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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 326–350 of 466 papers

TitleStatusHype
Mental arithmetic task classification with convolutional neural network based on spectral-temporal features from EEG—0
Method for Evaluating the Number of Signal Sources and Application to Non-invasive Brain-computer Interface—0
MindArm: Mechanized Intelligent Non-Invasive Neuro-Driven Prosthetic Arm System—0
Mindfulness Improves Brain Computer Interface Performance by Increasing Control over Neural Activity in the Alpha Band—0
MindReading: An Ultra-Low-Power Photonic Accelerator for EEG-based Human Intention Recognition—0
Minimally Invasive Brain Computer Interfaces: Evaluating the Impact of Tissue Layers on Signal Quality of Sub-Scalp EEG—0
Minimizing inter-subject variability in fNIRS based Brain Computer Interfaces via multiple-kernel support vector learning—0
Motor-Imagery-Based Brain Computer Interface using Signal Derivation and Aggregation Functions—0
Motor Imagery Classification of Single-Arm Tasks Using Convolutional Neural Network based on Feature Refining—0
Motor imagery classification using EEG spectrograms—0
Motor Imagery EEG Signals: Multi-Task Classification and Subject Identification with a Lightweight CNN—0
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
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