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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 101125 of 466 papers

TitleStatusHype
MAD: Multi-Alignment MEG-to-Text DecodingCode1
Beware of Overestimated Decoding Performance Arising from Temporal Autocorrelations in Electroencephalogram Signals0
High Performance P300 Spellers Using GPT2 Word Prediction With Cross-Subject Training0
NeuroAssist: Enhancing Cognitive-Computer Synergy with Adaptive AI and Advanced Neural Decoding for Efficient EEG Signal Classification0
Subject-Adaptive Transfer Learning Using Resting State EEG Signals for Cross-Subject EEG Motor Imagery ClassificationCode1
JNEEG shield for Jetson Nano for real-time EEG signal processing with deep learning0
Precision Enhancement in Sustained Visual Attention Training Platforms: Offline EEG Signal Analysis for Classifier Fine-Tuning0
EEG2TEXT: Open Vocabulary EEG-to-Text Decoding with EEG Pre-Training and Multi-View Transformer0
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 Optimization0
Unveiling Thoughts: A Review of Advancements in EEG Brain Signal Decoding into Text0
Evaluating Fast Adaptability of Neural Networks for Brain-Computer InterfaceCode0
Psychometry: An Omnifit Model for Image Reconstruction from Human Brain Activity0
MindArm: Mechanized Intelligent Non-Invasive Neuro-Driven Prosthetic Arm System0
Synthesizing EEG Signals from Event-Related Potential Paradigms with Conditional Diffusion ModelsCode1
Towards gaze-independent c-VEP BCI: A pilot studyCode1
S-JEPA: towards seamless cross-dataset transfer through dynamic spatial attentionCode1
Transferring BCI models from calibration to control: Observing shifts in EEG features0
Uncertainty Quantification for cross-subject Motor Imagery classificationCode0
Geometric Neural Network based on Phase Space for BCI-EEG decodingCode0
Physics-informed and Unsupervised Riemannian Domain Adaptation for Machine Learning on Heterogeneous EEG Datasets0
Spatiotemporal Pooling on Appropriate Topological Maps Represented as Two-Dimensional Images for EEG Classification0
FingerNet: EEG Decoding of A Fine Motor Imagery with Finger-tapping Task Based on A Deep Neural Network0
NeuSpeech: Decode Neural signal as SpeechCode3
Stimulation technology for brain and nerves, now and future0
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