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Subliminal Perception01:15

Subliminal Perception

Subliminal perception refers to the processing of sensory information that occurs below the level of conscious awareness. Researchers study subliminal perception by presenting a stimulus, such as a word or image, very quickly, typically around 50 milliseconds. This rapid presentation is often followed by another stimulus, such as a pattern of dots or lines, which blocks further mental processing of the initial stimulus. As a result, if participants cannot identify the initial stimulus better...
Understanding Consciousness01:23

Understanding Consciousness

Consciousness can be defined as the state of being aware of and able to think about one's existence, sensations, and surroundings. It encompasses two major components: awareness and arousal. Awareness pertains to the recognition of environmental stimuli and internal states. At the same time, arousal refers to the physiological readiness to engage with these stimuli, which varies significantly between states like sleep and wakefulness.
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The concept of subconscious awareness refers to the processing of information below the level of conscious thought, which significantly influences both behaviors and decisions. It is also known as waking subconscious awareness. This complex level of cognition operates without the direct awareness of the individual, facilitating rapid and simultaneous handling of multiple information streams.
An illustrative example of subconscious processing is its role in problem-solving. Often, individuals...
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Lucid dreaming is a unique state of consciousness where an individual realizes they are dreaming while still in the dream. This awareness allows them to manipulate their dream environment consciously. Researchers like Stephen LaBerge have significantly contributed to the understanding of lucid dreams, highlighting that during these dreams, certain areas of the brain, such as the prefrontal cortex, that involve self-awareness and thought evaluation show increased activity.
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Predicción de Transiciones de Estado de Conciencia bajo Sedación Mediante EEG Utilizando un Marco de Aprendizaje

Ji-Hoon Jeong, Minji Lee, Chaewon Lee

    IEEE journal of biomedical and health informatics
    |December 12, 2025
    PubMed
    Resumen
    Este resumen es generado por máquina.

    Este estudio presenta Deep-ConTrans, un novedoso modelo de aprendizaje profundo para clasificar los estados de conciencia durante la anestesia utilizando señales de electroencefalograma (EEG). Identifica con precisión las transiciones, mejorando la monitorización de la alerta intraoperatoria más allá de las evaluaciones binarias.

    Palabras clave:
    electroencefalogramaanestesiaaprendizaje profundoestado de concienciasedaciónmonitorización intraoperatoriaseguridad del paciente

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    Área de la Ciencia:

    • Anestesiología; Neurociencia; Inteligencia Artificial

    Sus antecedentes:

    • La alerta intraoperatoria es una preocupación crítica debido a las limitaciones en la monitorización actual de la profundidad de la anestesia.
    • Las clasificaciones binarias tradicionales (consciente/inconsciente) no logran capturar las transiciones dinámicas durante la inducción y el despertar de la anestesia.
    • Las transiciones anestésicas varían significativamente entre individuos y agentes.

    Objetivo del estudio:

    • Clasificar tres estados de conciencia distintos: conciencia, transiciones e falta de respuesta.
    • Analizar estos estados durante la sedación con propofol y midazolam utilizando señales de electroencefalograma (EEG).
    • Desarrollar y validar un novedoso marco de aprendizaje profundo para una clasificación robusta.

    Principales métodos:

    • Se desarrolló un novedoso marco de aprendizaje profundo, Deep-ConTrans.
    • El marco incorpora filtrado de patrón espacial común (CSP), extracción de características multidominio, fusión basada en atención y entrenamiento adversario de dominio.
    • Las transiciones se identificaron utilizando la respuesta conductual en paradigmas de sedación controlada por el paciente.

    Principales resultados:

    • Deep-ConTrans logró altas precisiones de clasificación promedio: 93,93 % (±3,32 %) para propofol y 97,42 % (±1,68 %) para midazolam.
    • El modelo demostró una fuerte generalización entre anestésicos, manteniendo el rendimiento entre propofol y midazolam.
    • Las firmas de EEG de las transiciones incluyeron un aumento de la potencia delta frontal y alfa parietal, consistente con la bistabilidad cortical.

    Conclusiones:

    • Deep-ConTrans ofrece un método más granular y confiable para la monitorización intraoperatoria que las evaluaciones binarias.
    • La generalización y sensibilidad del modelo permiten la identificación precisa de transiciones críticas, mejorando el manejo anestésico.
    • Este enfoque facilita protocolos de sedación personalizados basados en la dinámica del EEG en tiempo real, minimizando el riesgo de alerta intraoperatoria.