Brain Decoding

23 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.

Latest papers with no code

Brain-grounding of semantic vectors improves neural decoding of visual stimuli

no code yet • 22 Mar 2024

To address this issue, we propose a representation learning framework, termed brain-grounding of semantic vectors, which fine-tunes pretrained feature vectors to better align with the neural representation of visual stimuli in the human brain.

Multimodal wearable EEG, EMG and accelerometry measurements improve the accuracy of tonic-clonic seizure detection in-hospital

no code yet • 19 Mar 2024

The combination of wearable EEG and EMG achieved overall the most clinically useful performance in offline TCS detection with a sensitivity of 97. 7%, a FPR of 0. 4/24 h, a precision of 43. 0%, and a F1-score of 59. 7%.

S-JEPA: towards seamless cross-dataset transfer through dynamic spatial attention

no code yet • 18 Mar 2024

Motivated by the challenge of seamless cross-dataset transfer in EEG signal processing, this article presents an exploratory study on the use of Joint Embedding Predictive Architectures (JEPAs).

Unsupervised Adaptive Deep Learning Method For BCI Motor Imagery Decoding

no code yet • 15 Mar 2024

In the context of Brain-Computer Interfaces, we propose an adaptive method that reaches offline performance level while being usable online without requiring supervision.

Data-Efficient Sleep Staging with Synthetic Time Series Pretraining

no code yet • 13 Mar 2024

Analyzing electroencephalographic (EEG) time series can be challenging, especially with deep neural networks, due to the large variability among human subjects and often small datasets.

Reconstructing Visual Stimulus Images from EEG Signals Based on Deep Visual Representation Model

no code yet • 11 Mar 2024

Considering the advantages of low cost and easy portability of the electroencephalogram (EEG) acquisition equipments, we propose a novel image reconstruction method based on EEG signals in this paper.

See Through Their Minds: Learning Transferable Neural Representation from Cross-Subject fMRI

no code yet • 11 Mar 2024

Our model integrates a high-level perception decoding pipeline and a pixel-wise reconstruction pipeline guided by high-level perceptions, simulating bottom-up and top-down processes in neuroscience.

FAST functional connectivity implicates P300 connectivity in working memory deficits in Alzheimer's disease

no code yet • 28 Feb 2024

The resulting average connectivity matrix, containing information on the strongest general connections for the tasks, is used as a filter to analyse the transient high temporal resolution functional connectivity of individual subjects.

Enhancing EEG-to-Text Decoding through Transferable Representations from Pre-trained Contrastive EEG-Text Masked Autoencoder

no code yet • 27 Feb 2024

Reconstructing natural language from non-invasive electroencephalography (EEG) holds great promise as a language decoding technology for brain-computer interfaces (BCIs).

Contrastive Learning of Shared Spatiotemporal EEG Representations Across Individuals for Naturalistic Neuroscience

no code yet • 22 Feb 2024

Targeting the Electroencephalogram (EEG) technique, known for its rich spatial and temporal information, this study presents a general framework for Contrastive Learning of Shared SpatioTemporal EEG Representations across individuals (CL-SSTER).