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https://doi.org/10.37815/rt= e.v33n2.847

Artículos originales=

 

Implementación del algoritmo K-means para clusterización de señales EEG durante la aplicac= ión de una prueba Stroop

Implementation of the K-means algorithm for clustering of EEG signals during the application of a Stroop test

 

Paúl Esteban Cár= denas1 https://orcid.org/0000-0003-0526-8519,

Daniela Prado= 1  = https://orcid.org/0000-0003-1241-1782, Bruno Iglesias2, Ronnie Urdiales<= sup>2, Marcos Orellana2 https://orcid.org/0000-0002-3671-9362, Priscila Cedillo1, 2 https://orcid.org/0000-0002-6787-0655

 

1Universidad de Cuenca, Cuenca, Ecuador

paul.cardenasd@ucuenca.edu.ec, daniela.pradoc@ucuenca.edu.ec, priscila.cedillo@ucuenca.edu.ec

 

2Universidad del Azuay, Cuenca, Ecuador

biglesias@es.uazuay.edu.ecrurdiales@es.uazuay.edu.ecmarore@uazuay.edu.ecicedillo@uazuay.edu.ec

 

Enviado:         2021/07/11

Aceptado:       2021/09/28

Publicado:      2021/11/30                         

Resumen

3D"Cuadro

El análisis de datos y la generación de modelos mediante aprendizaje automático (Machine Learning - ML por sus siglas en inglés) es una de las técnicas más usadas por la comunidad científica para obtener conocimiento que no se puede interpretar o analizar= a simple vista. En este documento, se aborda el tema específico de la obtenci= ón, procesamiento de datos y modelado de los mismos mediante técnicas de agrupamiento, concretamente el algoritmo de K-Means. Los datos se obtienen a través de una herramienta de interfaz cerebro computador (ICC - BCI por sus siglas en inglés) basada en electroencefalograma (EEG), mientras un sujeto = de prueba realiza una tarea de tipo Stroop, la cual permite a los psicólogos evaluar el control inhibitorio de las personas. La aplicación de este tipo = de algritmo en esta área especifica, busca poder identificar patrones dentro de las señales EEG relacionados al estado del sujeto. La captura de datos se ejecuta en tres diferentes horas del día, matutina , vespertina, y nocturna, durante dos días consecutivos, con el objetivo de obtener variabilidad en l= os datos. Si bien la muestra de datos es pequeña, se puede emplear como punto = de partida para el análisis del algoritmo K-means en señales EEG durante la ejecución del test Stroop. Los resultados se muestran tanto desde el punto = de vista técnico como psicológico, y se puede ver en la clusterización realiza= da con las señales en el dominio del tiempo, que existe un patrón de agrupación según la hora del día en la que se realiza el test. Por otra parte, para la clusterización con las señales en el dominio de la frecuencia este patrón no resulta tan evidente. Desde la perspectiva psicológica, se comprueba que el proceso de aprendizaje y de acomodación al momento de realizar una prueba psicológica, disminuye su potencial.

 

Palabras clave: aprendizaje automático, electroencefalograma, agrupamiento, evaluación neuropsicológica, Interfaz Cerebro-Computador.

Abstract

Data analysis and model generation through machine learning (Machine Learning - ML) is one of the techniques most used by the scientific communi= ty to obtain knowledge that cannot be interpreted or analyzed with the naked e= ye. In this document, the specific issue of data collection, processing, and modeling is addressed using clustering techniques, specifically the K-Means algorithm. The data were obtained through a brain-computer interface (ICC - BCI) tool based on electroencephalogram (EEG), while a test subject perform= ed a Stroop-type task, which allows psychologists to evaluate people's inhibitory control. The application of this type of algorithm in this specific area se= eks to identify patterns within the EEG signals related to the subject's state. Data capture was carried out at three different hours of the day, morning, evening, and night, for two consecutive days, to obtain variability in the data. Although the data sample is small, it will serve as a starting point = for analyzing the K-means algorithm in EEG signals during the execution of the Stroop test. The results obtained are shown both from a technical and psychological point of view. It can be seen in the clustering carried out w= ith the signals in the time domain that there is a clustering pattern according= to the time of day in which the test was performed. On the other hand, this pattern is not so evident for the clustering with the signals in the freque= ncy domain. From the psychological perspective, it is found that the learning a= nd accommodation process at the time of performing a psychological test decrea= ses its potential.<= o:p>

 

Keywords: machine learning, electroencephalographic, groping, neuropsychological assessment, Brain-Computer Interface.

 

Introducción<= /span>

Desde h= ace algunos años, la Inteligencia Artificial (IA) es una de las de las herramie= ntas más importantes para el desarrollo de nuevas tecnologías que ayudan a las personas en tareas que, por dificultades físicas o psicológicas endógenas, tienen limitaciones al momento de ejecutarlas (Rusell &am= p; Norvig, 2004). Debido a la proliferación de la IA= , esta se ha divido en varios subcampos para cubrir la diversidad de aplicaciones = que presenta (Ayodele, 2= 010). Entre ellos está el desarrollo de técnicas de Machine Learning (M= L), que es uno de los campos más importantes hoy en día, ya que, se basa en la aplicación de técnicas estadísticas como regresiones lineales, regresiones logísticas, entre otras, para la creación de algoritmos mucho más eficiente= s, con el objetivo de identificar patrones o realizar predicciones sobre un conjunto de datos.

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En este= contexto, se encuentran dos clasificaciones: algoritmos supervisados y no supervisado= s. En ambos casos, los algoritmos reciben variables de entrada o inputs y pueden o no contener variables de salida.  Es decir, la diferencia radica en la presencia o no de variables de salida o target.  Precisamente en el caso de los algoritmos supervisados se requiere indicar este tipo de variable; mientras= que en los algoritmos no supervisados, no se requiere de esta variable. Por lo = que este tipo de algoritmos buscan encontrar patrones que permitan entender el comportamiento de los datos de mejor manera, ya sea con el fin de agruparlo= s, clasificarlos o realizar predicciones basadas en datos (Nasteski, = 2017).

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El Machine Learning se utiliza en un = amplio espectro de contextos (Carvalho et al., 2019), uno de los más relevantes es la aplicación en el área de la salud, y particularmente en el cuidado del paci= ente (Ben-Israel et al., 2020). Además, en este subcampo de la I= A se emplea para el análisis de información del comportamiento humano que sirve = como insumo para el desarrollo de modelos de entrenamiento cognitivo (Rennie et al., 2020). Es decir, el Machine Learning brinda soporte para el análisis de datos provenientes de la intervención neuropsicológica para el entrenamiento personalizado de las funciones cognitivas (Shani et&n= bsp;al., 2019), entendidas como los procesos men= tales involucrados en la adquisición de conocimiento, la manipulación de la información y el razonamiento (Montañés &= amp; de Brigard, 2011).

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Existen= distintas funciones cognitivas como la atención, concentración, memoria, lenguaje, razonamiento, control inhibitorio, entre otras (Trápaga et al., 2018). Particularmente, el control inhi= bitorio es la capacidad que tiene un individuo para controlar reacciones impulsivas tanto a nivel atencional como comportamental, esta función depende principalmente de la corteza pre frontal (Peña Barri= entos et al., 2017). A pesar de que Scarpina y = Tagini (2017) realizan varias pruebas neuropsic= ológicas para analizar esta función a lo largo de los años, la técnica predilecta por expertos, y que se mantiene desde el siglo pasado, es el test de Stroop (Stroop, 19= 35).

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Este te= st atencional tiene la finalidad de tasar la capacidad de un sujeto para resis= tir la interferencia de tipo verbal. Se trata de una herramienta de tamizaje út= il para el diagnóstico de patologías del desarrollo y adquiridas (por ejemplo: Trastorno del espectro autista, traumatismos craneoencefálicos, síndrome de Tourette, trastorno por déficit de atención con hiperactividad). También se emplea para identificar la capacidad del sujeto de dar una respuesta impide= ndo la intrusión de un proceso automático, donde la atención selectiva juega un papel fundamental, ya que el ser humano requiere controlar su capacidad atencional orientado a los estímulos de mayor beneficio y adaptación. Esta tarea consiste en indicar el color de la tinta con la que está escrita una palabra que no coincide con su significado, por ejemplo, si se escribe la palabra “azul” aunque está impresa con color verde, produce una interferenc= ia semántica en el cerebro que causa un aumento en la latencia de respuesta ve= rbal del sujeto de prueba.

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A parti= r de la explicación dada, el objetivo de esta investigación se centra en implementa= r un modelo que agrupe las señales del Electroencefalograma (EEG)  que se obtienen durante la ejecución de = la prueba Stroop de un sujeto de ensayo con el fin de identificar patrones o características del estado actual del individuo durante la aplicación del t= est Stroop. Para ello, se plantea el empleo de un algortimo muy utilizado dentro del Machine Learnig denominado = K-Means (Likas et&n= bsp;al., 2003), el cual es un algoritmo de apren= dizaje no supervisado que pertenece al tipo de algoritmo de agrupamiento o también conocido como Clustering (Alqurashi = & Wang, 2019). Está claro que dentro del Machine Learning existen modelos o algoritmos más potentes que se pueden utilizar en este contexto, sin embarg= o, se considera este algoritmo debido a que es relativamente de fácil implemen= tación y como una primera aproxiación al objetivo de identificar patrones pertenecientes al control inhibitorio en señales EEG.

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Este tr= abajo se organiza de la siguiente manera: la Sección 2 presenta estudios relacionado= s, la Sección 3 introduce la solución y método planteado, la Sección IV explic= a el caso de estudio donde se aplica la solución y el resultado del análisis de datos, finalmente se expone la conclusión.

Trabajos relacionados

Las for= mas de evaluación, diagnóstico y predicción del estado de las funciones cognitivas= , y particularmente, del control inhibitorio, avanzan de manera acelerada los últimos años (Emerenziani et al., 2020). Gracias a esto, se combinan meto= dologías de investigación de las ciencias sociales, médicas y técnicas; con el objet= ivo de que los estudios sean más exactos y que varias disciplinas se puedan jun= tar en una misma investigación para comprender, de mejor manera, los fenómenos humanos (Onwuegbuzie & Leech, 2005).

<= o:p> 

Priya et al. (2020) realizan un estudio en el que se predicen variables de condiciones psicológ= icas como la depresión, ansiedad y estrés mediante Machine Learning, específicamente árboles de decisión y algorit= mos de Naive-Bayes. Ellos no con= sideran dividir los factores psicológicos en niveles o tipos, solo detectan la presencia o ausencia de alteraciones cognitivas. Se puede resaltar que los autores consideran que, en algunas ocasiones, los algoritmos de Machine Learning dan falsos positi= vos o falsos negativos, frente a ello, desarrollan un algoritmo que detecta estos falsos resultados, haciendo que el sistema sea mucho más eficiente.

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Por otr= a parte, Wang et al.= (2018) desarrollan un sistema para detec= tar la ansiedad usando un casco para la interacción cerebro computador (Brain Computer Interface – BCI) me= diante la captura de señales EEG del sujeto. Medina et a= l. (2018) por su parte, depuran las señales= con técnicas específicas y; posteriormente, desarrollan un sistema que detecta = el nivel de atención del sujeto evaluado y predice cuantitativamente, por los diferentes picos que existen en determinadas señales, si el sujeto evaluado tiene ansiedad; esto resulta útil porque hay muy poco margen de error utilizando estas técnicas.

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Kaneta = et al. (2016) investigan la influencia del efecto Stroop en la onda cerebral, especificamente en los potenciales relacionados con eventos (ERP – por sus siglas en ingles). Para = ello hace uso de la técnica de agrupamiento máquina de vector de soporte (Sup= er Vector Machine – SVM), mediante la= cual clasifican a las señales en dos grupos dependiendo si el estímulo es dirigi= do o no dirigido.

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How et = al. (2015), realizan la implementación de un clasificador mediante SVM, con el objetivo= de clasificar los niveles de estrés en las señales EEG. Para esto utilizan la prueba Stroop como factor estresante. En sus resultados logran clasificar en cuatro niveles de estrés con una precisión media del 67,06%, en 3 niveles c= on una precisión del 75,22% y en dos niveles de estrés con una precisión del 85,71%.

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Lim et = al. (2016) analizan el estrés cognitivo en las señales EGG de 25 sujetos durante la aplicación del test Stroop. Para esto aplican tres técnicas o algori= mos como son: redes neuronales artificiales, k-Nearest Neighbor (KNN – por sus siglas en ingles) and Line= ar Discriminant Analysis (LDA –= por sus siglas en ingles). Sus resultados les permiten identificar que el algoritmo que tiene mejor rendimiento es el KNN.

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Como se= puede observar, se realizan diferentes estudios respecto al análisis de señales E= EG durante la aplicación del test Stroop, con el objetivo de clasificar o predecir distintas condiciones psicológicas (Al-Nafjan et al., 2017; Priya et al., 2020). Sin embargo, muchos de ellos se = centran en la detección del estrés, siendo necesario investigar otros estados menta= les o funciones cognitivas que puedan producirse durante la aplicación de este = test (Popov et&n= bsp;al., 2018). A partir de una indagación previ= a, se comprueba que no existe una investigación centrada en el análisis de señales EEG para determinar patrones del estado mental que se puedan relacionar al control inhibitorio. Por lo tanto, este artículo se centra en la implementación del algoritmo K-Means, con el objet= ivo de que, mediante la toma de datos de las señales EEG de un sujeto durante la ejecución del test Stroop, se genere un agrupamiento que permita identificar patrones en las señales EEG capturadas en las diferentes fases del test y sesiones realizadas, para contrastarlos con los datos tomados de forma manu= al.

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Metodología

La meto= dología de investigación seleccionada para la presentación de esta investigación es la propuesta por Runeson et&= nbsp;al. (2012) que implica la delimitación de es= tudios de caso. Acontinuación se explican los pasos empleados: A) diseño y planificación, B) consideraciones éticas, C) preparación y recolección de datos, D) análisis e interpretación de los resultados, E) amenazas a la validez. Sin embargo, en este apartado a fines metodológicos se presentan l= os pasos A, B y C; dejando los pasos D y E para la sección 4. Resultados.=

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Diseño y planificación

El objetivo central de este estudio se presenta por medio de la propuesta Goal Question Metric (GQM) de Basili et al. (1994), ya que se pu= ede plasmar de manera objetiva lo que se requiere evaluar por medio del estudio= de caso (Tabla 1).<= /span>

 

Tabla 1<= /span><= o:p>

Aproximación GQM

Analizar:

Patrones de las señales EEG relacionados al estado actual del suj= eto durante la ejecución de la tarea tipo Stroop verbal.

Con el propósito:

Generar clusteres de los patrones encontrados que proporcionen información de utilidad clínica.

Con respecto a:

Sujeto de estudio adulto sin precedentes de neuropatología.<= /o:p>

Desde el punto de vista de:

Técnico.

 

Consideraciones éticas

Aunque un estudio de investigación se basa principalmente en la confianza entre el investigador y el caso (Amschler & Pradhan, 2001), se consideran algunas medidas de prevención explícitas para evitar problemas futuros relacionados con el bienestar del participante. Los principales factores éticos incluidos en este estudio de caso son: i) consentimiento informado, ii) revisión de la aprobación de un comité de bioética, iii) confidencialidad y iv) retroalimentación de los hallazgos.

 

Preparación y recolección de datos

A continuación, se explica la metodología que orienta = a la obtención de datos para esta investigación, esta corresponde a la propuesta= de Fayyad et al. (1996) titulada Knowledge Discovery in Databases  (KDD), = que se refiere al proceso general de descubrimiento de conocimiento útil a partir de datos. Para garantizar que = se deriven conclusiones útiles, se efectúan pasos como la preparación, selecci= ón y limpieza de datos. Además de la incorporación de conocimientos apropiados y= la interpretación adecuada de los resultados de la minería.

 

En la Figura 1 se presentan los pasos para la recolección de los datos, clusterización a partir de la aplicación del algoritmo K-Means y análisis de los clusters encontrados. La ventaja de utilizar este procesamiento, radica en que este es un proceso no trivial de identificar patrones válidos, novedosos, potencialmente útiles y, en última instancia, comprensibles en los datos.

Los dat= os obtenidos del EEG, con los que se entrenó el modelo, se recolectaron mediante un casco BCI basado en EEG. La captura de los datos se la realizó en un solo sujeto mientras ejecutaba la prueba Stroop, en seis sesiones diferentes distribuid= as en dos días, en tres períodos de tiempo: al despertar, al medio día y antes= de dormir.

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Figura 1=

Pasos que constituyen el proceso KDD (Fayyad et al., 1996)3D"Diagrama,

 

Específ= icamente, se utilizó un casco OpenBCI, con una placa “Cyton Boisensing Board + Daisy”= , la cual permitió trabajar con 16 canales con electrodos secos no invasivos.  Para visualizar los datos en tiempo rea= l se utilizó la interfaz de usuario del software de OpenBCI (Suryotriso= ngko & Samopa, 2015), la cual permitió guardar el arch= ivo de datos tanto en formato de texto (.txt), como en formato europeo de 24 bits (.bdf). Para este estudio, se utilizó el archivo de texto, ya que permitió analizar de forma simple las señales para los objetivos planteados. El arch= ivo se organizó en columnas que corresponden a cada uno de los canales utilizad= os para el registro, en este caso se emplearon 6 canales posicionados en la corteza prefrontal, correspondientes a los electrodos 0, 1, 8, 9, 10, y 11, como se puede observar en la Figura 2. Esta selección de canales se la = realizó, ya que es en la corteza prefrontal donde se podría evidenciar con mayor cla= ridad la actividad de control inhibitorio en las señales cerebrales del sujeto. P= or otra parte, en las filas se mostraron los potenciales en micro voltios de c= ada uno de los registros realizados en el tiempo, con una frecuencia de 125 Hz.=

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Figura <= /span>2<= /span>

Distribución de los electrodos en= la corteza cerebral

3D"Diagrama

Descripción

 

Para el preprocesamiento de los datos se emplearon diferentes librerías en Python, = como Pandas, Numpy, Plotly, entre otras. Para utilizar el conjunto de datos en un modelo como K-Means, primero fue necesario realizar un análisis y preprocesamiento, lo cual permitió desarro= llar una correcta extracción de características de las señales capturadas. Para = este estudio se analizaron las señales EEG tanto en el domino del tiempo como de= la frecuencia, lo que dio paso a la identificación de la existencia de frecuen= cias no deseadas. El análisis se lo efectuó para cada uno de los canales de las = seis sesiones realizadas. En la Figura 3 se puede observar un ejemplo de l= as señales captada por los seis canales durante la sesión de la tarde del día = uno en el dominio del tiempo.

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Figura <= /span>3<= /span>

Señales en el dominio del tiempo = de la sesión de la tarde en el día 1

3D"Gráfico,

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Para re= alizar el análisis en el dominio de la frecuencia, se utilizó la transformada rápida = de Fourirer (FFT). La Figura 4 muestra un ejemplo de las señales= captada por los seis canales durante la sesión de la tarde del día uno en el domini= o de la frecuencia.

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Figura <= /span>4<= /span>

Señales EEG en el dominio de la frecuencia

  3D"Interfaz

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Después= del análisis realizado, como se puede ver en la Figura 4, el espectro de frecue= ncia de las señales presentó un pico en frecuencias bajas cercanas a cero Hz, po= r lo que se aplicó un filtro paso banda entre tres y 40 Hz. Como se puede ver en= la Figura 5, con el fin de eliminar frecuenci= as atípicas que puedan representar ruido o niveles muy bajos de frecuencia correspondientes a ondas delta, y a su vez tener una mejor visualización de= las bandas Theta, Alpha y Beta, ya que estas son las que contendrían mayor información respecto al control inhibitorio en la actividad cerebral del sujeto.

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Figura <= /span>5<= /span>

Filtro pasa banda aplicado a las = señales EEG

3D"Imagen

 

En la <= /span>Figura 6, se puede ver un ejemplo de tres = de las señales capturadas por los seis canales durante la sesión de la tarde del d= ía uno, filtradas en el dominio de la frecuencia y del tiempo. Además, se han atenuado las frecuencias cercanas a cero y se puede visibilizar de mejor ma= nera las frecuencias superiores correspondientes a las bandas de interés antes mencionadas.

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Figura <= /span>6<= /span>

Señales filtradas en el dominio d= e la frecuencia y tiempo

3D"Interfaz

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Para la implementación del algoritmo K-Mean= s, se realizaron dos pruebas en el primer caso, con los datos en el dominio del tiempo, y en el segundo caso en el dominio de la frecuencia. En el primer c= aso, las características o columnas a utilizar, fueron de cada uno de los canales seleccionados previamente, y las filas cada una de las muestras tomadas a lo largo del tiempo, concatenando una sesión tras otra en orden cronológico. P= or otra parte, en el segundo caso, para reducir la dimensionalidad de los dato= s en el dominio de la frecuenta, se realizó el cálculo y selección de características principales de las señales EEG capturadas. Entre las principales características utilizadas en este caso, se hizo uso de la dens= idad espectral de potencia, densidad relativa de potencia, potencia máxima de frecuencia, potencia mínima de frecuencia, media de la potencia de la frecuencia, y finalmente la varianza y covarianza de la frecuencia.

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Una vez= que se obtuvo la tabla final con los datos procesados, tanto en el dominio del tie= mpo como de la frecuencia, se realizó la normalización de los valores numéricos, para así obtener un mejor desempeño del algoritmo. En este estudio, debido = a la poca cantidad de datos recabados, se utilizó el 100% del set de datos para realizar el agrupamiento mediante el algoritmo de K-Means.

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Como ya= se mencionó anteriormente, este estudio se enfoca en las bandas Theta, Alpha, y Beta, en la Figura 7 se puede observar un ejemplo de c= ómo están distribuidas las bandas de frecuencias en las señales ya filtradas qu= e se utilizaron para entrenar el algoritmo. De igual manera en la Figura 8 se puede ver la densidad espectra= l de potencia de uno de los canales de las señales EEG a ser utilizadas para entrenar el algoritmo, en azul se puede ver las frecuencias a ser utilizadas para este estudio.

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Figura <= /span>7<= /span>

Bandas de frecuencias de las seña= les a ser utilizadas para entrenar el algoritmo

3D"Gráfico,

 

 

Figura <= /span>8<= /span>

Densidad espectral de potencia de= las señales a ser utilizadas para entrenar el algoritmo

3D"Gráfico,

 

Resultados

A continuación, se presenta la implementació= n del algortimo K-means y el análisis= de datos agrupados y las amenazas a la validez.

 

Análisis de datos agrupados

Para ejecutar el algoritmo de Clustering, se utilizó la librería Sklearn de Python, = por razones de afinidad tanto con la librería como con el lenguaje de programac= ión.

 

El algoritmo, en un primer caso, recibió como entrada el conjunto de datos de EEG, en este proceso, los seis canales en el dominio del tiempo seleccionados y preprocesados previamente. Como métrica = de evaluación y para una elección adecuada del número de clústeres a realizar,= se utilizó el método del codo, el cual empleó la distancia media de las observaciones a su centroide, es decir, se fijó en las distancias intra clú= ster, si el número de clústeres se incrementa, la varianza intra clúster tiende a disminuir. De acuerdo a la gráfica obtenida, como se puede ver en la Figura 9, se tiene que el número ideal de clústeres está entre dos a seis, para el ca= so de las señales en el dominio del tiempo.

 

Figura 9=

Gráfica de codo para los datos en el dominio del tiempo

3D"Interfaz

 

Después de varias pruebas y análisis realiza= das con diferentes valores de k clúster= es, se estableció que tres es el número adecuado de clústeres. En la Figura 10 se puede ver el resultado de la clusterización realizada en tres dimensiones.<= o:p>

 

Figura 10

Clusterización con las señales en el dominio del tiempo

3D"Gráfico,

En un segundo caso, el algoritmo recibió como entrada los datos de las características principales extraídas de los canal= es en el dominio de la frecuencia seleccionados y preprocesados, mencionados previamente. Al igual que en el primer caso, como métrica de evaluación y p= ara una elección adecuada del número de clústeres a realizar, se utilizó el mét= odo del codo. De acuerdo a la gráfica obtenida, como se puede ver en la = Figura 11, se tiene que el número ideal de clústeres está entre dos y ocho, para el ca= so de las señales en el dominio de la frecuencia. Esto sucede posiblemente, po= rque en este dominio, se pudo detectar algunas señales en el espectro de frecuen= cia atípicas con respecto al resto de canales, principalmente de la sesión del = día uno en la mañana. Otro aspecto importante a mencionar, es que el número de muestras disminuyó a 36, ya que al realizar el proceso de extracción de características en el domino de la frecuencia, cada una de las señales capturadas correspondían a una muestra, mientras que las características extraídas a las dimensiones. Esto por un lado ayudó a que el rendimiento del algoritmo sea mejor, sin embargo, la poca cantidad de muestras utilizadas pueden representar un inconveniente para la obtención de resultados adecuad= os.

 

Figura 11

Gráfica de codo para los datos en el dominio de la frecuencia

3D"Interfaz =

Después de varias pruebas y análisis realiza= dos con diferentes valores de k clústeres, se estableció que el número ideal de clústeres, con el fin de identificar similitudes con la clusterización realizada en el dominio del tiempo. En la Figura 12 se puede ver el resultado de la clusterización elaborada en tres dimensiones.<= o:p>

 

Figura 12

Clusterización con las señales en el dominio de la frecuencia

3D"Gráfico,

 

Los resultados obtenidos después de la clusterización se analizaron con base en tres características: i) hora del día (mañana, tarde, noche), ii) día (día = 1, día 2), e ii) interferencia (baja, media, alta), esta última establecida se= gún los valores calculados más adelante, obtenidos en la realización del test. =

&n= bsp;

De acue= rdo a esto en la Tabla 2 se puede observar la distribució= n de los resultados obtenidos mediante los dos casos de clusterización.

&n= bsp;

De acuerdo con estos resultados, se puede evidenciar que en la clusterización, en el dominio del tiempo, existe una m= ejor distribución de los datos, ya que se logra distinguir claramente que el algoritmo ha separado a las señales casi de manera perfecta según la hora d= el día en la que fueron capturadas, sin embargo, en relación tanto al día como= a la interferencia, no se encontraron mayores diferencias entre clústeres ya = que los porcentajes se distribuyen de manera similar.<= /p>

 

En cuanto a la segunda clusterización, en el dominio de la frecuencia, los resultados no evidencian una clara distribuci= ón de los datos, ya que los porcentajes son similares para cada clúster, esto puede deberse a la poca cantidad de datos obtenidos, se puede probar a futu= ro el algoritmo en este dominio con un espectro mucho más amplio de datos y con más sujetos de estudio.

 

Tabla 2= =

Porcentaje de la distribución de los datos en los clústeres realizados a base de tres características analizadas

CLUSTERIZACIÓN EN EL DOMINIO DEL TIEMPO<= /span>

CLUSTERIZACIÓN EN EL DOMINIO DE LA FRECUENCIA<= /o:p>

JORNADA

CLUSTER 1 (ROJO)

CLUSTER 2 (VERDE= )

CLUSTER 3 (CELESTE)

JORNADA

CLUSTER 1 (ROJO)

CLUSTER 2 (VERDE)

CLUSTER 3 (CELESTE)

Matutina

34,56%

0,00%=

0,00%=

Matutina

27,78%

2,78%

2,78%

Vespertina<= /o:p>

17,72%

14,73%

0,00%

Vespertina

27,78%

0,00%

5,56%

Nocturna

0,00%

0,00%

32,98%

Nocturna

27,78%

0,00%

5,56%<= /span>

 

DÍA

CLUSTER 1 (ROJO)

CLUSTER 2 (VERDE= )

CLUSTER 3 (CELESTE)

DÍA

CLUSTER 1 (ROJO)<= /span>

CLUSTER 2 (VERDE)<= /span>

CLUSTER 3 (CELESTE)<= /span>

1

37,03%

0,00%=

18,45%

1

41,67%

2,78%

5,56%

2

15,25%

14,73%

14,54%

2

41,67%

0,00%

8,33%

 

INTERF.

CLUSTER 1 (ROJO)

CLUSTER 2 (VERDE= )<= /span>

CLUSTER 3 (CELESTE)

INTERF.

CLUSTER 1 (ROJO)

CLUSTER 2 (VERDE)

CLUSTER 3 (CELESTE)

Baja<= /span>

0,00%

0,00%

18,45%

Baja

41,67%

0,00%

8,33%

Media=

327,03%<= /span>

0,00%=

0,00%=

Media

27,78%

2,78%

2,78%

Alta<= /span>

15,25%

14,73%

14,54%

Alta

13,89%

0,00%

2,78%

 

 

La Figura 13, presenta los resultados calculados según indica el manual de = Scarpina & Tagini (2017), para ello, se emplearon los cálculos presentados en la Figura 14 (Stroop, 1935).

 

Como se ve en la Figura 14, existen cinco variables que se utilizan para obtener el valor de la interferencia, las cuales son:

 

·      = P: número de palabras leídas en la= primera fase del test

·      = C: número de colores leídos en la = segunda fase del test

·      = PC: número de palabras leídas en la tercera fase del test

·      = PC’: Valor obtenido de la división= entre la suma y el producto del valor de P con el valor de C

·      = Interferencia: Valor obtenido de la diferencia entre el valor de PC y el valor de PC’<= /p>

 

Figura 13

Comparación de la interferencia en los días de prueba

3D"Gráfico,

 

Figura 14

Fórmula utilizada para el cálculo = de la interferencia al realizar la prueba de tipo Stroop <= /span>(Stroop, 1992)

3D"Tabla

Descripción

 

 

Amenazas a la validez

En los proce= sos de evaluación empírica existen cuatro tipos de amenazas a la validez que son necesarios señalar: i) validez de constructo, ii) validez interna, iii) val= idez externa y iv) fiabilidad.

 

Validez de constructo

La validez de constructo se centra en la relación entre la teoría detrás del c= aso de estudio y la observación. Para ello, se considera dos propuestas teóricas validadas. Por un lado el efecto Stroop= (1935) que ha sido validado estadísticamente = por la comunidad científica para la valoración del estado de control inhibitorio y atención selectiva; además, el algoritmo k-means que ha sido demostrado matemáticamente (Xin, = 2011).

 

Validez i= nterna

La validez interna está relacionada con la calidad de los resultados del estud= io. En este estudio de caso, se cuent con información limitada para el análisis; por lo tanto, el resultado de la clusterización puede presentar imprecision= es; por ello, se procede a calificar los resultados de manera manual, para contrastar la información.

 

Validez e= xterna

La validez e= xterna esta realacionada a la capacidad de generalización de los resultados obteni= dos. En este estudio de caso, al contar con información de un solo sujeto, los resultados son poco generalizables. Sin embargo, el estudio sirve como una primera aproximación a la aplicación de tecnología a este tipo de herramien= tas psicológicas, puntualmente algoritmos de machine learnig que permitan identificar patrones en los datos, y sirvan como soporte a la intervención = de un psicólogo o personal de la salud.

 

Fiabilida= d

El test Stro= op tiene un alfa de Cronbach de 0.76, lo que significa que es un instrumento confiab= le para medir el control inhibitorio (Eliorraga, 2017).

 <= /o:p>

Discusión

En este apartado, se presenta la discusión d= esde dos puntos de vista; por un lado, el técnico que visibiliza las fortalezas y limitaciones del estudio; por otro, desde el punto de vista psicológico y l= as implicaciones clínicas que este tipo de herramientas tienen para los profesionales de la salud cognitiva.

 

Desde el punto de vista técnico

La aplicación de algoritmos de agrupamiento a señales EEG ayudan a identificar patrones y relaciones con ciertas tareas q= ue realiza un sujeto, como en este caso el teste Stroop. En este sentido el algoritmo implementado permite ver ciertas relaciones en los clústeres obtenidos con las señales en el dominio del tiempo, respecto a los horarios= de realización del test, de esta forma se puede plantear la interrogante si el horario en el que un sujeto realiza el test influye en sus resultados o en = el comportamiento de sus ondas de la corteza prefrontal. Por otro lado, la clusterización con las señales en el dominio de la frecuencia no muestra resultados del todo óptimos, esto debido a la poca cantidad de datos recaba= dos para analizar las señales en este dominio, que por lo general permite obten= er mayor información de las señales que en el dominio del tiempo.

 

Sin embargo, el obtener los modelos en este estudio, es un aporte a las pruebas del procesamiento de las señales y la extracción de características de las mismas, así como identificar los facto= res que influyen en la existencia de un modelo de clusterización en este dominio.  Esto a su vez, da la paut= a para una siguiente investigación, en la que se prueben los modelos planteados co= n un conjunto de datos mucho más robusto.

 

Desde el punto de vista psicológico

Los resultados que se derivan de la aplicaci= ón del test Stroop a un sujeto sin patología severa, denota que, en el segundo día, tal y como lo muestra la Figura 13, presenta un proceso de adecuación y aprendizaje de la tarea, por lo que, el efecto Stroop pierde fuerza. Además, se puede observar cómo la interferenci= a es mayor en el horario nocturno, es decir, se comprueba que la capacidad del s= er humano para inhibir o controlar las respuestas impulsivas (o automáticas), y generar respuestas mediadas por la atención y el razonamiento se reduce cua= ndo existen factores como el cansancio o fatiga de la jornada laboral (MacLeod, 1991; Stroop, 1935). Todo esto, se comprueba con los hallazgos representados en la Tabla 1, particularmente en la clusterización. En el dominio del tiempo brinda información relevante, se observa que, el clúster uno concentra datos que corresponden a la jornada matutina, mientras que en el tres está la vespert= ina.

 

Tomando en cuenta que la media de interferen= cia para personas de entre 16 a 44 años oscila desde 30 a -30 ADDIN CSL_CITATION {"citationItems":[{"id":"ITEM-1","itemDa= ta":{"DOI":"10.1037/h0054651","ISSN":&qu= ot;00221015","abstract":"In this study pairs of conflicting stimuli, both being inherent aspects of the same symbols, were presented simultaneously (a name of one color printed in= the ink of another color--a word stimulus and a color stimulus). The difference= in time for reading the words printed in colors and the same words printed in black is the measure of the interference of color stimuli upon reading word= s. The difference in the time for naming the colors in which the words are pri= nted and the same colors printed in squares (or swastikas) is the measure of the interference of conflicting word stimuli upon naming colors. The interferen= ce of conflicting color stimuli upon the time for reading 100 words (each word naming a color unlike the ink-color of its print) caused an increase of 2.3 seconds or 5.6% over the normal time for reading the same words printed in black. This increase is not reliable. But the interference of conflicting w= ord stimuli upon the time for naming 100 colors (each color being the print of a word which names another color) caused an increase of 47.0 seconds or 74.3%= of the normal time for naming colors printed in squares. Tests on the permanen= cy of the interference of conflicting word stimuli are also described. (PsycIN= FO Database Record (c) 2006 APA, all rights reserved). © 1935 American Psychological Association.","author":[{"dropping-particle":"= ;","family":"Stroop","given":"J. R.","non-dropping-particle":"","parse-names&q= uot;:false,"suffix":""}],"container-title":&q= uot;Journal of Experimental Psychology","id":"ITEM-1","issue":"= 6","issued":{"date-parts":[["1935","= ;12"]]},"page":"643-662","title":"S= tudies of interference in serial verbal reactions","type":"article-journal","volume&q= uot;:"18"},"uris":["http://www.mendeley.com/docume= nts/?uuid=3D4832c7f2-ad9e-33a9-848d-04c6086727e7"]}],"mendeley&qu= ot;:{"formattedCitation":"(Stroop, 1935)","plainTextFormattedCitation":"(Stroop, 1935)","previouslyFormattedCitation":"(Stroop, 1935)"},"properties":{"noteIndex":0},"schema&= quot;:"https://github.com/citation-style-language/schema/raw/master/cs= l-citation.json"}(Stroop, 1935), en el día uno, el sujeto supera la media en interferencia, y que en el día = dos se encuentra justo a la mitad. Cabe recalcar que, mientras menos interferen= cia presente el sujeto al realizar la prueba de Stroop la función cognitiva del control inhibitorio está conservada.

 

Conclusiones

En este= trabajo se describe un proceso para implementar un agrupador de señales EEG, el mis= mo que fue realizado mediante Python usando el algoritmo de K-Means de la librer= ía sklearn.

&n= bsp;

Lo resu= ltados se analizan desde la perspectiva tanto técnica como psicológica y se pueden identificar ciertos patrones en los clusters con respecto a los resultados psicológicos. Los mejores resultados se obtienen con las señales en el domi= nio del tiempo, sin embargo, para lograr un mejor rendimiento del algoritmo con= los datos en el dominio de la frecuencia, es necesario utilizar un mayor número= de datos, ya que como se conoce, en este dominio se pueden extraer mayor canti= dad de características de las señales, las mismas que pueden servir para detect= ar patrones no identificables con un set de datos pequeño.

&n= bsp;

Desde la perspectiva psicológica, se puede comprobar que el proceso de aprendizaje y= de acomodación al momento de realizar una prueba psicológica, disminuye su potencial.

&n= bsp;

Finalmente, para trabajos futuros se plantea repetir= el proceso con una cantidad de datos mayor, que permita por un lado validar lo realizado y, a su vez, mejorar los resultados obtenidos. De igual manera, es importante entrenar el algoritmo con datos de varios sujetos de diferentes edades, para nutrirlo con una mayor diversidad de muestras y señales, mejor= ando el rango del de análisis y su capacidad de obtención de conocimientos.=

 

Agradecimientos

Los aut= ores desean agradecer al Vicerrectorado de Investigaciones de la Universidad del Azuay por el apoyo financiero y académico, así como al personal de la escue= la de Ingeniería de Sistemas y Telemática, y el Laboratorio de Investigación y Desarrollo en Informática (LIDI). Además, este trabajo es parte de los sigu= ientes proyectos de investigación: “Fog Computing aplicado a monitoreo de disposit= ivos usados en ambientes de vida asistidos (Ambient Assisted Living). Caso de estudio: plataforma para el adulto mayor”; y “Diseño de arquitecturas y mod= elos de interacción para ambientes de vida asistida orientados a adultos mayores. Caso de estudio: ambientes lúdicos y sociales”. Ganadores de las convocator= ias DIUC XVII y XVIII respectivamente, por ello los autores agradecen a la Dirección de Investigación de la Universidad de Cuenca (DIUC) por su apoyo.=

 

 

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=  

 

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