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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 126–150 of 466 papers

TitleStatusHype
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
Improving EEG Signal Classification Accuracy Using Wasserstein Generative Adversarial NetworksCode1
Classification of Emerging Neural Activity from Planning to Grasp Execution using a Novel EEG-Based BCI Platform—0
Wavelet Analysis of Noninvasive EEG Signals Discriminates Complex and Natural Grasp Types—0
Stimulus-Informed Generalized Canonical Correlation Analysis for Group Analysis of Neural Responses to Natural StimuliCode0
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
Brain-Conditional Multimodal Synthesis: A Survey and TaxonomyCode1
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
DTP-Net: Learning to Reconstruct EEG signals in Time-Frequency Domain by Multi-scale Feature ReuseCode1
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
Neuro-GPT: Towards A Foundation Model for EEGCode2
Improved Motor Imagery Classification Using Adaptive Spatial Filters Based on Particle Swarm Optimization Algorithm—0
Neuromorphic Online Clustering and Classification—0
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