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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 351–375 of 466 papers

TitleStatusHype
Attention Patterns Detection using Brain Computer Interfaces—0
Deep learning approaches for neural decoding: from CNNs to LSTMs and spikes to fMRI—0
Classification of Visual Perception and Imagery based EEG Signals Using Convolutional Neural Networks—0
Decoding of Intuitive Visual Motion Imagery Using Convolutional Neural Network under 3D-BCI Training Environment—0
Gradual Relation Network: Decoding Intuitive Upper Extremity Movement Imaginations Based on Few-Shot EEG Learning—0
Prediction of Memory Retrieval Performance Using Ear-EEG Signals—0
Deep Feature Mining via Attention-based BiLSTM-GCN for Human Motor Imagery Recognition—0
Brain-based control of car infotainment—0
Detecting Driver's Distraction using Long-term Recurrent Convolutional Network—0
Transfer Learning for EEG-Based Brain-Computer Interfaces: A Review of Progress Made Since 2016—0
Active recursive Bayesian inference using Rényi information measures—0
Human action recognition with a large-scale brain-inspired photonic computer—0
Investigating EEG-Based Functional Connectivity Patterns for Multimodal Emotion Recognition—0
An Accurate EEGNet-based Motor-Imagery Brain-Computer Interface for Low-Power Edge Computing—0
Mindfulness Improves Brain Computer Interface Performance by Increasing Control over Neural Activity in the Alpha Band—0
Multi-Scale Neural network for EEG Representation Learning in BCI—0
DeepBrain: Towards Personalized EEG Interaction through Attentional and Embedded LSTM Learning—0
Motor Imagery Classification of Single-Arm Tasks Using Convolutional Neural Network based on Feature Refining—0
Towards a Fast Steady-State Visual Evoked Potentials (SSVEP) Brain-Computer Interface (BCI)—0
Towards Brain-Computer Interfaces for Drone Swarm Control—0
Classification of High-Dimensional Motor Imagery Tasks based on An End-to-end role assigned convolutional neural networkCode0
MindReading: An Ultra-Low-Power Photonic Accelerator for EEG-based Human Intention Recognition—0
EEG-based Brain-Computer Interfaces (BCIs): A Survey of Recent Studies on Signal Sensing Technologies and Computational Intelligence Approaches and their Applications—0
EEG-based Drowsiness Estimation for Driving Safety using Deep Q-Learning—0
Capsule Attention for Multimodal EEG-EOG Representation Learning with Application to Driver Vigilance Estimation—0
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