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medical-9

Brain Decoding

3260 papers • 126 benchmarks • 313 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.

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Benchmarks

These leaderboards are used to track progress in brain-decoding-18

Trend

Dataset

Best Model

Actions

BCI Competition IV: ECoG to Finger Movements

BCI Competition IV: ECoG to Finger Movements

Stanford ECoG library: ECoG to Finger Movements

Stanford ECoG library: ECoG to Finger Movements

Libraries

i

Use these libraries to find brain-decoding-18 models and implementations

Datasets

BCI Competition IV: ECoG to Finger Movements

BCI Competition IV: ECoG to Finger Movements

Stanford ECoG library: ECoG to Finger Movements

Stanford ECoG library: ECoG to Finger Movements

GOD

Subtasks

No subtasks available.

Most implemented papers

Deep learning-based electroencephalography analysis: a systematic review

Yannick Roy, Hubert J. Banville, Isabela Albuquerque, Alexandre Gramfort, T. Falk, J. Faubert•Tue Jan 15 2019

This work reviews 154 papers that apply DL to EEG, published between January 2010 and July 2018, and spanning different application domains such as epilepsy, sleep, brain–computer interfacing, and cognitive and affective monitoring, to extract trends and highlight interesting approaches from this large body of literature in order to inform future research and formulate recommendations.

Content

Introduction Benchmarks Datasets Subtasks Libraries Papers

1096

0

Functional Magnetic Resonance Imaging data augmentation through conditional ICA

Hugo Richard, B. Thirion, Badr Tajini•Sat Jul 10 2021

This paper introduces Conditional Independent Components Analysis (Conditional ICA): a fast functional Magnetic Resonance Imaging (fMRI) data augmentation technique, that leverages abundant resting-state data to create images by sampling from an ICA decomposition, and proposes a mechanism to condition the generator on classes observed with few samples.

5 0

Deep brain state classification of MEG data

Ismail Alaoui Abdellaoui, Jesús García Fernández, Caner Sahinli, S. Mehrkanoon•Wed Jul 01 2020

Three models based on compact convolution, combined convolutional and long short-term architecture as well as a model based on multi-view learning that aims at fusing the outputs of the two stream networks are proposed and examined and show that the inclusion of attention improves the generalization of the models across subjects.

8 0

Does the brain represent words? An evaluation of brain decoding studies of language understanding

Jon Gauthier, Anna A. Ivanova•Thu May 31 2018

It is claimed that current studies vastly underdetermine the content of these word representations, the algorithms which the brain deploys to produce and consume them, and the computational tasks which they are designed to solve.

32 0

Interpretability of Multivariate Brain Maps in Brain Decoding: Definition and Quantification

S. M. Kia•Mon Mar 28 2016

A theoretical definition of interpretability in brain decoding is presented and it is shown that the interpretability of multivariate brain maps can be decomposed into their reproducibility and representativeness and facilitates the development of more effective brain decoding algorithms in the future.

2 0

Extracting representations of cognition across neuroimaging studies improves brain decoding

G. Varoquaux, J. Mairal, B. Thirion, Arthur Mensch•Sun Sep 16 2018

A new methodology to analyze brain responses across tasks without a joint model of the psychological processes is introduced, which boosts statistical power in small studies with specific cognitive focus by analyzing them jointly with large studies that probe less focal mental processes.

16 0

Brain2Word: Decoding Brain Activity for Language Generation

Damian Pascual, Roger Wattenhofer, Béni Egressy, Nicolas Affolter•Wed Sep 09 2020

This work proposes to directly classify an fMRI scan, mapping it to the corresponding word within a fixed vocabulary, and presents a model that can decode fMRI data from unseen subjects, and uses the decoded words to guide language generation with the GPT-2 model.

26 0

Linking artificial and human neural representations of language

Jon Gauthier, R. Levy•Tue Oct 01 2019

The results constrain the space of NLU models that could best account for human neural representations of language, but also suggest limits on the possibility of decoding fine-grained syntactic information from fMRI human neuroimaging.

97 0

More than meets the eye: Self-supervised depth reconstruction from brain activity

M. Irani, Guy Gaziv•Tue Jun 08 2021

It is shown that dense 3D depth maps of observed 2D natural images can also be recovered directly from fMRI brain recordings, and the newly defined and trained Depth-based Perceptual Similarity metric is used as a reconstruction criterion.

5 0

Adding a benchmark result helps the community track progress.

Brain Decoding | State-of-the-Art