Luis Alfredo Moctezuma
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Explore the profile of Luis Alfredo Moctezuma including associated specialties, affiliations and a list of published articles.
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7
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48
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Recent Articles
1.
Moctezuma L, Suzuki Y, Furuki J, Molinas M, Abe T
Annu Int Conf IEEE Eng Med Biol Soc
. 2025 Mar;
2024:1-4.
PMID: 40039811
We present a method that uses a convolutional neural network (CNN) called EEGNeX to extract and classify the characteristics of sleep-related waveforms from electroencephalographic (EEG) signals in different stages of...
2.
Moctezuma L, Molinas M, Abe T
Biomed Res Int
. 2025 Feb;
2025:3585125.
PMID: 39963589
Research suggests that dreams play a role in the regulation of emotional processing and memory consolidation; electroencephalography (EEG) is useful for studying them, but manual annotation is time-consuming and prone...
3.
Moctezuma L, Suzuki Y, Furuki J, Molinas M, Abe T
Sci Rep
. 2024 Aug;
14(1):17952.
PMID: 39095608
We present a new approach to classifying the sleep stage that incorporates a computationally inexpensive method based on permutations for channel selection and takes advantage of deep learning power, specifically...
4.
Soler A, Moctezuma L, Giraldo E, Molinas M
Sci Rep
. 2022 Jul;
12(1):11221.
PMID: 35780173
High-density Electroencephalography (HD-EEG) has proven to be the EEG montage that estimates the neural activity inside the brain with highest accuracy. Multiple studies have reported the effect of electrode number...
5.
Moctezuma L, Abe T, Molinas M
Sci Rep
. 2022 Mar;
12(1):3523.
PMID: 35241745
In this study we explore how different levels of emotional intensity (Arousal) and pleasantness (Valence) are reflected in electroencephalographic (EEG) signals. We performed the experiments on EEG data of 32...
6.
Moctezuma L, Molinas M
Sci Rep
. 2020 Sep;
10(1):14917.
PMID: 32913275
We present a new approach for a biometric system based on electroencephalographic (EEG) signals of resting-state, that can identify a subject and reject intruders with a minimal subset of EEG...
7.
Moctezuma L, Molinas M
Front Neurosci
. 2020 Jul;
14:593.
PMID: 32625054
We present a multi-objective optimization method for electroencephalographic (EEG) channel selection based on the non-dominated sorting genetic algorithm (NSGA) for epileptic-seizure classification. We tested the method on EEG data of...
8.
Classification of low-density EEG for epileptic seizures by energy and fractal features based on EMD
Moctezuma L, Molinas M
J Biomed Res
. 2020 Jun;
34(3):180-190.
PMID: 32561698
We are here to present a new method for the classification of epileptic seizures from electroencephalogram (EEG) signals. It consists of applying empirical mode decomposition (EMD) to extract the most...
9.
Moctezuma L, Molinas M
Sci Rep
. 2020 Apr;
10(1):5850.
PMID: 32246122
We present a four-objective optimization method for optimal electroencephalographic (EEG) channel selection to provide access to subjects with permission in a system by detecting intruders and identifying the subject. Each...