Experimenting with classifiers in biofeedback-based mental effort measurement

Gazdi, Laszlo; Pomazi, Krisztian; Radostyan, Bertalan; Szabó, Máté; Szegletes, Luca; Forstner, Bertalan

doi:10.1109/coginfocom.2016.7804571

Cited by 6 publications

(4 citation statements)

References 11 publications

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“…In our paper, the EEG wave is utilized as the feature map in the time domain. Spectrum feature The EEG wave has frequency of levels: λ 1 (0-3 Hz), λ 2 (3-6 Hz), λ 3 (6-10 Hz), λ 4 (10-16 Hz), λ 5 (16)(17)(18)(19)(20)(21)(22)(23)(24)(25)(26)(27)(28)(29)(30)(31)(32), and λ 6 . Where, λ i is the wavelength at level i, and HZ represents Hertz.…”

Section: Time Domainmentioning

confidence: 99%

“…The range of both metrics is between 0 and 1. the metric value is directly proportional to the cluster accuracy metric. The specific computation of the two indicators is referenced [23].…”

Section: Experimental Settingsmentioning

confidence: 99%

“…Authors in [22] utilized wavelet discrete transforms to identify Encephalitis. In [23], they presented the entropy based neural networks to categorize Encephalitis EEG data. In [24], they utilized a discrete cosine transform for preprocessing and classification to enhance the performance.…”

Section: Introductionmentioning

confidence: 99%

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Encephalitis Detection from EEG Fuzzy Density-Based Clustering Model with Multiple Centroid

Mengash¹,

Hafez²,

Mahmoud³

2023

Intelligent Automation &Amp; Soft Computing

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Encephalitis is a brain inflammation disease. Encephalitis can yield to seizures, motor disability, or some loss of vision or hearing. Sometimes, encephalitis can be a life-threatening and proper diagnosis in an early stage is very crucial. Therefore, in this paper, we are proposing a deep learning model for computerized detection of Encephalitis from the electroencephalogram data (EEG). Also, we propose a Density-Based Clustering model to classify the distinctive waves of Encephalitis. Customary clustering models usually employ a computed single centroid virtual point to define the cluster configuration, but this single point does not contain adequate information. To precisely extract accurate inner structural data, a multiple centroids approach is employed and defined in this paper, which defines the cluster configuration by allocating weights to each state in the cluster. The multiple EEG view fuzzy learning approach incorporates data from every single view to enhance the model's clustering performance. Also a fuzzy Density-Based Clustering model with multiple centroids (FDBC) is presented. This model employs multiple real state centroids to define clusters using Partitioning Around Centroids algorithm. The Experimental results validate the medical importance of the proposed clustering model.

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Section: Time Domainmentioning

confidence: 99%

“…The range of both metrics is between 0 and 1. the metric value is directly proportional to the cluster accuracy metric. The specific computation of the two indicators is referenced [23].…”

Section: Experimental Settingsmentioning

confidence: 99%

See 1 more Smart Citation

Encephalitis Detection from EEG Fuzzy Density-Based Clustering Model with Multiple Centroid

Mengash¹,

Hafez²,

Mahmoud³

2023

Intelligent Automation &Amp; Soft Computing

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“…CogInfoCom covers several disciplines appearing in applications and research areas also. CogInfoCom is available technology from socio-cognitive ICT [55][56][57][58][59][60][61][62][63][64][65] to cognitive aided engineering [66][67][68][69][70][71][72][73][74][75][76][77] and its related aspects in terms of online collaborative systems and virtual reality solutions [78][79][80], teaching-learning [81][82][83][84][85][86] and human cognitive interfaces such as braincomputer interfaces (BCIs) [87][88][89][90][91][92] and medicals [93][94][95][96].…”

Section: Human-computer Interfaces and Cognitive Infocommunicationmentioning

confidence: 99%

Examining the Learning Efficiency by a Brain-Computer Interface System

2018

APH

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Brain research is one of the most significant research areas of the last decades, in which many developments and modern engineering technologies are applied. The electroencephalogram (EEG)-based brain activity observation processes are very promising and have been used in several engineering research fields. Objective: The main goal of this research was to develop a Brain-Computer Interface (BCI) system to observe the level of vigilance calculated by Think Gear-ASIC Module (TGAM1) technology and to evaluate the output with learning efficiency tests applied in cognitive neuroscience. Methods: The performance of the BCI system is evaluated in a comparative study. The BCI system was tested by thirty-two test subjects and the attention level output was compared by the Psychology Experiment Building Language's (PEBL's) Corsi block test (P CORSI) and PEBL's Ebbinghaus procedure (P EBBINGHAUS) tasks. Results: Using the BCI, we have shown statistically significant results between the BCI and the conventional cognitive neuroscience tests. The correlation between the tests and the average attention of the BCI was slightly strong for P CORSI Total Score (r=.63, p<.01 (2-tailed) and slightly strong for P EBBINGHAUS Total Correct (r=-.71, p<.01 (2-tailed). The average level of attention measured by the BCI system during the P CORSI test was 49.00%, while in case of the P EBBINGHAUS test it was 52.41% on all samples. Conclusion: The developed BCI system has a significant correlation with P CORSI and P EBBINGHAUS cognitive neuroscience tests. The BCI system is capable of observing attentional vigilance continuously. Significance: The developed BCI system is applicable to observe vigilance level in realtime while the level of attention depends on activities.

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