Papers Brain Computer Interface
“Brain Computer Interface” 태그가 달린 논문 466편 · 필터 해제
Autoregressive models for biomedical signal processing
Autoregressive models are ubiquitous tools for the analysis of time series in many domains such as computational neuroscience and biomedical engineering. In these domains, data is, for example, collected from measurement…
Brain Computer Interfaceparameter estimationTime SeriesEEG Cortical Source Feature based Hand Kinematics Decoding using Residual CNN-LSTM Neural Network
Motor kinematics decoding (MKD) using brain signal is essential to develop Brain-computer interface (BCI) system for rehabilitation or prosthesis devices. Surface electroencephalogram (EEG) signal has been widely utilize…
Brain Computer InterfaceEEGElectroencephalogram (EEG)Multimodal Brain-Computer Interface for In-Vehicle Driver Cognitive Load Measurement: Dataset and Baselines
Through this paper, we introduce a novel driver cognitive load assessment dataset, CL-Drive, which contains Electroencephalogram (EEG) signals along with other physiological signals such as Electrocardiography (ECG) and …
Brain Computer InterfaceEEGElectrocardiography (ECG)Electroencephalogram (EEG)Upper Limb Movement Execution Classification using Electroencephalography for Brain Computer Interface
An accurate classification of upper limb movements using electroencephalography (EEG) signals is gaining significant importance in recent years due to the prevalence of brain-computer interfaces. The upper limbs in the h…
Brain Computer InterfaceClassificationEEGOptimized EEG based mood detection with signal processing and deep neural networks for brain-computer interface
Electroencephalogram (EEG) is a very promising and widely implemented procedure to study brain signals and activities by amplifying and measuring the post-synaptical potential arising from electrical impulses produced by…
Brain Computer InterfaceDecision MakingEEGElectroencephalogram (EEG)LMDA-Net:A lightweight multi-dimensional attention network for general EEG-based brain-computer interface paradigms and interpretability
EEG-based recognition of activities and states involves the use of prior neuroscience knowledge to generate quantitative EEG features, which may limit BCI performance. Although neural network-based methods can effectivel…
Brain Computer InterfaceEEGMotor ImageryAn embedding for EEG signals learned using a triplet loss
Neurophysiological time series recordings like the electroencephalogram (EEG) or local field potentials are obtained from multiple sensors. They can be decoded by machine learning models in order to estimate the ongoing …
Brain Computer InterfaceEEGElectroencephalogram (EEG)Metric Learning+2Sliced-Wasserstein on Symmetric Positive Definite Matrices for M/EEG Signals
When dealing with electro or magnetoencephalography records, many supervised prediction tasks are solved by working with covariance matrices to summarize the signals. Learning with these matrices requires using Riemanian…
Brain Computer InterfaceComputational EfficiencyDomain AdaptationEEG+3Natural scene reconstruction from fMRI signals using generative latent diffusion
In neural decoding research, one of the most intriguing topics is the reconstruction of perceived natural images based on fMRI signals. Previous studies have succeeded in re-creating different aspects of the visuals, suc…
Brain Computer InterfaceBrain DecodingDescriptiveEEG Synthetic Data Generation Using Probabilistic Diffusion Models
Electroencephalography (EEG) plays a significant role in the Brain Computer Interface (BCI) domain, due to its non-invasive nature, low cost, and ease of use, making it a highly desirable option for widespread adoption b…
Brain Computer InterfaceData AugmentationDenoisingEEG+2Benchmarking framework for machine learning classification from fNIRS data
Background: While efforts to establish best practices with functional near infrared spectroscopy (fNIRS) signal processing have been published, there are still no community standards for applying machine learning to fNIR…
BenchmarkingBrain Computer InterfaceClassificationDeep comparisons of Neural Networks from the EEGNet family
Most of the Brain-Computer Interface (BCI) publications, which propose artificial neural networks for Motor Imagery (MI) Electroencephalography (EEG) signal classification, are presented using one of the BCI Competition …
Brain Computer InterfaceEEGEEG Signal ClassificationElectroencephalogram (EEG)+2Cross-Subject Deep Transfer Models for Evoked Potentials in Brain-Computer Interface
Brain Computer Interface (BCI) technologies have the potential to improve the lives of millions of people around the world, whether through assistive technologies or clinical diagnostic tools. Despite advancements in the…
BenchmarkingBrain Computer InterfaceDiagnosticEEG+2Neurorehab: An Interface for Rehabilitation
About 15% of the world population is affected by a disability in some form, amongst whom only 31% perform the recommended exercises without intervention. We are working on developing a motivating and effective way to enc…
Brain Computer InterfaceGame DesignUnityDevice JNEEG to convert Jetson Nano to brain-Computer interfaces. Short report
Artificial intelligence has made significant advances in recent years and this has had an impact on the field of neuroscience. As a result, different architectures have been implemented to extract features from EEG signa…
Brain Computer InterfaceEEGElectroencephalogram (EEG)Subject-Independent Classification of Brain Signals using Skip Connections
Untapped potential for new forms of human-to-human communication can be found in the active research field of studies on the decoding of brain signals of human speech. A brain-computer interface system can be implemented…
Brain Computer InterfaceClassificationDecoderSubject-Independent Brain-Computer Interfaces with Open-Set Subject Recognition
A brain-computer interface (BCI) can't be effectively used since electroencephalography (EEG) varies between and within subjects. BCI systems require calibration steps to adjust the model to subject-specific data. It is …
Brain Computer InterfaceEEGElectroencephalogram (EEG)Open Set LearningShort-length SSVEP data extension by a novel generative adversarial networks based framework
Steady-state visual evoked potentials (SSVEPs) based brain-computer interface (BCI) has received considerable attention due to its high information transfer rate (ITR) and available quantity of targets. However, the perf…
Brain Computer InterfaceEEGElectroencephalogram (EEG)SSVEPAMDET: Attention based Multiple Dimensions EEG Transformer for Emotion Recognition
Affective computing is an important branch of artificial intelligence, and with the rapid development of brain computer interface technology, emotion recognition based on EEG signals has received broad attention. It is s…
Brain Computer InterfaceEEGElectroencephalogram (EEG)Emotion RecognitionToward BCI-enabled Metaverse: A Joint Learning and Resource Allocation Approach
Toward user-driven Metaverse applications with fast wireless connectivity and tremendous computing demand through future 6G infrastructures, we propose a Brain-Computer Interface (BCI) enabled framework that paves the wa…
Brain Computer InterfaceDecision MakingDeep Reinforcement Learning