Papers Brain Computer Interface
“Brain Computer Interface” 태그가 달린 논문 466편 · 필터 해제
Toward asynchronous EEG-based BCI: Detecting imagined words segments in continuous EEG signals
An asynchronous Brain--Computer Interface (BCI) based on imagined speech is a tool that allows to control an external device or to emit a message at the moment the user desires to by decoding EEG signals of imagined spee…
Brain Computer InterfaceEEGElectroencephalogram (EEG)Source Aware Deep Learning Framework for Hand Kinematic Reconstruction using EEG Signal
The ability to reconstruct the kinematic parameters of hand movement using non-invasive electroencephalography (EEG) is essential for strength and endurance augmentation using exosuit/exoskeleton. For system development,…
Brain Computer Interfacechannel selectionEEGElectroencephalogram (EEG)+1Classification of Motor Imagery EEG Signals by Using a Divergence Based Convolutional Neural Network
Deep neural networks (DNNs) are observed to be successful in pattern classification. However, high classification performances of DNNs are related to their large training sets. Unfortunately, in the literature, the datas…
Brain Computer InterfaceClassificationEEGElectroencephalogram (EEG)+2A New Method for Features Normalization in Motor Imagery Few-Shot Learning using Resting-State
Brain-computer interface (BCI) systems are usually designed specifically for each subject based on motor imagery. Therefore, the usability of these networks has become a significant challenge. The network has to be desig…
Brain Computer InterfaceClassificationFew-Shot LearningGeneral Classification+1FBCNet: A Multi-view Convolutional Neural Network for Brain-Computer Interface
Lack of adequate training samples and noisy high-dimensional features are key challenges faced by Motor Imagery (MI) decoding algorithms for electroencephalogram (EEG) based Brain-Computer Interface (BCI). To address the…
Binary ClassificationBrain Computer InterfaceClassificationEEG+3Inter-subject Deep Transfer Learning for Motor Imagery EEG Decoding
Convolutional neural networks (CNNs) have become a powerful technique to decode EEG and have become the benchmark for motor imagery EEG Brain-Computer-Interface (BCI) decoding. However, it is still challenging to train C…
Brain Computer InterfaceDecoderEEGEeg Decoding+3EmoWrite: A Sentiment Analysis-Based Thought to Text Conversion -- A Validation Study
Objective- The objective of this study is to introduce EmoWrite, a novel brain-computer interface (BCI) system aimed at addressing the limitations of existing BCI-based systems. Specifically, the objective includes impro…
Brain Computer InterfaceSentiment AnalysisDecoding Event-related Potential from Ear-EEG Signals based on Ensemble Convolutional Neural Networks in Ambulatory Environment
Recently, practical brain-computer interface is actively carried out, especially, in an ambulatory environment. However, the electroencephalography (EEG) signals are distorted by movement artifacts and electromyography s…
Brain Computer InterfaceEEGElectroencephalogram (EEG)Real Time Vigilance Detection using Frontal EEG
Vigilance of an operator is compromised in performing many monotonous activities like workshop and manufacturing floor tasks, driving, night shift workers, flying, and in general any activity which requires keen attentio…
Brain Computer InterfaceEEGElectroencephalogram (EEG)Common Spatial Generative Adversarial Networks based EEG Data Augmentation for Cross-Subject Brain-Computer Interface
The cross-subject application of EEG-based brain-computer interface (BCI) has always been limited by large individual difference and complex characteristics that are difficult to perceive. Therefore, it takes a long time…
Brain Computer InterfaceData AugmentationEEGElectroencephalogram (EEG)+3FOIT: Fast Online Instance Transfer for Improved EEG Emotion Recognition
The Electroencephalogram (EEG)-based emotion recognition is promising yet limited by the requirement of a large number of training data. Collecting substantial labeled samples in the training trails is the key to the gen…
Active LearningBrain Computer InterfaceDomain AdaptationEEG+5BENDR: using transformers and a contrastive self-supervised learning task to learn from massive amounts of EEG data
Deep neural networks (DNNs) used for brain-computer-interface (BCI) classification are commonly expected to learn general features when trained across a variety of contexts, such that these features could be fine-tuned t…
Automatic Speech RecognitionAutomatic Speech Recognition (ASR)Brain Computer InterfaceEEG+6EEG-Inception: An Accurate and Robust End-to-End Neural Network for EEG-based Motor Imagery Classification
Classification of EEG-based motor imagery (MI) is a crucial non-invasive application in brain-computer interface (BCI) research. This paper proposes a novel convolutional neural network (CNN) architecture for accurate an…
Brain Computer InterfaceClassificationData AugmentationEEG+5Online LDA based brain-computer interface system to aid disabled people
This paper aims to develop brain-computer interface system based on electroencephalography that can aid disabled people in daily life. The system relies on one of the most effective event-related potential wave, P300, wh…
Brain Computer InterfaceEEGElectroencephalogram (EEG)Classification of fNIRS Data Under Uncertainty: A Bayesian Neural Network Approach
Functional Near-Infrared Spectroscopy (fNIRS) is a non-invasive form of Brain-Computer Interface (BCI). It is used for the imaging of brain hemodynamics and has gained popularity due to the certain pros it poses over oth…
Binary ClassificationBrain Computer InterfaceClassificationGeneral Classification+1Motor-Imagery-Based Brain Computer Interface using Signal Derivation and Aggregation Functions
Brain Computer Interface technologies are popular methods of communication between the human brain and external devices. One of the most popular approaches to BCI is Motor Imagery. In BCI applications, the ElectroEncepha…
Brain Computer InterfaceDecision MakingEEGEEG 4 classes+34D Attention-based Neural Network for EEG Emotion Recognition
Electroencephalograph (EEG) emotion recognition is a significant task in the brain-computer interface field. Although many deep learning methods are proposed recently, it is still challenging to make full use of the info…
Brain Computer InterfaceEEGEEG Emotion RecognitionElectroencephalogram (EEG)+1Real or Virtual? Using Brain Activity Patterns to differentiate Attended Targets during Augmented Reality Scenarios
Augmented Reality is the fusion of virtual components and our real surroundings. The simultaneous visibility of generated and natural objects often requires users to direct their selective attention to a specific target …
Brain Computer InterfaceEEGElectroencephalogram (EEG)In-Ear SpO2 for Classification of Cognitive Workload
Classification of cognitive workload promises immense benefit in diverse areas ranging from driver safety to augmenting human capability through closed loop brain computer interface. The brain is the most metabolically a…
Brain Computer InterfaceClassificationGeneral ClassificationTowards Real-World BCI: CCSPNet, A Compact Subject-Independent Motor Imagery Framework
A conventional brain-computer interface (BCI) requires a complete data gathering, training, and calibration phase for each user before it can be used. In recent years, a number of subject-independent (SI) BCIs have been …
Brain Computer InterfaceEdge-computingEEGElectroencephalogram (EEG)+1