Neural Correspondence to Human Cognition from Analysis to Intuition – Implications of Display Design for Cognition

Nuamah, Joseph; Seong, Younho

doi:10.1177/1541931213601508

Cited by 6 publications

(4 citation statements)

References 24 publications

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“…Additionally, as in [ 32 , 33 , 34 , 35 ], Pearson’s correlation between the implemented measures was used to determine the association between measures and cognitive states as a validation method for the subject’s internal state. A hypothesis was defined, where, if the EEG signals were correlated with the subjective and vehicle performance measures, the subject experienced the same level of cognitive workload internally and externally.…”

Section: Methodsmentioning

confidence: 99%

Feature Selection Model based on EEG Signals for Assessing the Cognitive Workload in Drivers

Becerra-Sánchez

Reyes-Muñoz

Ibáñez

2020

Sensors

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In recent years, research has focused on generating mechanisms to assess the levels of subjects’ cognitive workload when performing various activities that demand high concentration levels, such as driving a vehicle. These mechanisms have implemented several tools for analyzing the cognitive workload, and electroencephalographic (EEG) signals have been most frequently used due to their high precision. However, one of the main challenges in implementing the EEG signals is finding appropriate information for identifying cognitive states. Here, we present a new feature selection model for pattern recognition using information from EEG signals based on machine learning techniques called GALoRIS. GALoRIS combines Genetic Algorithms and Logistic Regression to create a new fitness function that identifies and selects the critical EEG features that contribute to recognizing high and low cognitive workloads and structures a new dataset capable of optimizing the model’s predictive process. We found that GALoRIS identifies data related to high and low cognitive workloads of subjects while driving a vehicle using information extracted from multiple EEG signals, reducing the original dataset by more than 50% and maximizing the model’s predictive capacity, achieving a precision rate greater than 90%.

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Section: Methodsmentioning

confidence: 99%

Feature Selection Model based on EEG Signals for Assessing the Cognitive Workload in Drivers

Becerra-Sánchez

Reyes-Muñoz

Ibáñez

2020

Sensors

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“…EEG signals are processed using power spectral density (PSD) to extract the most representative features in the context of cognitive workload. These characteristics are defined as frequency bands: Delta (0.5-4 Hz), Theta (4-8 Hz), Alpha (8-12 Hz ), Beta (12)(13)(14)(15)(16)(17)(18)(19)(20)(21)(22)(23)(24)(25)(26)(27)(28)(29)(30), and Gamma (30-100 Hz) [23].…”

Section: Methodsmentioning

confidence: 99%

“…where samples EEG with a value of ≤ are set within the search space. Additionally, as in [25,26,27,28,29], we used the Pearson's correlation between the implemented measures to determine the association between measures and cognitive states as a validation's method of the internal state of the subject. A hypothesis is defined, where if the EEG signals are correlated with the subjective and vehicle performance measures, the subject experienced the same level of cognitive workload internally and externally.…”

Section: ≤ ∴ ℎmentioning

confidence: 99%

Feature Selection Model based on EEG signals to Assess the Cognitive Workload in Drivers

Becerra-Sánchez¹,

Reyes²,

Ibáñez³

2020

Preprint

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In recent years, research has focused on generating mechanisms to assess the levels of subjects' cognitive workload when performing various activities that demand high concentration levels, such as driving a vehicle. These mechanisms have implemented several tools to analyze cognitive workload where the electroencephalographic (EEG) signals are the most used due to its high precision. However, one of the main challenges in the EEG signals implementing is finding the appropriate information to identify cognitive states. Here we show a new feature selection model for pattern recognition using information from EEG signals based on machine learning techniques called GALoRIS. GALoRIS combines Genetic Algorithms and Logistic Regression to create a new fitness function that identifies and selects the critical EEG features that contribute to recognizing high and low cognitive workload and structures a new dataset capable of optimizing the model's predictive process. We found that GALoRIS identifies data related to high and low cognitive workload of subjects while driving a vehicle using information extracted from multiple EEG signals, reducing the original dataset by more than 50%, maximizing the model's predictive capacity-achieving a precision rate greater than 90%.

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“…Communication services include PBX and ISDN. Multimedia services include movies and TV, media and other related systems, remote video transmission, interactive TV, interactive order-based, and other services [18,19]. Te general development trend of the Internet of Tings is shown in Figure 1.…”

Section: Analysis Of Related Technologies Of Iot Heterogeneousmentioning

confidence: 99%

Display Design Model Based on the Internet of Things Prototype System

Wang

Yan

2023

Mobile Information Systems

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The display design of the Internet of Things prototype system is the top priority of the design of the display space, and it is also a problem to be studied in the future development of the display space. However, the current research on the display design direction of IoT prototype system is not deep enough. This article mainly studies the display design model based on the Internet of Things prototype system. This article mainly uses the related technology of IoT heterogeneous protocol to analyze the structure of the IoT prototype system and proposes to use the heterogeneous network to speculate the future development trend of the IoT. In the aspect of display design, applications such as network information retrieval and data mining at the level of missions are accelerated in depth so that the arrangement of multiple factors is more reasonable. Finally, the data conversion function is used to design each port in the IoT accessor architecture. The protocol is adapted and converted, and the parsed protocol is uniformly encapsulated and uniformly transmitted on the platform. The model in this article integrates various heterogeneous detection networks, realizes the standardized operation of top-level applications, and completes the decoupling of top-level applications. It is encapsulated in a unified protocol and meets the standard operation requirements of web applications. The experimental data in this article show that the frequency of the audio signal designed by the system under normal working conditions is mostly concentrated at about 180 Hz, and the highest frequency value is less than 800 Hz; when the system has a partial discharge failure, the frequency of the audio signal is mostly concentrated between 1400 Hz and 1800 Hz. The experimental results of this article show that the prototype system of the Internet of Things can identify and judge the state of the display design model. Applying it in practice can effectively solve practical problems such as large-scale data storage of the Internet of Things.

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Neural Correspondence to Human Cognition from Analysis to Intuition – Implications of Display Design for Cognition

Cited by 6 publications

References 24 publications

Feature Selection Model based on EEG Signals for Assessing the Cognitive Workload in Drivers

Feature Selection Model based on EEG Signals for Assessing the Cognitive Workload in Drivers

Feature Selection Model based on EEG signals to Assess the Cognitive Workload in Drivers

Display Design Model Based on the Internet of Things Prototype System

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