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Brain Computer Interface

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.

Papers

Showing 8190 of 466 papers

TitleStatusHype
EEG2GAIT: A Hierarchical Graph Convolutional Network for EEG-based Gait Decoding0
Edge-Fog Computing-Enabled EEG Data Compression via Asymmetrical Variational Discrete Cosine Transform Network0
On Questions of Predictability and Control of an Intelligent System Using Probabilistic State-Transitions0
Motor Imagery EEG Signals: Multi-Task Classification and Subject Identification with a Lightweight CNN0
Integrating Biological and Machine Intelligence: Attention Mechanisms in Brain-Computer Interfaces0
Minima Possible Weights: A Homogenous Deep Ensemble Method for Cross-Subject Motor Imagery ClassificationCode0
SSVEP-BiMA: Bifocal Masking Attention Leveraging Native and Symmetric-Antisymmetric Components for Robust SSVEP Decoding0
CSSSTN: A Class-sensitive Subject-to-subject Semantic Style Transfer Network for EEG Classification in RSVP TasksCode0
ISAM-MTL: Cross-subject multi-task learning model with identifiable spikes and associative memory networks0
Interpretable Dual-Filter Fuzzy Neural Networks for Affective Brain-Computer Interfaces0
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