Papers Brain Computer Interface
“Brain Computer Interface” 태그가 달린 논문 466편 · 필터 해제
Reconfiguration of Brain Network between Resting-state and Oddball Paradigm
The oddball paradigm is widely applied to the investigation of multiple cognitive functions. Prior studies have explored the cortical oscillation and power spectral differing from the resting-state conduction to oddball …
Brain Computer InterfaceEEGElectroencephalogram (EEG)Towards Asynchronous Motor Imagery-Based Brain-Computer Interfaces: a joint training scheme using deep learning
In this paper, the deep learning (DL) approach is applied to a joint training scheme for asynchronous motor imagery-based Brain-Computer Interface (BCI). The proposed DL approach is a cascade of one-dimensional convoluti…
Brain Computer InterfaceEEGElectroencephalogram (EEG)Motor ImageryDeep Transfer Learning for EEG-based Brain Computer Interface
The electroencephalography classifier is the most important component of brain-computer interface based systems. There are two major problems hindering the improvement of it. First, traditional methods do not fully explo…
Brain Computer InterfaceEEGElectroencephalogram (EEG)Optical Flow Estimation+1Classification of EEG Signal based on non-Gaussian Neutral Vector
In the design of brain-computer interface systems, classification of Electroencephalogram (EEG) signals is the essential part and a challenging task. Recently, as the marginalized discrete wavelet transform (mDWT) repres…
Brain Computer InterfaceClassificationEEGEEG Signal Classification+3Multimodal Classification with Deep Convolutional-Recurrent Neural Networks for Electroencephalography
Electroencephalography (EEG) has become the most significant input signal for brain computer interface (BCI) based systems. However, it is very difficult to obtain satisfactory classification accuracy due to traditional …
Brain Computer InterfaceClassificationEEGElectroencephalogram (EEG)+3A neuro-inspired system for online learning and recognition of parallel spike trains, based on spike latency and heterosynaptic STDP
Humans perform remarkably well in many cognitive tasks including pattern recognition. However, the neuronal mechanisms underlying this process are not well understood. Nevertheless, artificial neural networks, inspired i…
Brain Computer InterfaceImproving brain computer interface performance by data augmentation with conditional Deep Convolutional Generative Adversarial Networks
One of the big restrictions in brain computer interface field is the very limited training samples, it is difficult to build a reliable and usable system with such limited data. Inspired by generative adversarial network…
Brain Computer InterfaceData AugmentationEEGElectroencephalogram (EEG)+2Fast and Accurate Multiclass Inference for MI-BCIs Using Large Multiscale Temporal and Spectral Features
Accurate, fast, and reliable multiclass classification of electroencephalography (EEG) signals is a challenging task towards the development of motor imagery brain-computer interface (MI-BCI) systems. We propose enhancem…
Brain Computer InterfaceClassificationEEGElectroencephalogram (EEG)+1Brain-Computer Interface with Corrupted EEG Data: A Tensor Completion Approach
One of the current issues in Brain-Computer Interface is how to deal with noisy Electroencephalography measurements organized as multidimensional datasets. On the other hand, recently, significant advances have been made…
Brain Computer InterfaceClassificationEEGElectroencephalogram (EEG)+3A Multi-Context Character Prediction Model for a Brain-Computer Interface
Brain-computer interfaces and other augmentative and alternative communication devices introduce language-modeing challenges distinct from other character-entry methods. In particular, the acquired signal of the EEG (ele…
Brain Computer InterfaceEEGElectroencephalogram (EEG)Language Modeling+1Adaptive neural network classifier for decoding MEG signals
Convolutional Neural Networks (CNN) outperform traditional classification methods in many domains. Recently these methods have gained attention in neuroscience and particularly in brain-computer interface (BCI) community…
Brain Computer InterfaceClassificationEEGElectroencephalogram (EEG)+1Agreement Rate Initialized Maximum Likelihood Estimator for Ensemble Classifier Aggregation and Its Application in Brain-Computer Interface
Ensemble learning is a powerful approach to construct a strong learner from multiple base learners. The most popular way to aggregate an ensemble of classifiers is majority voting, which assigns a sample to the class tha…
Brain Computer InterfaceEnsemble LearningGeneral ClassificationOffline EEG-Based Driver Drowsiness Estimation Using Enhanced Batch-Mode Active Learning (EBMAL) for Regression
There are many important regression problems in real-world brain-computer interface (BCI) applications, e.g., driver drowsiness estimation from EEG signals. This paper considers offline analysis: given a pool of unlabele…
Active LearningBrain Computer InterfaceDiversityEEG+2Active Semi-supervised Transfer Learning (ASTL) for Offline BCI Calibration
Single-trial classification of event-related potentials in electroencephalogram (EEG) signals is a very important paradigm of brain-computer interface (BCI). Because of individual differences, usually some subject-specif…
Active LearningBrain Computer InterfaceEEGElectroencephalogram (EEG)+2Covariate Shift Estimation based Adaptive Ensemble Learning for Handling Non-Stationarity in Motor Imagery related EEG-based Brain-Computer Interface
The non-stationary nature of electroencephalography (EEG) signals makes an EEG-based brain-computer interface (BCI) a dynamic system, thus improving its performance is a challenging task. In addition, it is well-known th…
Brain Computer InterfaceEEGElectroencephalogram (EEG)Ensemble Learning+1Mining within-trial oscillatory brain dynamics to address the variability of optimized spatial filters
Data-driven spatial filtering algorithms optimize scores such as the contrast between two conditions to extract oscillatory brain signal components. Most machine learning approaches for filter estimation, however, disreg…
Brain Computer InterfaceFeature Weighting and Regularization of Common Spatial Patterns in EEG-Based Motor Imagery BCI
Electroencephalography signals have very low spatial resolution and electrodes capture signals that are overlapping each other. To extract the discriminative features and alleviate overfitting problem for motor imagery b…
Brain Computer InterfaceEEGElectroencephalogram (EEG)feature selection+1A Technique Based on Chaos for Brain Computer Interfacing
A user of Brain Computer Interface (BCI) system must be able to control external computer devices with brain activity. Although the proof-of-concept was given decades ago, the reliable translation of user intent into dev…
Brain Computer InterfaceClassificationClusteringEEG+3Multiclass Common Spatial Pattern for EEG based Brain Computer Interface with Adaptive Learning Classifier
In Brain Computer Interface (BCI), data generated from Electroencephalogram (EEG) is non-stationary with low signal to noise ratio and contaminated with artifacts. Common Spatial Pattern (CSP) algorithm has been proved t…
Brain Computer InterfaceClassificationEEGElectroencephalogram (EEG)+2Human and Smart Machine Co-Learning with Brain Computer Interface
Machine learning has become a very popular approach for cybernetics systems, and it has always been considered important research in the Computational Intelligence area. Nevertheless, when it comes to smart machines, it …
Brain Computer Interface