<?xml version="1.0" encoding="ISO-8859-1"?><article xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink" xmlns:xsi="http://www.w3.org/2001/XMLSchema-instance">
<front>
<journal-meta>
<journal-id>0188-9532</journal-id>
<journal-title><![CDATA[Revista mexicana de ingeniería biomédica]]></journal-title>
<abbrev-journal-title><![CDATA[Rev. mex. ing. bioméd]]></abbrev-journal-title>
<issn>0188-9532</issn>
<publisher>
<publisher-name><![CDATA[Sociedad Mexicana de Ingeniería Biomédica]]></publisher-name>
</publisher>
</journal-meta>
<article-meta>
<article-id>S0188-95322024000100006</article-id>
<article-id pub-id-type="doi">10.17488/rmib.45.1.1</article-id>
<title-group>
<article-title xml:lang="en"><![CDATA[Preventive Detection of Driver Drowsiness from EEG Signals using Fuzzy Expert Systems]]></article-title>
<article-title xml:lang="es"><![CDATA[Detección Preventiva de la Somnolencia del Conductor a partir de Señales EEG Mediante Sistemas Expertos Difusos]]></article-title>
</title-group>
<contrib-group>
<contrib contrib-type="author">
<name>
<surname><![CDATA[Almirón]]></surname>
<given-names><![CDATA[Rony]]></given-names>
</name>
<xref ref-type="aff" rid="Aff"/>
</contrib>
<contrib contrib-type="author">
<name>
<surname><![CDATA[Castillo]]></surname>
<given-names><![CDATA[Bruno Adolfo]]></given-names>
</name>
<xref ref-type="aff" rid="Aff"/>
</contrib>
<contrib contrib-type="author">
<name>
<surname><![CDATA[Montoya Angulo]]></surname>
<given-names><![CDATA[Andrés]]></given-names>
</name>
<xref ref-type="aff" rid="Aff"/>
</contrib>
<contrib contrib-type="author">
<name>
<surname><![CDATA[Supo]]></surname>
<given-names><![CDATA[Elvis]]></given-names>
</name>
<xref ref-type="aff" rid="Aff"/>
</contrib>
<contrib contrib-type="author">
<name>
<surname><![CDATA[Talavera Suarez]]></surname>
<given-names><![CDATA[Jesús José Fortunato]]></given-names>
</name>
<xref ref-type="aff" rid="Aff"/>
</contrib>
<contrib contrib-type="author">
<name>
<surname><![CDATA[Yanyachi Aco Cardenas]]></surname>
<given-names><![CDATA[Daniel Domingo]]></given-names>
</name>
<xref ref-type="aff" rid="Aff"/>
</contrib>
</contrib-group>
<aff id="Af1">
<institution><![CDATA[,Universidad Nacional de San Agustín de Arequipa  ]]></institution>
<addr-line><![CDATA[ ]]></addr-line>
<country>Peru</country>
</aff>
<pub-date pub-type="pub">
<day>00</day>
<month>04</month>
<year>2024</year>
</pub-date>
<pub-date pub-type="epub">
<day>00</day>
<month>04</month>
<year>2024</year>
</pub-date>
<volume>45</volume>
<numero>1</numero>
<fpage>6</fpage>
<lpage>20</lpage>
<copyright-statement/>
<copyright-year/>
<self-uri xlink:href="http://www.scielo.org.mx/scielo.php?script=sci_arttext&amp;pid=S0188-95322024000100006&amp;lng=en&amp;nrm=iso"></self-uri><self-uri xlink:href="http://www.scielo.org.mx/scielo.php?script=sci_abstract&amp;pid=S0188-95322024000100006&amp;lng=en&amp;nrm=iso"></self-uri><self-uri xlink:href="http://www.scielo.org.mx/scielo.php?script=sci_pdf&amp;pid=S0188-95322024000100006&amp;lng=en&amp;nrm=iso"></self-uri><abstract abstract-type="short" xml:lang="en"><p><![CDATA[Abstract Currently, the percentage of traffic accidents has increased, and according to statistics, this percentage will continue to increase every year, so it is necessary to develop new technologies to prevent this kind of accidents. This paper presents a drowsiness detection system based on electroencephalogram (EEG) signals using a pair of channels (Fp1 and Fp2) applied to drivers before entering their vehicles. First, this model detects the relationship between the area under the curve (AUC) of alpha brain waves, an effective parameter for detecting drowsiness. Then, the extracted information is passed to a fuzzy expert system (FES) that classifies the subject's state as "alert" or "sleepy"; the criterion used was a threshold and training with subjective levels. The proposed system was compared with neural network models, such as support vector machine (SVM), K nearest neighbors (KNN), and random forest (RF). Measurements of one hundred and twenty minutes were performed on each of the ten drivers for two days to test the system. The tests confirm that this system is suitable for preventive measures and that the fuzzy system is superior to traditional neural network methods.]]></p></abstract>
<abstract abstract-type="short" xml:lang="es"><p><![CDATA[Resumen Actualmente, el porcentaje de accidentes de tráfico ha aumentado, y según las estadísticas, este porcentaje seguirá aumentando cada año, por lo que es necesario desarrollar nuevas tecnologías para prevenir este tipo de accidentes. Este trabajo presenta un sistema de detección de somnolencia basado en señales de electroencefalograma (EEG) utilizando un par de canales (Fp1 y Fp2) aplicado a los conductores antes de entrar en sus vehículos. En primer lugar, este modelo detecta la relación entre el área bajo la curva (AUC) de las ondas cerebrales alfa, un parámetro eficaz para detectar la somnolencia. A continuación, la información extraída se pasa a un sistema experto difuso (FES) que clasifica el estado del sujeto como "alerta" o "somnoliento"; el criterio utilizado fue un umbral y el entrenamiento con niveles subjetivos. El sistema propuesto se comparó con modelos de redes neuronales, como la máquina de vectores de soporte (SVM), K vecinos más cercanos (KNN) y el bosque aleatorio (RF). Se realizaron mediciones de ciento veinte minutos en cada uno de los diez conductores durante dos días para probar el sistema. Las pruebas confirman que este sistema es adecuado para las medidas preventivas y que el sistema difuso es superior a los métodos tradicionales de redes neuronales.]]></p></abstract>
<kwd-group>
<kwd lng="en"><![CDATA[drive drowsiness]]></kwd>
<kwd lng="en"><![CDATA[electroencephalogram]]></kwd>
<kwd lng="en"><![CDATA[expert systems]]></kwd>
<kwd lng="en"><![CDATA[sleepiness detection]]></kwd>
<kwd lng="es"><![CDATA[electroencefalograma]]></kwd>
<kwd lng="es"><![CDATA[detección de somnolencia]]></kwd>
<kwd lng="es"><![CDATA[sistemas expertos]]></kwd>
<kwd lng="es"><![CDATA[somnolencia en la conducción]]></kwd>
</kwd-group>
</article-meta>
</front><back>
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