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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 276–300 of 466 papers

TitleStatusHype
Are Brain-Computer Interfaces Feasible with Integrated Photonic Chips?—0
Functional connectivity ensemble method to enhance BCI performance (FUCONE)Code1
End-to-end translation of human neural activity to speech with a dual-dual generative adversarial network—0
Topological Data Analysis (TDA) Techniques Enhance Hand Pose Classification from ECoG Neural Recordings—0
A case study on profiling of an EEG-based brain decoding interface on Cloud and Edge servers—0
Low-cost brain computer interface for everyday useCode1
EEGDnet: Fusing Non-Local and Local Self-Similarity for 1-D EEG Signal Denoising with 2-D Transformer—0
Online Optimization of Stimulation Speed in an Auditory Brain-Computer Interface under Time Constraints—0
EEG-based Classification of Drivers Attention using Convolutional Neural Network—0
Generating Music and Generative Art from Brain activity—0
A SPA-based Manifold Learning Framework for Motor Imagery EEG Data Classification—0
Voxel selection framework based on meta-heuristic search and mutual information for brain decoding—0
Classification of Upper Arm Movements from EEG signals using Machine Learning with ICA Analysis—0
DAL: Feature Learning from Overt Speech to Decode Imagined Speech-based EEG Signals with Convolutional Autoencoder—0
EEG-ConvTransformer for Single-Trial EEG based Visual Stimuli ClassificationCode1
Complex common spatial patterns on time-frequency decomposed EEG for brain-computer interfaceCode0
Partial Maximum Correntropy Regression for Robust Trajectory Decoding from Noisy Epidural Electrocorticographic Signals—0
Transformer-based Spatial-Temporal Feature Learning for EEG DecodingCode1
Wheelchair automation by a hybrid BCI system using SSVEP and eye blinks—0
Subject-Independent Brain-Computer Interface for Decoding High-Level Visual Imagery Tasks—0
Generating Ten BCI Commands Using Four Simple Motor Imageries—0
An overview of deep learning techniques for epileptic seizures detection and prediction based on neuroimaging modalities: Methods, challenges, and future works—0
CNN-based Approaches For Cross-Subject Classification in Motor Imagery: From The State-of-The-Art to DynamicNetCode1
LGGNet: Learning from Local-Global-Graph Representations for Brain-Computer InterfaceCode1
A magnetoencephalography dataset for motor and cognitive imagery-based brain-computer interfaceCode1
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