Deep convolutional architecture-based hybrid learning for sleep arousal events detection through single-lead EEG signals.
Sign Up to like & getrecommendations! Published in 2023 at "Brain and behavior"
DOI: 10.1002/brb3.3028
Abstract: INTRODUCTION Detecting arousal events during sleep is a challenging, time-consuming, and costly process that requires neurology knowledge. Even though similar automated systems detect sleep stages exclusively, early detection of sleep events can assist in identifying… read more here.
Keywords: arousal events; single lead; detection; sleep arousal ... See more keywords
Classification of electroencephalogram records related to cursor movements with a hybrid method based on deep learning
Sign Up to like & getrecommendations! Published in 2021 at "International Journal of Imaging Systems and Technology"
DOI: 10.1002/ima.22643
Abstract: In brain computer interface (BCI), many transformation methods are used when processing electroencephalogram (EEG) signals. Thus, the EEG can be represented in different domains. However, designing an EEG‐based BCI system without any transformation technique is… read more here.
Keywords: cursor movements; classification; eeg signals; cursor ... See more keywords
A deep convolutional neural network model for automated identification of abnormal EEG signals
Sign Up to like & getrecommendations! Published in 2018 at "Neural Computing and Applications"
DOI: 10.1007/s00521-018-3889-z
Abstract: Electroencephalogram (EEG) is widely used to monitor the brain activities. The manual examination of these signals by experts is strenuous and time consuming. Hence, machine learning techniques can be used to improve the accuracy of… read more here.
Keywords: eeg signals; abnormal eeg; convolutional neural; model ... See more keywords
A hybrid classifier combination for home automation using EEG signals
Sign Up to like & getrecommendations! Published in 2020 at "Neural Computing and Applications"
DOI: 10.1007/s00521-020-04804-y
Abstract: Over the years, the usage of artificial intelligence (AI) algorithms is increased to develop various smart applications using Internet-of-Things. Home automation is a fast emerging area that involves monitoring and controlling of household appliances for… read more here.
Keywords: eeg signals; home automation; brain; model ... See more keywords
Criminal psychological emotion recognition based on deep learning and EEG signals
Sign Up to like & getrecommendations! Published in 2020 at "Neural Computing and Applications"
DOI: 10.1007/s00521-020-05024-0
Abstract: The difficulty of criminal psychological recognition is that it is difficult to classify emotions, and the accuracy of traditional recognition methods is insufficient. Therefore, it is necessary to improve the accuracy rate in combination with… read more here.
Keywords: emotion recognition; criminal psychological; eeg signals; deep learning ... See more keywords
Analysis of complex cognitive task and pattern recognition using distributed patterns of EEG signals with cognitive functions
Sign Up to like & getrecommendations! Published in 2020 at "Neural Computing and Applications"
DOI: 10.1007/s00521-020-05439-9
Abstract: The arrangement and functional distribution of an EEG signal structure related to higher cortical functions are being analyzed by both recent and substantial hypothesis experiments. This article provides a technique for analyzing the distribution pattern of EEG signals with… read more here.
Keywords: pattern recognition; signals cognitive; eeg signals; pattern ... See more keywords
A new NLEO based technique for the detection of burst–suppression patterns in multichannel neonatal EEG signals
Sign Up to like & getrecommendations! Published in 2017 at "Analog Integrated Circuits and Signal Processing"
DOI: 10.1007/s10470-017-0989-0
Abstract: In this paper, we propose a new method, based on the nonlinear energy operator (NLEO), to automatically detect burst–suppression (B–S) patterns in multichannel newborn electroencephalograms (EEGs). The proposed approach consists of two algorithms: (1) per-brain… read more here.
Keywords: suppression; detection; eeg signals; patterns multichannel ... See more keywords
Augmenting Global Coherence in EEG Signals with Binaural or Monaural Noises
Sign Up to like & getrecommendations! Published in 2020 at "Brain Topography"
DOI: 10.1007/s10548-020-00774-5
Abstract: Internal stochastic resonance (internal SR) is a phenomenon of non-linear systems in which the addition of a non-zero level of noise produces an enhancement in the coherence between two or more signals. In a previous… read more here.
Keywords: binaural monaural; coherence; eeg signals; global coherence ... See more keywords
Classification of EEG Signals for Epileptic Seizures Using Feature Dimension Reduction Algorithm based on LPP
Sign Up to like & getrecommendations! Published in 2020 at "Multimedia Tools and Applications"
DOI: 10.1007/s11042-020-09135-7
Abstract: Computer-aided diagnosis of epilepsy based on Electroencephalography (EEG) analysis is a beneficial practice which adopts machine learning to increase the recognition rate and saves physicians from long hours of EEG inspection. However multi-channel epilepsy EEG… read more here.
Keywords: eeg signals; algorithm; feature dimension; reduction algorithm ... See more keywords
Automatic seizure detection using a highly adaptive directional time–frequency distribution
Sign Up to like & getrecommendations! Published in 2018 at "Multidimensional Systems and Signal Processing"
DOI: 10.1007/s11045-017-0522-8
Abstract: Electroencephalogram (EEG) signals can be used by a proficient neurologist to detect the presence of seizure activity inside the brain. Automated detection of seizures in EEG signals has clinical importance given that manual round-the-clock monitoring… read more here.
Keywords: detection; eeg signals; time; time frequency ... See more keywords
Classification of EEG Signals Using Hybrid Feature Extraction and Ensemble Extreme Learning Machine
Sign Up to like & getrecommendations! Published in 2018 at "Neural Processing Letters"
DOI: 10.1007/s11063-018-9919-0
Abstract: Electroencephalogram (EEG) signals play an important role in clinical diagnosis and cognitive neuroscience. Automatic classification of EEG signals is gradually becoming the research focus, which contains two procedures: feature extraction and classification. In the phase… read more here.
Keywords: eeg signals; extraction; feature extraction; classification ... See more keywords