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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 276–300 of 466 papers

TitleStatusHype
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
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
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
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
Are Brain-Computer Interfaces Feasible with Integrated Photonic Chips?—0
End-to-end translation of human neural activity to speech with a dual-dual generative adversarial network—0
Topological Data Analysis (TDA) Techniques Enhance Hand Pose Classification from ECoG Neural Recordings—0
A case study on profiling of an EEG-based brain decoding interface on Cloud and Edge servers—0
EEGDnet: Fusing Non-Local and Local Self-Similarity for 1-D EEG Signal Denoising with 2-D Transformer—0
Online Optimization of Stimulation Speed in an Auditory Brain-Computer Interface under Time Constraints—0
EEG-based Classification of Drivers Attention using Convolutional Neural Network—0
Generating Music and Generative Art from Brain activity—0
A SPA-based Manifold Learning Framework for Motor Imagery EEG Data Classification—0
Voxel selection framework based on meta-heuristic search and mutual information for brain decoding—0
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