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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 151–200 of 466 papers

TitleStatusHype
EEG2TEXT: Open Vocabulary EEG-to-Text Decoding with EEG Pre-Training and Multi-View Transformer—0
Precision Enhancement in Sustained Visual Attention Training Platforms: Offline EEG Signal Analysis for Classifier Fine-Tuning—0
Quantifying Spatial Domain Explanations in BCI using Earth Mover's DistanceCode0
Optimizing Brain-Computer Interface Performance: Advancing EEG Signals Channel Selection through Regularized CSP and SPEA II Multi-Objective Optimization—0
Unveiling Thoughts: A Review of Advancements in EEG Brain Signal Decoding into Text—0
Evaluating Fast Adaptability of Neural Networks for Brain-Computer InterfaceCode0
MindArm: Mechanized Intelligent Non-Invasive Neuro-Driven Prosthetic Arm System—0
Psychometry: An Omnifit Model for Image Reconstruction from Human Brain Activity—0
Uncertainty Quantification for cross-subject Motor Imagery classificationCode0
Transferring BCI models from calibration to control: Observing shifts in EEG features—0
Geometric Neural Network based on Phase Space for BCI-EEG decodingCode0
Physics-informed and Unsupervised Riemannian Domain Adaptation for Machine Learning on Heterogeneous EEG Datasets—0
Spatiotemporal Pooling on Appropriate Topological Maps Represented as Two-Dimensional Images for EEG Classification—0
FingerNet: EEG Decoding of A Fine Motor Imagery with Finger-tapping Task Based on A Deep Neural Network—0
Stimulation technology for brain and nerves, now and future—0
Towards Decoding Brain Activity During Passive Listening of SpeechCode0
ArEEG_Chars: Dataset for Envisioned Speech Recognition using EEG for Arabic Characters—0
Early feasibility of an embedded bi-directional brain-computer interface for ambulation—0
Classification of Emerging Neural Activity from Planning to Grasp Execution using a Novel EEG-Based BCI Platform—0
Stimulus-Informed Generalized Canonical Correlation Analysis for Group Analysis of Neural Responses to Natural StimuliCode0
Wavelet Analysis of Noninvasive EEG Signals Discriminates Complex and Natural Grasp Types—0
Subject-Independent Deep Architecture for EEG-based Motor Imagery Classification—0
A Systematic Evaluation of Euclidean Alignment with Deep Learning for EEG Decoding—0
Using i-vectors for subject-independent cross-session EEG transfer learning—0
A Temporal-Spectral Fusion Transformer with Subject-Specific Adapter for Enhancing RSVP-BCI DecodingCode0
Multiagent Copilot Approach for Shared Autonomy between Human EEG and TD3 Deep Reinforcement Learning—0
3D-CLMI: A Motor Imagery EEG Classification Model via Fusion of 3D-CNN and LSTM with Attention—0
Brain Computer Interface Technology for Future Battlefield—0
Relevance Feedback with Brain SignalsCode0
A Literature Review on the Smart Wheelchair Systems—0
Visual tracking brain computer interface—0
Enhanced Generative Adversarial Networks for Unseen Word Generation from EEG Signals—0
Sample Dominance Aware Framework via Non-Parametric Estimation for Spontaneous Brain-Computer Interface—0
EEG-DG: A Multi-Source Domain Generalization Framework for Motor Imagery EEG ClassificationCode0
Improved Motor Imagery Classification Using Adaptive Spatial Filters Based on Particle Swarm Optimization Algorithm—0
Neuromorphic Online Clustering and Classification—0
Reputation-Based Federated Learning Defense to Mitigate Threats in EEG Signal Classification—0
A Convolutional Network Adaptation for Cortical Classification During Mobile Brain Imaging—0
Automatic Control of Reactive Brain Computer Interfaces—0
Is controlling a brain-computer interface just a matter of presence of mind? The limits of cognitive-motor dissociation—0
Unidirectional brain-computer interface: Artificial neural network encoding natural images to fMRI response in the visual cortexCode0
Phase Synchrony Component Self-Organization in Brain Computer Interface—0
A Dynamic Domain Adaptation Deep Learning Network for EEG-based Motor Imagery Classification—0
Brief Architectural Survey of Biopotential Recording Front-Ends since the 1970s—0
Bootstrapping Adaptive Human-Machine Interfaces with Offline Reinforcement Learning—0
Pseudo-online framework for BCI evaluation: A MOABB perspective—0
Advancing Brain-Computer Interface System Performance in Hand Trajectory Estimation with NeuroKinect—0
Aggregating Intrinsic Information to Enhance BCI Performance through Federated LearningCode0
A Brain-Computer Interface Augmented Reality Framework with Auto-Adaptive SSVEP Recognition—0
Deep Learning Architecture for Motor Imaged Words—0
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