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   Articles with "eeg signal" as a keyword



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EEG signal classification using LSTM and improved neural network algorithms

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recommendations! Published in 2020 at "Soft Computing"

DOI: 10.1007/s00500-019-04515-0

Abstract: Neural network (NN) finds role in variety of applications due to combined effect of feature extraction and classification availability in deep learning algorithms. In this paper, we have chosen SVM, logistic regression machine learning algorithms… read more here.

Keywords: classification; eeg signal; performance; neural network ... See more keywords
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Huffman quantization approach for optimized EEG signal compression with transformation technique

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recommendations! Published in 2020 at "Soft Computing"

DOI: 10.1007/s00500-020-04804-z

Abstract: The significance of the electroencephalography (EEG) signal is used to read the brain activity in the form of electrical patterns. EEG signals help to diagnose anomalies in the brain at the time of head injuries,… read more here.

Keywords: compression; eeg signal; huffman quantization; discrete cosine ... See more keywords
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Diagnosis of Delusion and Hallucination from Schizophrenia Patient Using RADWT

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recommendations! Published in 2019 at "Journal of Medical Systems"

DOI: 10.1007/s10916-019-1354-x

Abstract: In this paper, early detection of schizophrenia types such as hallucination and delusion propose through the high Q-factor of RADWT in EEG signal acquired during the cognitive task of the patient. The earlier diagnose obtains… read more here.

Keywords: schizophrenia; delusion; diagnosis; eeg signal ... See more keywords
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IDEA: Intellect database for emotion analysis using EEG signal

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recommendations! Published in 2020 at "Journal of King Saud University - Computer and Information Sciences"

DOI: 10.1016/j.jksuci.2020.10.007

Abstract: Abstract Emotion recognition using Electroencephalography (EEG) is a convenient and reliable technique. EEG based emotion detection study can find its application in various fields such as defense, aerospace, medical, and many more. This analysis helps… read more here.

Keywords: analysis; eeg signal; idea intellect; database ... See more keywords
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A novel end-to-end 1D-ResCNN model to remove artifact from EEG signals

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recommendations! Published in 2020 at "Neurocomputing"

DOI: 10.1016/j.neucom.2020.04.029

Abstract: Abstract Electroencephalography (EEG) signals are an important tool in the field of clinical medicine, brain research and the study of neurological diseases. EEG is very susceptible to a variety of physiological signals, which brings great… read more here.

Keywords: eeg signals; eeg signal; model; end end ... See more keywords
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EEG signal processing with separable convolutional neural network for automatic scoring of sleeping stage

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recommendations! Published in 2020 at "Neurocomputing"

DOI: 10.1016/j.neucom.2020.05.085

Abstract: Abstract Nowadays, among the Deep Learning works, there is a tendency to develop networks with millions of trainable parameters. However, this tendency has two main drawbacks: overfitting and resource consumption due to the low-quality features… read more here.

Keywords: network; scoring sleeping; eeg signal; signal processing ... See more keywords
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Time-domain exponential energy for epileptic EEG signal classification

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recommendations! Published in 2019 at "Neuroscience Letters"

DOI: 10.1016/j.neulet.2018.10.062

Abstract: Automatic classification and prediction of epileptic electroencephalogram (EEG) signal are of great concern to the research community due to its non-stationary and non-linear properties. Features with minimal computation cost are highly needed for the rapid… read more here.

Keywords: classification; energy; eeg signal; time domain ... See more keywords
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Using temporal EEG signal decomposition to identify specific neurophysiological correlates of distractor-response bindings proposed by the theory of event coding

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recommendations! Published in 2020 at "NeuroImage"

DOI: 10.1016/j.neuroimage.2020.116524

Abstract: The ability to cope with distracting information is a major requirement for goal-directed behavior. It is particularly challenged when distracting information is either potentially relevant or temporally close to goal-direct responses, resulting in so-called distractor-response… read more here.

Keywords: response bindings; distractor response; response; eeg signal ... See more keywords
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Effective automated method for detection and suppression of muscle artefacts from single-channel EEG signal

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recommendations! Published in 2020 at "Healthcare Technology Letters"

DOI: 10.1049/htl.2019.0053

Abstract: This Letter proposes an automated method for the detection and suppression of muscle artefacts (MAs) in the single-channel electroencephalogram (EEG) signal based on variational mode decomposition (VMD) and zero crossings count threshold criterion without the… read more here.

Keywords: detection; eeg signal; automated method; detection suppression ... See more keywords
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Design and implementation of cost-effective IIR filter for EEG signal on FPGA

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recommendations! Published in 2020 at "Australian Journal of Electrical and Electronics Engineering"

DOI: 10.1080/1448837x.2020.1771662

Abstract: ABSTRACT Filter is unfathomably used to identify diverse human flag in genuine time. In this paper, a digital IIR filter is proposed for the fast detection of EEG signal to smooth and compress the signal.… read more here.

Keywords: design implementation; eeg signal; fpga; iir filter ... See more keywords
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A novel channel selection scheme for olfactory EEG signal classification on Riemannian manifolds

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recommendations! Published in 2022 at "Journal of Neural Engineering"

DOI: 10.1088/1741-2552/ac7b4a

Abstract: Objective. The classification of olfactory-induced electroencephalogram (olfactory EEG) signals has potential applications in disease diagnosis, emotion regulation, multimedia, and so on. To achieve high-precision classification, numerous EEG channels are usually used, but this also brings… read more here.

Keywords: classification; eeg signal; olfactory eeg; channel selection ... See more keywords