Papers Brain Computer Interface
“Brain Computer Interface” 태그가 달린 논문 466편 · 필터 해제
Comparison of Classification Algorithms Towards Subject-Specific and Subject-Independent BCI
Motor imagery brain computer interface designs are considered difficult due to limitations in subject-specific data collection and calibration, as well as demanding system adaptation requirements. Recently, subject-indep…
Brain Computer InterfaceClassificationGeneral ClassificationMotor ImageryImproving EEG Decoding via Clustering-based Multi-task Feature Learning
Accurate electroencephalogram (EEG) pattern decoding for specific mental tasks is one of the key steps for the development of brain-computer interface (BCI), which is quite challenging due to the considerably low signal-…
Brain Computer InterfaceClusteringEEGEeg Decoding+2Light-Weight 1-D Convolutional Neural Network Architecture for Mental Task Identification and Classification Based on Single-Channel EEG
Mental task identification and classification using single/limited channel(s) electroencephalogram (EEG) signals in real-time play an important role in the design of portable brain-computer interface (BCI) and neurofeedb…
Brain Computer InterfaceClassificationEEGElectroencephalogram (EEG)+2An algorithm for onset detection of linguistic segments in continuous electroencephalogram signals
A Brain Computer Interface based on imagined words can decode the word a subject is thinking on through brain signals to control an external device. In order to build a fully asynchronous Brain Computer Interface based o…
Brain Computer InterfaceOnset DetectionT-WaveNet: Tree-Structured Wavelet Neural Network for Sensor-Based Time Series Analysis
Sensor-based time series analysis is an essential task for applications such as activity recognition and brain-computer interface. Recently, features extracted with deep neural networks (DNNs) are shown to be more effect…
Activity RecognitionBrain Computer InterfaceGesture RecognitionIntent Detection+3Cross-Correlation Based Discriminant Criterion for Channel Selection in Motor Imagery BCI Systems
Objective. Many electroencephalogram (EEG)-based brain-computer interface (BCI) systems use a large amount of channels for higher performance, which is time-consuming to set up and inconvenient for practical applications…
Brain Computer Interfacechannel selectionEEGElectroencephalogram (EEG)+1Deep Learning in EEG: Advance of the Last Ten-Year Critical Period
Deep learning has achieved excellent performance in a wide range of domains, especially in speech recognition and computer vision. Relatively less work has been done for EEG, but there is still significant progress attai…
Brain Computer InterfaceDeep LearningEEGElectroencephalogram (EEG)+3Interval-valued aggregation functions based on moderate deviations applied to Motor-Imagery-Based Brain Computer Interface
In this work we study the use of moderate deviation functions to measure similarity and dissimilarity among a set of given interval-valued data. To do so, we introduce the notion of interval-valued moderate deviation fun…
Brain Computer InterfaceDecision MakingEEG 4 classesEEG Left/Right hand+1A Deep Neural Network for SSVEP-based Brain-Computer Interfaces
Objective: Target identification in brain-computer interface (BCI) spellers refers to the electroencephalogram (EEG) classification for predicting the target character that the subject intends to spell. When the visual s…
Brain Computer InterfaceEEGElectroencephalogram (EEG)Multi-class Classification+1Deep learning-based classification of fine hand movements from low frequency EEG
The classification of different fine hand movements from EEG signals represents a relevant research challenge, e.g., in brain-computer interface applications for motor rehabilitation. Here, we analyzed two different data…
Brain Computer InterfaceEEGElectroencephalogram (EEG)General ClassificationLearning Patterns in Imaginary Vowels for an Intelligent Brain Computer Interface (BCI) Design
Technology advancements made it easy to measure non-invasive and high-quality electroencephalograph (EEG) signals from human's brain. Hence, development of robust and high-performance AI algorithms becomes crucial to pro…
Brain Computer InterfaceEEGElectroencephalogram (EEG)Motor Imagery+1Binarization Methods for Motor-Imagery Brain-Computer Interface Classification
Successful motor-imagery brain-computer interface (MI-BCI) algorithms either extract a large number of handcrafted features and train a classifier, or combine feature extraction and classification within deep convolution…
BinarizationBrain Computer InterfaceClassificationGeneral Classification+1Electroencephalography signal processing based on textural features for monitoring the driver's state by a Brain-Computer Interface
In this study we investigate a textural processing method of electroencephalography (EEG) signal as an indicator to estimate the driver's vigilance in a hypothetical Brain-Computer Interface (BCI) system. The novelty of …
Brain Computer InterfaceEEGElectroencephalogram (EEG)The "Sound of Silence" in EEG -- Cognitive voice activity detection
Speech cognition bears potential application as a brain computer interface that can improve the quality of life for the otherwise communication impaired people. While speech and resting state EEG are popularly studied, h…
Action DetectionActivity DetectionBrain Computer InterfaceEEG+4Transfer Learning and SpecAugment applied to SSVEP Based BCI Classification
Objective: We used deep convolutional neural networks (DCNNs) to classify electroencephalography (EEG) signals in a steady-state visually evoked potentials (SSVEP) based single-channel brain-computer interface (BCI), whi…
Brain Computer InterfaceClassificationData AugmentationEEG+6Analysis of artifacts in EEG signals for building BCIs
Brain-Computer Interface (BCI) is an essential mechanism that interprets the human brain signal. It provides an assistive technology that enables persons with motor disabilities to communicate with the world and also emp…
Brain Computer InterfaceDynamic Time WarpingEEGElectroencephalogram (EEG)+1To Root Artificial Intelligence Deeply in Basic Science for a New Generation of AI
One of the ambitions of artificial intelligence is to root artificial intelligence deeply in basic science while developing brain-inspired artificial intelligence platforms that will promote new scientific discoveries. T…
Brain Computer InterfaceDecision MakingVisual Commonsense ReasoningA 6.3-Nanowatt-per-Channel 96-Channel Neural Spike Processor for a Movement-Intention-Decoding Brain-Computer-Interface Implant
This paper presents microwatt end-to-end neural signal processing hardware for deployment-stage real-time upper-limb movement intent decoding. This module features intercellular spike detection, sorting, and decoding ope…
Brain Computer InterfaceState EstimationA Computationally Efficient Multiclass Time-Frequency Common Spatial Pattern Analysis on EEG Motor Imagery
Common spatial pattern (CSP) is a popular feature extraction method for electroencephalogram (EEG) motor imagery (MI). This study modifies the conventional CSP algorithm to improve the multi-class MI classification accur…
Brain Computer InterfaceClassificationEEGElectroencephalogram (EEG)+2Revisiting the Application of Feature Selection Methods to Speech Imagery BCI Datasets
Brain-computer interface (BCI) aims to establish and improve human and computer interactions. There has been an increasing interest in designing new hardware devices to facilitate the collection of brain signals through …
Brain Computer InterfaceEEGElectroencephalogram (EEG)feature selection