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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
Cross-Correlation Based Discriminant Criterion for Channel Selection in Motor Imagery BCI Systems—0
Deep Learning in EEG: Advance of the Last Ten-Year Critical Period—0
Interval-valued aggregation functions based on moderate deviations applied to Motor-Imagery-Based Brain Computer Interface—0
A Deep Neural Network for SSVEP-based Brain-Computer InterfacesCode1
Deep learning-based classification of fine hand movements from low frequency EEG—0
Learning Patterns in Imaginary Vowels for an Intelligent Brain Computer Interface (BCI) Design—0
Binarization Methods for Motor-Imagery Brain-Computer Interface Classification—0
Electroencephalography signal processing based on textural features for monitoring the driver's state by a Brain-Computer Interface—0
The "Sound of Silence" in EEG -- Cognitive voice activity detection—0
Transfer Learning and SpecAugment applied to SSVEP Based BCI Classification—0
Analysis of artifacts in EEG signals for building BCIs—0
A 6.3-Nanowatt-per-Channel 96-Channel Neural Spike Processor for a Movement-Intention-Decoding Brain-Computer-Interface Implant—0
To Root Artificial Intelligence Deeply in Basic Science for a New Generation of AI—0
A Computationally Efficient Multiclass Time-Frequency Common Spatial Pattern Analysis on EEG Motor Imagery—0
Revisiting the Application of Feature Selection Methods to Speech Imagery BCI Datasets—0
Hybrid Template Canonical Correlation Analysis Method for Enhancing SSVEP Recognition under data-limited Condition—0
Selection of Proper EEG Channels for Subject Intention Classification Using Deep Learning—0
GCNs-Net: A Graph Convolutional Neural Network Approach for Decoding Time-resolved EEG Motor Imagery SignalsCode2
Classification and Recognition of Encrypted EEG Data Neural Network—0
Constrained Variational Autoencoder for improving EEG based Speech Recognition Systems—0
PhyAAt: Physiology of Auditory Attention to Speech DatasetCode0
Mu-suppression detection in motor imagery electroencephalographic signals using the generalized extreme value distribution—0
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
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