Brain Computer Interface
76 papers with code • 0 benchmarks • 0 datasets
A Brain-Computer Interface (BCI), also known as a Brain-Machine Interface (BMI), is a technology that enables direct communication between the brain and an external device, such as a computer or a machine, without the need for any muscular or peripheral nerve activity. Essentially, BCIs establish a direct pathway between the brain and an external device, allowing for bidirectional communication.
BCIs typically work by detecting and interpreting brain signals, which are then translated into commands that control external devices or provide feedback to the user. These brain signals can be detected through various methods, including electroencephalography (EEG), which measures electrical activity in the brain through electrodes placed on the scalp, or invasive techniques such as implanted electrodes.
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Libraries
Use these libraries to find Brain Computer Interface models and implementations
Most implemented papers
Sliced-Wasserstein on Symmetric Positive Definite Matrices for M/EEG Signals
clbonet/spdsw
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When dealing with electro or magnetoencephalography records, many supervised prediction tasks are solved by working with covariance matrices to summarize the signals.
LMDA-Net:A lightweight multi-dimensional attention network for general EEG-based brain-computer interface paradigms and interpretability
By incorporating two novel attention modules designed specifically for EEG signals, the channel attention module and the depth attention module, LMDA-Net can effectively integrate features from multiple dimensions, resulting in improved classification performance across various BCI tasks.
EEG motor imagery decoding: A framework for comparative analysis with channel attention mechanisms
martinwimpff/channel-attention
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The objective of this study is to investigate the application of various channel attention mechanisms within the domain of brain-computer interface (BCI) for motor imagery decoding.
Decode Neural signal as Speech
In this paper, we explore the brain-to-text translation of MEG signals in a speech-decoding formation.
Feature Weighting and Regularization of Common Spatial Patterns in EEG-Based Motor Imagery BCI
Electroencephalography signals have very low spatial resolution and electrodes capture signals that are overlapping each other.
Mining within-trial oscillatory brain dynamics to address the variability of optimized spatial filters
Based on electroencephalography data of 18 healthy subjects, we found that the components' distinct temporal envelope dynamics are highly subject-specific.
Exploring Embedding Methods in Binary Hyperdimensional Computing: A Case Study for Motor-Imagery based Brain-Computer Interfaces
All these methods, differing in complexity, aim to represent EEG signals in binary HD space, e. g. with 10, 000 bits.
Recurrent Neural Networks for P300-based BCI
Ori226/p300_lstm
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P300-based spellers are one of the main methods for EEG-based brain-computer interface, and the detection of the P300 target event with high accuracy is an important prerequisite.
Mutual Information-driven Subject-invariant and Class-relevant Deep Representation Learning in BCI
In this paper, we propose a novel framework that learns class-relevant and subject-invariant feature representations in an information-theoretic manner, without using adversarial learning.
Different Set Domain Adaptation for Brain-Computer Interfaces: A Label Alignment Approach
Currently, most domain adaptation approaches require the source domains to have the same feature space and label space as the target domain, which limits their applications, as the auxiliary data may have different feature spaces and/or different label spaces.
