Papers Brain Computer Interface
“Brain Computer Interface” 태그가 달린 논문 466편 · 필터 해제
Robust alignment of cross-session recordings of neural population activity by behaviour via unsupervised domain adaptation
Neural population activity relating to behaviour is assumed to be inherently low-dimensional despite the observed high dimensionality of data recorded using multi-electrode arrays. Therefore, predicting behaviour from ne…
Brain Computer InterfaceDecoderDomain AdaptationUnsupervised Domain AdaptationSpectrally Adaptive Common Spatial Patterns
The method of Common Spatial Patterns (CSP) is widely used for feature extraction of electroencephalography (EEG) data, such as in motor imagery brain-computer interface (BCI) systems. It is a data-driven method estimati…
Brain Computer InterfaceEEGElectroencephalogram (EEG)Motor ImageryAI-based artistic representation of emotions from EEG signals: a discussion on fairness, inclusion, and aesthetics
While Artificial Intelligence (AI) technologies are being progressively developed, artists and researchers are investigating their role in artistic practices. In this work, we present an AI-based Brain-Computer Interface…
Brain Computer InterfaceEEGElectroencephalogram (EEG)FairnessBrain-Computer-Interface controlled robot via RaspberryPi and PiEEG
This paper presents Open-source software and a developed shield board for the Raspberry Pi family of single-board computers that can be used to read EEG signals. We have described the mechanism for reading EEG signals an…
Brain Computer InterfaceEEGElectroencephalogram (EEG)Deep Learning in fNIRS: A review
Significance: Optical neuroimaging has become a well-established clinical and research tool to monitor cortical activations in the human brain. It is notable that outcomes of functional Near-InfraRed Spectroscopy (fNIRS)…
Brain Computer InterfaceClassificationData AugmentationDeep LearningEnhanced motor imagery-based EEG classification using a discriminative graph Fourier subspace
Dealing with irregular domains, graph signal processing (GSP) has attracted much attention especially in brain imaging analysis. Motor imagery tasks are extensively utilized in brain-computer interface (BCI) systems that…
Brain Computer InterfaceClassificationEEGElectroencephalogram (EEG)+1Deep Optimal Transport for Domain Adaptation on SPD Manifolds
Recent progress in geometric deep learning has drawn increasing attention from the machine learning community toward domain adaptation on symmetric positive definite (SPD) manifolds, especially for neuroimaging data that…
Brain Computer InterfaceDomain AdaptationElectroencephalogram (EEG)Transfer LearningApplication of Common Spatial Patterns in Gravitational Waves Detection
Common Spatial Patterns (CSP) is a feature extraction algorithm widely used in Brain-Computer Interface (BCI) Systems for detecting Event-Related Potentials (ERPs) in multi-channel magneto/electroencephalography (MEG/EEG…
Brain Computer InterfaceEEGElectroencephalogram (EEG)regression+2A 1D CNN for high accuracy classification and transfer learning in motor imagery EEG-based brain-computer interface
Objective. Brain-computer interface (BCI) aims to establish communication paths between the brain processes and external devices. Different methods have been used to extract human intentions from electroencephalography (…
Brain Computer InterfaceData AugmentationEEGElectroencephalogram (EEG)+2Neural Network-Based Feature Extraction for Multi-Class Motor Imagery Classification
Decoding of motor imagery (MI) from Electroencephalogram (EEG) is an important component of the Brain-Computer Interface (BCI) system that helps motor-disabled people interact with the outside world via external devices.…
Brain Computer InterfaceEEGElectroencephalogram (EEG)Motor ImageryAutomatic Muscle Artifacts Identification and Removal from Single-Channel EEG Using Wavelet Transform with Meta-heuristically Optimized Non-local Means Filter
Electroencephalogram (EEG) signals may get easily contaminated by muscle artifacts, which may lead to wrong interpretation in the brain--computer interface (BCI) system as well as in various medical diagnoses. The main o…
Brain Computer InterfaceDenoisingEEGEEG Denoising+1A Survey on Brain-Computer Interaction
Brain-Computer Interface(BCI) systems support communication through direct measures of neural activity without muscle activity. Brain-Computer Interface systems need to be validated in long-term studies of real-world use…
Brain Computer InterfaceSurveyLearning shared neural manifolds from multi-subject FMRI data
Functional magnetic resonance imaging (fMRI) is a notoriously noisy measurement of brain activity because of the large variations between individuals, signals marred by environmental differences during collection, and sp…
Brain Computer InterfaceDecoderConfidence-Aware Subject-to-Subject Transfer Learning for Brain-Computer Interface
The inter/intra-subject variability of electroencephalography (EEG) makes the practical use of the brain-computer interface (BCI) difficult. In general, the BCI system requires a calibration procedure to tune the model e…
Brain Computer InterfaceEEGElectroencephalogram (EEG)Subject Transfer+1Interpretable Convolutional Neural Networks for Subject-Independent Motor Imagery Classification
Deep learning frameworks have become increasingly popular in brain computer interface (BCI) study thanks to their outstanding performance. However, in terms of the classification model alone, they are treated as black bo…
Brain Computer InterfaceEEGElectroencephalogram (EEG)Motor ImageryAre Brain-Computer Interfaces Feasible with Integrated Photonic Chips?
The present paper examines the viability of a radically novel idea for brain-computer interface (BCI), which could lead to novel technological, experimental and clinical applications. BCIs are computer-based systems that…
Brain Computer InterfaceEEGElectroencephalogram (EEG)Functional connectivity ensemble method to enhance BCI performance (FUCONE)
Functional connectivity is a key approach to investigate oscillatory activities of the brain that provides important insights on the underlying dynamic of neuronal interactions and that is mostly applied for brain activi…
Brain Computer InterfaceDiversityFunctional ConnectivityMotor ImageryEnd-to-end translation of human neural activity to speech with a dual-dual generative adversarial network
In a recent study of auditory evoked potential (AEP) based brain-computer interface (BCI), it was shown that, with an encoder-decoder framework, it is possible to translate human neural activity to speech (T-CAS). Howeve…
Brain Computer InterfaceDecoderDimensionality ReductionEEG+4Topological Data Analysis (TDA) Techniques Enhance Hand Pose Classification from ECoG Neural Recordings
Electrocorticogram (ECoG) well characterizes hand movement intentions and gestures. In the present work we aim to investigate the possibility to enhance hand pose classification, in a Rock-Paper-Scissor - and Rest - task…
Brain Computer InterfaceFeature ImportanceHyperparameter OptimizationModel Selection+3A case study on profiling of an EEG-based brain decoding interface on Cloud and Edge servers
Brain-Computer Interfaces (BCIs) enable converting the brain electrical activity of an interface user to the user commands. BCI research studies demonstrated encouraging results in different areas such as neurorehabilita…
Brain Computer InterfaceBrain DecodingEEGElectroencephalogram (EEG)