Papers Brain Computer Interface
“Brain Computer Interface” 태그가 달린 논문 466편 · 필터 해제
A Low-complexity Brain-computer Interface for High-complexity Robot Swarm Control
A brain-computer interface (BCI) is a system that allows a human operator to use only mental commands in controlling end effectors that interact with the world around them. Such a system consists of a measurement device …
Brain Computer InterfaceAn Adaptive Contrastive Learning Model for Spike Sorting
Brain-computer interfaces (BCIs), is ways for electronic devices to communicate directly with the brain. For most medical-type brain-computer interface tasks, the activity of multiple units of neurons or local field pote…
Binary ClassificationBrain Computer InterfaceClassificationContrastive Learning+2Bayesian Networks for Brain-Computer Interfaces: A Survey
Brain-Computer Interface (BCI) is a rapidly developing technology that allows direct communications between the human brain and external devices, such as robotic arms and computers. Bayesian Networks is a powerful tool i…
Brain Computer InterfaceSurveyIFTT-PIN: A PIN-Entry Method Leveraging the Self-Calibration Paradigm
IFTT-PIN is a self-calibrating version of the PIN-entry method introduced in Roth et al. (2004) [1]. In [1], digits are split into two sets and assigned a color respectively. To communicate their digit, users press the b…
Brain Computer InterfaceHuman Emotion Classification based on EEG Signals Using Recurrent Neural Network And KNN
In human contact, emotion is very crucial. Attributes like words, voice intonation, facial expressions, and kinesics can all be used to portray one's feelings. However, brain-computer interface (BCI) devices have not yet…
Brain Computer Interfacechannel selectionEEGElectroencephalogram (EEG)+2PreMovNet: Pre-Movement EEG-based Hand Kinematics Estimation for Grasp and Lift task
Kinematics decoding from brain activity helps in developing rehabilitation or power-augmenting brain-computer interface devices. Low-frequency signals recorded from non-invasive electroencephalography (EEG) are associate…
Brain Computer InterfaceEEGElectroencephalogram (EEG)Unsupervised Motor Imagery Saliency Detection Based on Self-Attention Mechanism
Detecting the salient parts of motor-imagery electroencephalogram (MI-EEG) signals can enhance the performance of the brain-computer interface (BCI) system and reduce the computational burden required for processing leng…
Brain Computer InterfaceEEGElectroencephalogram (EEG)Motor Imagery+1A Subject-Independent Brain-Computer Interface Framework Based on Supervised Autoencoder
A calibration procedure is required in motor imagery-based brain-computer interface (MI-BCI) to tune the system for new users. This procedure is time-consuming and prevents na\"ive users from using the system immediately…
Brain Computer InterfaceMotor ImageryPrototype-based Domain Generalization Framework for Subject-Independent Brain-Computer Interfaces
Brain-computer interface (BCI) is challenging to use in practice due to the inter/intra-subject variability of electroencephalography (EEG). The BCI system, in general, necessitates a calibration technique to obtain subj…
Brain Computer InterfaceDomain GeneralizationEEGElectroencephalogram (EEG)+1Decoding Neural Correlation of Language-Specific Imagined Speech using EEG Signals
Speech impairments due to cerebral lesions and degenerative disorders can be devastating. For humans with severe speech deficits, imagined speech in the brain-computer interface has been a promising hope for reconstructi…
Brain Computer InterfaceEEGElectroencephalogram (EEG)A self-paced BCI system with low latency for motor imagery onset detection based on time series prediction paradigm
In a self-paced motor-imagery brain-computer interface (MI-BCI), the onsets of the MI commands presented in a continuous electroencephalogram (EEG) signal are unknown. To detect these onsets, most self-paced approaches a…
Brain Computer InterfaceDecoderEEGElectroencephalogram (EEG)+5Fuzzy temporal convolutional neural networks in P300-based Brain-computer interface for smart home interaction
The processing and classification of electroencephalographic signals (EEG) are increasingly performed using deep learning frameworks, such as convolutional neural networks (CNNs), to generate abstract features from brain…
Brain Computer InterfaceClassificationEEGElectroencephalogram (EEG)On the Handwriting Tasks' Analysis to Detect Fatigue
Practical determination of physical recovery after intense exercise is a challenging topic that must include mechanical aspects as well as cognitive ones because most of physical sport activities, as well as professional…
Brain Computer InterfaceA Spiking Neural Network based on Neural Manifold for Augmenting Intracortical Brain-Computer Interface Data
Brain-computer interfaces (BCIs), transform neural signals in the brain into in-structions to control external devices. However, obtaining sufficient training data is difficult as well as limited. With the advent of adva…
Brain Computer InterfaceData AugmentationDecoderBEATS: An Open-Source, High-Precision, Multi-Channel EEG Acquisition Tool System
Stable and accurate electroencephalogram (EEG) signal acquisition is fundamental in non-invasive brain-computer interface (BCI) technology. Commonly used EEG acquisition system's hardware and software are usually closed-…
4kBrain Computer InterfaceEEGElectroencephalogram (EEG)X2T: Training an X-to-Text Typing Interface with Online Learning from User Feedback
We aim to help users communicate their intent to machines using flexible, adaptive interfaces that translate arbitrary user input into desired actions. In this work, we focus on assistive typing applications in which a u…
Brain Computer InterfaceFast and accurate decoding of finger movements from ECoG through Riemannian features and modern machine learning techniques
Objective.Accurate decoding of individual finger movements is crucial for advanced prosthetic control. In this work, we introduce the use of Riemannian-space features and temporal dynamics of electrocorticography (ECoG) …
Brain Computer InterfaceBrain DecodingDGAFF: Deep Genetic Algorithm Fitness Formation for EEG Bio-Signal Channel Selection
Brain-computer interface systems aim to facilitate human-computer interactions in a great deal by direct translation of brain signals for computers. Recently, using many electrodes has caused better performance in these …
Brain Computer Interfacechannel selectionEEGElectroencephalogram (EEG)+1Priming Cross-Session Motor Imagery Classification with A Universal Deep Domain Adaptation Framework
Motor imagery (MI) is a common brain computer interface (BCI) paradigm. EEG is non-stationary with low signal-to-noise, classifying motor imagery tasks of the same participant from different EEG recording sessions is gen…
Brain Computer InterfaceDomain AdaptationEEGElectroencephalogram (EEG)+1Raspberry PI Shield - for measure EEG (PIEEG)
Abstract: The paper presented the easiest way to the neuroscience world with a shield for RaspberryPI - PIEEG. Over the past decade, advances in artificial intelligence have expanded and reshaped many industries. It als…
Brain Computer InterfaceEEGElectroencephalogram (EEG)