Brain Decoding
24 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.
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
GOD
Most implemented papers
FingerFlex: Inferring Finger Trajectories from ECoG signals
Motor brain-computer interface (BCI) development relies critically on neural time series decoding algorithms.
Semantic Brain Decoding: from fMRI to conceptually similar image reconstruction of visual stimuli
matteoferrante/semantic-brain-decoding
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Brain decoding is a field of computational neuroscience that uses measurable brain activity to infer mental states or internal representations of perceptual inputs.
BrainCLIP: Bridging Brain and Visual-Linguistic Representation Via CLIP for Generic Natural Visual Stimulus Decoding
Our experiments show that this combination can boost the decoding model's performance on certain tasks like fMRI-text matching and fMRI-to-image generation.
Natural scene reconstruction from fMRI signals using generative latent diffusion
In the second stage, we use the image-to-image framework of a latent diffusion model (Versatile Diffusion) conditioned on predicted multimodal (text and visual) features, to generate final reconstructed images.
Contrast, Attend and Diffuse to Decode High-Resolution Images from Brain Activities
soinx0629/vis_dec_neurips
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NeurIPS 2023
The second phase tunes the feature learner to attend to neural activation patterns most informative for visual reconstruction with guidance from an image auto-encoder.
Second Sight: Using brain-optimized encoding models to align image distributions with human brain activity
reesekneeland/Second-Sight
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This emphasis belies the fact that there is always a family of images that are equally compatible with any evoked brain activity pattern, and the fact that many image-generators are inherently stochastic and do not by themselves offer a method for selecting the single best reconstruction from among the samples they generate.
Structural Similarities Between Language Models and Neural Response Measurements
Human language processing is also opaque, but neural response measurements can provide (noisy) recordings of activation during listening or reading, from which we can extract similar representations of words and phrases.
JGAT: a joint spatio-temporal graph attention model for brain decoding
brainml-gt/jgat
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However, traditional approaches for integrating FC and SC overlook the dynamical variations, which stand a great chance to over-generalize the brain neural network.
Memory Encoding Model
huzeyann/MemoryEncodingModel
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Our ensemble model without memory input (61. 4) can also stand a 3rd place.
Decoding visual brain representations from electroencephalography through Knowledge Distillation and latent diffusion models
Additionally, we incorporated an image reconstruction mechanism based on pre-trained latent diffusion models, which allowed us to generate an estimate of the images which had elicited EEG activity.
