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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 301–325 of 466 papers

TitleStatusHype
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
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
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
Toward asynchronous EEG-based BCI: Detecting imagined words segments in continuous EEG signalsCode0
Source Aware Deep Learning Framework for Hand Kinematic Reconstruction using EEG Signal—0
Classification of Motor Imagery EEG Signals by Using a Divergence Based Convolutional Neural NetworkCode0
A New Method for Features Normalization in Motor Imagery Few-Shot Learning using Resting-State—0
Inter-subject Deep Transfer Learning for Motor Imagery EEG Decoding—0
EmoWrite: A Sentiment Analysis-Based Thought to Text Conversion -- A Validation Study—0
Decoding Event-related Potential from Ear-EEG Signals based on Ensemble Convolutional Neural Networks in Ambulatory Environment—0
Real Time Vigilance Detection using Frontal EEG—0
Common Spatial Generative Adversarial Networks based EEG Data Augmentation for Cross-Subject Brain-Computer Interface—0
FOIT: Fast Online Instance Transfer for Improved EEG Emotion RecognitionCode0
Online LDA based brain-computer interface system to aid disabled people—0
Classification of fNIRS Data Under Uncertainty: A Bayesian Neural Network Approach—0
Motor-Imagery-Based Brain Computer Interface using Signal Derivation and Aggregation Functions—0
4D Attention-based Neural Network for EEG Emotion Recognition—0
Real or Virtual? Using Brain Activity Patterns to differentiate Attended Targets during Augmented Reality Scenarios—0
In-Ear SpO2 for Classification of Cognitive Workload—0
Towards Real-World BCI: CCSPNet, A Compact Subject-Independent Motor Imagery FrameworkCode0
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