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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

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Showing 251–275 of 466 papers

TitleStatusHype
A self-paced BCI system with low latency for motor imagery onset detection based on time series prediction paradigm—0
Fuzzy temporal convolutional neural networks in P300-based Brain-computer interface for smart home interaction—0
On the Handwriting Tasks' Analysis to Detect Fatigue—0
A Spiking Neural Network based on Neural Manifold for Augmenting Intracortical Brain-Computer Interface Data—0
X2T: Training an X-to-Text Typing Interface with Online Learning from User Feedback—0
BEATS: An Open-Source, High-Precision, Multi-Channel EEG Acquisition Tool SystemCode1
Fast and accurate decoding of finger movements from ECoG through Riemannian features and modern machine learning techniques—0
DGAFF: Deep Genetic Algorithm Fitness Formation for EEG Bio-Signal Channel Selection—0
Priming Cross-Session Motor Imagery Classification with A Universal Deep Domain Adaptation FrameworkCode1
Raspberry PI Shield - for measure EEG (PIEEG)Code2
Robust alignment of cross-session recordings of neural population activity by behaviour via unsupervised domain adaptation—0
Spectrally Adaptive Common Spatial Patterns—0
AI-based artistic representation of emotions from EEG signals: a discussion on fairness, inclusion, and aestheticsCode0
Brain-Computer-Interface controlled robot via RaspberryPi and PiEEGCode2
Deep Learning in fNIRS: A review—0
Enhanced motor imagery-based EEG classification using a discriminative graph Fourier subspace—0
Deep Optimal Transport for Domain Adaptation on SPD ManifoldsCode0
Application of Common Spatial Patterns in Gravitational Waves Detection—0
A 1D CNN for high accuracy classification and transfer learning in motor imagery EEG-based brain-computer interfaceCode1
Automatic Muscle Artifacts Identification and Removal from Single-Channel EEG Using Wavelet Transform with Meta-heuristically Optimized Non-local Means Filter—0
Neural Network-Based Feature Extraction for Multi-Class Motor Imagery Classification—0
A Survey on Brain-Computer Interaction—0
Learning shared neural manifolds from multi-subject FMRI data—0
Confidence-Aware Subject-to-Subject Transfer Learning for Brain-Computer Interface—0
Interpretable Convolutional Neural Networks for Subject-Independent Motor Imagery Classification—0
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