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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 226–250 of 466 papers

TitleStatusHype
Multiagent Copilot Approach for Shared Autonomy between Human EEG and TD3 Deep Reinforcement Learning—0
Multiclass Common Spatial Pattern for EEG based Brain Computer Interface with Adaptive Learning Classifier—0
Multimodal Brain-Computer Interface for In-Vehicle Driver Cognitive Load Measurement: Dataset and Baselines—0
Multimodal Classification with Deep Convolutional-Recurrent Neural Networks for Electroencephalography—0
Multi-Scale Neural network for EEG Representation Learning in BCI—0
Mu-suppression detection in motor imagery electroencephalographic signals using the generalized extreme value distribution—0
Navigating in Virtual Reality using Thought: The Development and Assessment of a Motor Imagery based Brain-Computer Interface—0
Neural Network-Based Feature Extraction for Multi-Class Motor Imagery Classification—0
NeuroAssist: Enhancing Cognitive-Computer Synergy with Adaptive AI and Advanced Neural Decoding for Efficient EEG Signal Classification—0
Neuromorphic Online Clustering and Classification—0
Neurophysiological Analysis in Motor and Sensory Cortices for Improving Motor Imagination—0
Neuroprosthetic decoder training as imitation learning—0
Neurorehab: An Interface for Rehabilitation—0
Noisy Neural Language Modeling for Typing Prediction in BCI Communication—0
Non-invasive two-step strategy BCI: brain-muscle-hand interface—0
Offline EEG-Based Driver Drowsiness Estimation Using Enhanced Batch-Mode Active Learning (EBMAL) for Regression—0
On-device Learning of EEGNet-based Network For Wearable Motor Imagery Brain-Computer Interface—0
On improving learning capability of ELM and an application to brain-computer interface—0
Online and Offline Domain Adaptation for Reducing BCI Calibration Effort—0
Online LDA based brain-computer interface system to aid disabled people—0
Online Optimization of Stimulation Speed in an Auditory Brain-Computer Interface under Time Constraints—0
On Questions of Predictability and Control of an Intelligent System Using Probabilistic State-Transitions—0
On The Effects Of Data Normalisation For Domain Adaptation On EEG Data—0
On the Handwriting Tasks' Analysis to Detect Fatigue—0
On the Vulnerability of CNN Classifiers in EEG-Based BCIs—0
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