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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 76–100 of 466 papers

TitleStatusHype
Are Brain-Computer Interfaces Feasible with Integrated Photonic Chips?—0
An overview of deep learning techniques for epileptic seizures detection and prediction based on neuroimaging modalities: Methods, challenges, and future works—0
Application of Common Spatial Patterns in Gravitational Waves Detection—0
Agreement Rate Initialized Maximum Likelihood Estimator for Ensemble Classifier Aggregation and Its Application in Brain-Computer Interface—0
A Computationally Efficient Multiclass Time-Frequency Common Spatial Pattern Analysis on EEG Motor Imagery—0
A Novel Semi-supervised Meta Learning Method for Subject-transfer Brain-computer Interface—0
An intertwined neural network model for EEG classification in brain-computer interfaces—0
A3E: Aligned and Augmented Adversarial Ensemble for Accurate, Robust and Privacy-Preserving EEG Decoding—0
A New Method for Features Normalization in Motor Imagery Few-Shot Learning using Resting-State—0
A GA-based feature selection of the EEG signals by classification evaluation: Application in BCI systems—0
4D Attention-based Neural Network for EEG Emotion Recognition—0
Covariance Density Neural Networks—0
Classification of Electroencephalograms during Mathematical Calculations Using Deep Learning—0
Classification of fNIRS Data Under Uncertainty: A Bayesian Neural Network Approach—0
Brief Architectural Survey of Biopotential Recording Front-Ends since the 1970s—0
BrainStratify: Coarse-to-Fine Disentanglement of Intracranial Neural Dynamics—0
A Neural-Inspired Architecture for EEG-Based Auditory Attention Detection—0
Brain informed transfer learning for categorizing construction hazards—0
An Ensemble Learning Based Classification of Individual Finger Movement from EEG—0
A neuro-inspired system for online learning and recognition of parallel spike trains, based on spike latency and heterosynaptic STDP—0
Canonical Polyadic Decomposition with Auxiliary Information for Brain Computer Interface—0
Capsule Attention for Multimodal EEG-EOG Representation Learning with Application to Driver Vigilance Estimation—0
Channel Reflection: Knowledge-Driven Data Augmentation for EEG-Based Brain-Computer Interfaces—0
Chromatic and High-frequency cVEP-based BCI Paradigm—0
A Dynamic Domain Adaptation Deep Learning Network for EEG-based Motor Imagery Classification—0
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