Brain Computer Interface
99 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
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Most implemented papers
Transformer-based Spatial-Temporal Feature Learning for EEG Decoding
anranknight/EEG-Transformer
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As far as we know, it is the first time that a detailed and complete method based on the transformer idea has been proposed in this field.
Priming Cross-Session Motor Imagery Classification with A Universal Deep Domain Adaptation Framework
Compared to the vanilla EEGNet and ConvNet, the proposed SDDA framework was able to boost the MI classification accuracy by 15. 2%, 10. 2% respectively in IIA dataset, and 5. 5%, 4. 2% in IIB dataset.
Using Riemannian geometry for SSVEP-based Brain Computer Interface
Riemannian geometry has been applied to Brain Computer Interface (BCI) for brain signals classification yielding promising results.
Converting Your Thoughts to Texts: Enabling Brain Typing via Deep Feature Learning of EEG Signals
xiangzhang1015/Brain_typing
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An electroencephalography (EEG) based Brain Computer Interface (BCI) enables people to communicate with the outside world by interpreting the EEG signals of their brains to interact with devices such as wheelchairs and intelligent robots.
Fast and Accurate Multiclass Inference for MI-BCIs Using Large Multiscale Temporal and Spectral Features
Accurate, fast, and reliable multiclass classification of electroencephalography (EEG) signals is a challenging task towards the development of motor imagery brain-computer interface (MI-BCI) systems.
PhyAAt: Physiology of Auditory Attention to Speech Dataset
In this article, we present a dataset of physiological signals collected from an experiment on auditory attention to natural speech.
CNN-based Approaches For Cross-Subject Classification in Motor Imagery: From The State-of-The-Art to DynamicNet
In this paper, we first present a review of the most recent studies using deep learning for MI classification, with particular attention to their cross-subject performance.
BEATS: An Open-Source, High-Precision, Multi-Channel EEG Acquisition Tool System
Commonly used EEG acquisition system's hardware and software are usually closed-source.
Physics-inform attention temporal convolutional network for EEG-based motor imagery classification
Altaheri/EEG-ATCNet
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IEEE Transactions on Industrial Informatics 2022
In this paper, we propose an attention-based temporal convolutional network (ATCNet) for EEG-based motor imagery classification.
A Transformer-based deep neural network model for SSVEP classification
The proposed model validates the feasibility of deep learning models based on Transformer structure for SSVEP classification task, and could serve as a potential model to alleviate the calibration procedure in the practical application of SSVEP-based BCI systems.
