Examine the effect of different web-based media on human brain waves

Katona, József; Ujbányi, Tibor; Sziladi, Gergely; Kővári, Attila

doi:10.1109/coginfocom.2017.8268280

Cited by 33 publications

(25 citation statements)

References 17 publications

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“…Each X i takes binary form, 0 or 1. We define dependent variable Y as the following form, namely Model (12),…”

Section: Artificial Example Ii: Four Modules With Discrete Variablesmentioning

confidence: 99%

“…which is a more complicated problem than Model (7). The complexity is higher since there are four modules instead of two modules in Model (12).…”

Section: Artificial Example Ii: Four Modules With Discrete Variablesmentioning

confidence: 99%

“…All together, we should expect θ c ({S 1 , S 2 , S 3 , S 4 }) = max(0.75, 0.625, 0.5625) = 75%. In other words, the derivation for the theoretical prediction of each variable module in the underlying Model (12) helps us to understand that with correct model specification, we should expect simulation results to be approximately 75% on average. Scenario II.…”

Section: Algorithmmentioning

confidence: 99%

“…Although the advantage of the I-score is shown using a CNN in image classification tasks in this paper, the application of the I-score can be adapted to any large-scale data set in any supervised learning research task. The proposed methodology can be applied in a variety of different fields such as human computer interaction using brain waves [12,13], brain-computer interfaces [14], PEBL-based attention tests [14], and potentially advanced eye-tracking based evaluation that assist human-computer interaction [15].…”

Section: Introduction 1ai Systems For Covid-19 Chest X-raysmentioning

confidence: 99%

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An Interaction-Based Convolutional Neural Network (ICNN) Toward a Better Understanding of COVID-19 X-ray Images

Yin

2021

Algorithms

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The field of explainable artificial intelligence (XAI) aims to build explainable and interpretable machine learning (or deep learning) methods without sacrificing prediction performance. Convolutional neural networks (CNNs) have been successful in making predictions, especially in image classification. These popular and well-documented successes use extremely deep CNNs such as VGG16, DenseNet121, and Xception. However, these well-known deep learning models use tens of millions of parameters based on a large number of pretrained filters that have been repurposed from previous data sets. Among these identified filters, a large portion contain no information yet remain as input features. Thus far, there is no effective method to omit these noisy features from a data set, and their existence negatively impacts prediction performance. In this paper, a novel interaction-based convolutional neural network (ICNN) is introduced that does not make assumptions about the relevance of local information. Instead, a model-free influence score (I-score) is proposed to directly extract the influential information from images to form important variable modules. This innovative technique replaces all pretrained filters found by trial-and-error with explainable, influential, and predictive variable sets (modules) determined by the I-score. In other words, future researchers need not rely on pretrained filters; the suggested algorithm identifies only the variables or pixels with high I-score values that are extremely predictive and important. The proposed method and algorithm were tested on real-world data set and a state-of-the-art prediction performance of 99.8% was achieved without sacrificing the explanatory power of the model. This proposed design can efficiently screen patients infected by COVID-19 before human diagnosis and can be a benchmark for addressing future XAI problems in large-scale data sets.

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“…Each X i takes binary form, 0 or 1. We define dependent variable Y as the following form, namely Model (12),…”

Section: Artificial Example Ii: Four Modules With Discrete Variablesmentioning

confidence: 99%

“…which is a more complicated problem than Model (7). The complexity is higher since there are four modules instead of two modules in Model (12).…”

Section: Artificial Example Ii: Four Modules With Discrete Variablesmentioning

confidence: 99%

Section: Algorithmmentioning

confidence: 99%

Section: Introduction 1ai Systems For Covid-19 Chest X-raysmentioning

confidence: 99%

See 2 more Smart Citations

An Interaction-Based Convolutional Neural Network (ICNN) Toward a Better Understanding of COVID-19 X-ray Images

Yin

2021

Algorithms

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show abstract

“…In this work, we exploit these learned associations among the abstract concepts to simulate a regulated recall operation. In recent years, advances in technologies have played an essential role in cognitive and psychological research, e.g., use of devices like GP3 [16], TheEyeTribe [17], and electroencephalography (EEG) [18,19] to study visual attention. In this article, we focus on representations of computational models that are also very useful in understanding the psychological and cognitive phenomena, validating the existing cognitive theories, and helping to formulate fresh ideas related to cognition [20][21][22][23].…”

Section: Introductionmentioning

confidence: 99%

Emulating Cued Recall of Abstract Concepts via Regulated Activation Networks

et al. 2021

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concepts play a vital role in decision-making or recall operations because the associations among them are essential for contextual processing. Abstract concepts are complex and difficult to represent (conceptually, formally, or computationally), leading to difficulties in their comprehension and recall. This contribution reports the computational simulation of the cued recall of abstract concepts by exploiting their learned associations. The cued recall operation is realized via a novel geometric back-propagation algorithm that emulates the recall of abstract concepts learned through regulated activation network (RAN) modeling. During recall operation, another algorithm uniquely regulates the activation of concepts (nodes) by injecting excitatory, neutral, and inhibitory signals to other concepts of the same level. A Toy-data problem is considered to illustrate the RAN modeling and recall procedure. The results display how regulation enables contextual awareness among abstract nodes during the recall process. The MNIST dataset is used to show how recall operations retrieve intuitive and non-intuitive blends of abstract nodes. We show that every recall process converges to an optimal image. With more cues, better images are recalled, and every intermediate image obtained during the recall iterations corresponds to the varying cognitive states of the recognition procedure.

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Blink-To-Live eye-based communication system for users with speech impairments

Ezzat,

Maged,

Gamal

et al. 2023

Sci Rep

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Eye-based communication languages such as Blink-To-Speak play a key role in expressing the needs and emotions of patients with motor neuron disorders. Most invented eye-based tracking systems are complex and not affordable in low-income countries. Blink-To-Live is an eye-tracking system based on a modified Blink-To-Speak language and computer vision for patients with speech impairments. A mobile phone camera tracks the patient’s eyes by sending real-time video frames to computer vision modules for facial landmarks detection, eye identification and tracking. There are four defined key alphabets in the Blink-To-Live eye-based communication language: Left, Right, Up, and Blink. These eye gestures encode more than 60 daily life commands expressed by a sequence of three eye movement states. Once the eye gestures encoded sentences are generated, the translation module will display the phrases in the patient’s native speech on the phone screen, and the synthesized voice can be heard. A prototype of the Blink-To-Live system is evaluated using normal cases with different demographic characteristics. Unlike the other sensor-based eye-tracking systems, Blink-To-Live is simple, flexible, and cost-efficient, with no dependency on specific software or hardware requirements. The software and its source are available from the GitHub repository (https://github.com/ZW01f/Blink-To-Live).

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Examine the effect of different web-based media on human brain waves

Cited by 33 publications

References 17 publications

An Interaction-Based Convolutional Neural Network (ICNN) Toward a Better Understanding of COVID-19 X-ray Images

An Interaction-Based Convolutional Neural Network (ICNN) Toward a Better Understanding of COVID-19 X-ray Images

Emulating Cued Recall of Abstract Concepts via Regulated Activation Networks

Blink-To-Live eye-based communication system for users with speech impairments

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