Papers Brain Computer Interface
“Brain Computer Interface” 태그가 달린 논문 466편 · 필터 해제
In the realm of hybrid Brain: Human Brain and AI
With the recent developments in neuroscience and engineering, it is now possible to record brain signals and decode them. Also, a growing number of stimulation methods have emerged to modulate and influence brain activit…
Brain Computer InterfaceOn The Effects Of Data Normalisation For Domain Adaptation On EEG Data
In the Machine Learning (ML) literature, a well-known problem is the Dataset Shift problem where, differently from the ML standard hypothesis, the data in the training and test sets can follow different probability distr…
Brain Computer InterfaceDomain AdaptationEEGElectroencephalogram (EEG)+1Cross Task Neural Architecture Search for EEG Signal Classifications
Electroencephalograms (EEGs) are brain dynamics measured outside the brain, which have been widely utilized in non-invasive brain-computer interface applications. Recently, various neural network approaches have been pro…
Brain Computer InterfaceEEGElectroencephalogram (EEG)Emotion Recognition+2Mental arithmetic task classification with convolutional neural network based on spectral-temporal features from EEG
In recent years, neuroscientists have been interested to the development of brain-computer interface (BCI) devices. Patients with motor disorders may benefit from BCIs as a means of communication and for the restoration …
Brain Computer InterfaceEEGElectroencephalogram (EEG)mental arithmetic taskA Novel Semi-supervised Meta Learning Method for Subject-transfer Brain-computer Interface
Brain-computer interface (BCI) provides a direct communication pathway between human brain and external devices. Before a new subject could use BCI, a calibration procedure is usually required. Because the inter- and int…
Brain Computer InterfaceEmotion RecognitionMeta-LearningSleep Staging+2Subject-independent trajectory prediction using pre-movement EEG during grasp and lift task
Brain-computer interface (BCI) systems can be utilized for kinematics decoding from scalp brain activation to control rehabilitation or power-augmenting devices. In this study, the hand kinematics decoding for grasp and …
Brain Computer InterfaceEEGElectroencephalogram (EEG)Trajectory PredictionTransfer Learning of an Ensemble of DNNs for SSVEP BCI Spellers without User-Specific Training
Objective: Steady-state visually evoked potentials (SSVEPs), measured with EEG (electroencephalogram), yield decent information transfer rates (ITR) in brain-computer interface (BCI) spellers. However, the current high p…
Brain Computer InterfaceEEGElectroencephalogram (EEG)SSVEP+1Classification of Electroencephalograms during Mathematical Calculations Using Deep Learning
Classifying Electroencephalogram(EEG) signals helps in understanding Brain-Computer Interface (BCI). EEG signals are vital in studying how the human mind functions. In this paper, we have used an Arithmetic Calculation d…
Brain Computer InterfaceClassificationDeep LearningEEG+1Zydeco-Style Spike Sorting Low Power VLSI Architecture for IoT BCI Implants
Brain Computer Interface (BCI) has great potential for solving many brain signal analysis limitations, mental disorder resolutions, and restoring missing limb functionality via neural-controlled implants. However, there …
Brain Computer InterfaceSpike SortingEEG-BBNet: a Hybrid Framework for Brain Biometric using Graph Connectivity
Brain biometrics based on electroencephalography (EEG) have been used increasingly for personal identification. Traditional machine learning techniques as well as modern day deep learning methods have been applied with p…
Brain Computer InterfaceEEGElectroencephalogram (EEG)ERPAn intertwined neural network model for EEG classification in brain-computer interfaces
The brain computer interface (BCI) is a nonstimulatory direct and occasionally bidirectional communication link between the brain and a computer or an external device. Classically, EEG-based BCI algorithms have relied on…
Brain Computer InterfaceDenoisingEEGElectroencephalogram (EEG)+1Physics-inform attention temporal convolutional network for EEG-based motor imagery classification
The brain-computer interface (BCI) is a cutting-edge technology that has the potential to change the world. Electroencephalogram (EEG) motor imagery (MI) signal has been used extensively in many BCI applications to assis…
Brain Computer InterfaceEEGEEG 4 classesEeg Decoding+2How does artificial intelligence contribute to iEEG research?
Artificial intelligence (AI) is a fast-growing field focused on modeling and machine implementation of various cognitive functions with an increasing number of applications in computer vision, text processing, robotics, …
Brain Computer InterfaceToward reliable signals decoding for electroencephalogram: A benchmark study to EEGNeX
This study examines the efficacy of various neural network (NN) models in interpreting mental constructs via electroencephalogram (EEG) signals. Through the assessment of 16 prevalent NN models and their variants across …
Brain Computer InterfaceEEGElectroencephalogram (EEG)Representation LearningFactorization Approach for Sparse Spatio-Temporal Brain-Computer Interface
Recently, advanced technologies have unlimited potential in solving various problems with a large amount of data. However, these technologies have yet to show competitive performance in brain-computer interfaces (BCIs) w…
Brain Computer InterfaceEEGElectroencephalogram (EEG)Motor ImageryEPOC Emotiv EEG Basics
This document provides some basic guidance to start working with the EPOC Emotiv neuroheadset device and describes how to use it to perform basic Brain-Computer Interface (BCI) research. A brief tutorial on how to set up…
Brain Computer InterfaceEEGElectroencephalogram (EEG)RhythmInteraction-Grounded Learning with Action-inclusive Feedback
Consider the problem setting of Interaction-Grounded Learning (IGL), in which a learner's goal is to optimally interact with the environment with no explicit reward to ground its policies. The agent observes a context ve…
Brain Computer InterfaceSPD domain-specific batch normalization to crack interpretable unsupervised domain adaptation in EEG
Electroencephalography (EEG) provides access to neuronal dynamics non-invasively with millisecond resolution, rendering it a viable method in neuroscience and healthcare. However, its utility is limited as current EEG te…
Brain Computer InterfaceDomain AdaptationEEGElectroencephalogram (EEG)+2A Neural-Inspired Architecture for EEG-Based Auditory Attention Detection
Humans have the ability to focus on one of the sound sources in a noisy scene, which is critical for everyday communication. Auditory attention detection (AAD) seeks to detect selective attention from one’s brain signals…
Brain Computer InterfaceData VisualizationEEGEEG Signal Classification+1Classification of EEG Motor Imagery Using Deep Learning for Brain-Computer Interface Systems
A trained T1 class Convolutional Neural Network (CNN) model will be used to examine its ability to successfully identify motor imagery when fed pre-processed electroencephalography (EEG) data. In theory, and if the model…
Brain Computer InterfaceEEGElectroencephalogram (EEG)Motor Imagery