A review on machine learning for EEG signal processing in bioengineering
MP Hosseini, A Hosseini, K Ahi - IEEE reviews in biomedical …, 2020 - ieeexplore.ieee.org
Electroencephalography (EEG) has been a staple method for identifying certain health
conditions in patients since its discovery. Due to the many different types of classifiers …
conditions in patients since its discovery. Due to the many different types of classifiers …
Patient specific seizure prediction system using Hilbert spectrum and Bayesian networks classifiers
N Ozdemir, E Yildirim - Computational and mathematical …, 2014 - Wiley Online Library
The aim of this paper is to develop an automated system for epileptic seizure prediction from
intracranial EEG signals based on Hilbert‐Huang transform (HHT) and Bayesian classifiers …
intracranial EEG signals based on Hilbert‐Huang transform (HHT) and Bayesian classifiers …
On the classification of EEG signal by using an SVM based algorithm
In clinical practice, study of brain functions is fundamental to notice several diseases
potentially dangerous for the health of the subject. Electroencephalography (EEG) can be …
potentially dangerous for the health of the subject. Electroencephalography (EEG) can be …
EEG Signal Classification Using Manifold Learning and Matrix‐Variate Gaussian Model
L Zhu, Q Hu, J Yang, J Zhang, P Xu… - Computational …, 2021 - Wiley Online Library
In brain‐computer interface (BCI), feature extraction is the key to the accuracy of recognition.
There is important local structural information in the EEG signals, which is effective for …
There is important local structural information in the EEG signals, which is effective for …
An implementation of independent component analysis for 3D statistical shape analysis
An implementation of the independent component analysis (ICA) technique for three-
dimensional (3D) statistical shape analysis is presented. The capabilities of the ICA …
dimensional (3D) statistical shape analysis is presented. The capabilities of the ICA …
The Efficacy and Utility of Lower-Dimensional Riemannian Geometry for EEG-Based Emotion Classification
Electroencephalography (EEG) signals have diverse applications in brain-computer
interfaces (BCIs), neurological condition diagnoses, and emotion recognition across …
interfaces (BCIs), neurological condition diagnoses, and emotion recognition across …
Effect of two adjacent muscles of flexor and extensor on finger pinch and Hand grip force
Hand grip force and motion pattern classification using bio signal such as Electromyogram
(EMG) has been very important in current studies. EMG based pattern classification has gain …
(EMG) has been very important in current studies. EMG based pattern classification has gain …
Weighted complex network based framework for epilepsy detection from EEG signals
This chapter presents a weighted complex network based framework to identify one of the
most challenging brain disorders, namely epilepsy. The pattern of action potentials …
most challenging brain disorders, namely epilepsy. The pattern of action potentials …
Exploración de patrones de conectividad de fase fuente/sumidero para el estudio del procesamiento del lenguaje a través de la causalidad inter-canal en señales de …
I Rodríguez Rodríguez - 2023 - repositorio.upct.es
[SPA] Esta investigación titulada" Exploración de Patrones de Conectividad de Fase
Fuente/Sumidero para el Estudio del Procesamiento del Lenguaje a través de la …
Fuente/Sumidero para el Estudio del Procesamiento del Lenguaje a través de la …
An improved WaveCluster algorithm based on ICA
X Li, M Luo - 2009 5th International Conference on Wireless …, 2009 - ieeexplore.ieee.org
In this paper, we introduce an improved WaveCluster algorithm, which can deal with the
high-dimensional data issue (IWCA algorithm for short). The algorithm combines the …
high-dimensional data issue (IWCA algorithm for short). The algorithm combines the …