
hans/nn-decoding
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Language decoding studies have identified word representations which can be used to predict brain activity in response to novel words and sentences (Anderson et al., 2016; Pereira et al., 2018).
Extracting representations of cognition across neuroimaging studies improves brain decoding
Analyzing data across studies could bring more statistical power; yet the current brain-imaging analytic framework cannot be used at scale as it requires casting all cognitive tasks in a unified theoretical framework.
Linking artificial and human neural representations of language
hans/nn-decoding
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IJCNLP 2019
Through further task ablations and representational analyses, we find that tasks which produce syntax-light representations yield significant improvements in brain decoding performance.
Brain2Word: Decoding Brain Activity for Language Generation
nicolaffETHZ/Brain2Word_paper
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In the case of language stimuli, recent studies have shown that it is possible to decode fMRI scans into an embedding of the word a subject is reading.
More Than Meets the Eye: Self-Supervised Depth Reconstruction From Brain Activity
WeizmannVision/SelfSuperReconst
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This is applied to both: (i) the small number of images presented to subjects in an fMRI scanner (images for which we have fMRI recordings - referred to as "paired" data), and (ii) a very large number of natural images with no fMRI recordings ("unpaired data").
Group-level Brain Decoding with Deep Learning
ricsinaruto/meg-group-decode
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Importantly, group models outperform subject models on low-accuracy subjects (although slightly impair high-accuracy subjects) and can be helpful for initialising subject models.
