Brain Decoding

21 papers with code • 2 benchmarks • 3 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.

Most implemented papers

Deep learning-based electroencephalography analysis: a systematic review

kylemath/DeepEEG 16 Jan 2019

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 2 Jul 2020

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

BTajini/augfmri 11 Jul 2021

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

smkia/interpretability 29 Mar 2016

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 2 Jun 2018

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

arthurmensch/cogspaces 17 Sep 2018

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 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 10 Sep 2020

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 9 Jun 2021

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 27 May 2022

Decoding brain imaging data are gaining popularity, with applications in brain-computer interfaces and the study of neural representations.