Evaluation of divided attention using different stimulation models in event-related potentials

Batbat, Turgay; Güven, Ayşegül; Dolu, Nazan

doi:10.1007/s11517-019-02013-x

Cited by 5 publications

(2 citation statements)

References 52 publications

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“…In the studies [ 46 , 47 , 48 ], the peak point of the curve was chosen as the reference point, and area calculations were made accordingly. Therefore, in this study, various features were extracted according to the area under the PSD curve [ 49 , 50 , 51 ]. The features are defined in Table 2 .…”

Section: Methodsmentioning

confidence: 99%

“…After the calculation of features from the EEG signal obtained at different stages, we performed the statistical analysis. As examined in the literature, most studies use the ANOVA test to compare multiple groups [ 49 , 50 , 51 ]. Therefore, we applied the normality test of Kolmogorov–Smirnov by considering p = 0.05 in the case of all statistical tests using the SPSS statistical software package.…”

Section: Methodsmentioning

confidence: 99%

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Quantitative Electroencephalography Analysis for Improved Assessment of Consciousness Levels in Deep Coma Patients Using a Proposed Stimulus Stage

et al. 2023

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“Coma” is defined as an inability to obey commands, to speak, or to open the eyes. So, a coma is a state of unarousable unconsciousness. In a clinical setting, the ability to respond to a command is often used to infer consciousness. Evaluation of the patient’s level of consciousness (LeOC) is important for neurological evaluation. The Glasgow Coma Scale (GCS) is the most widely used and popular scoring system for neurological evaluation and is used to assess a patient’s level of consciousness. The aim of this study is the evaluation of GCSs with an objective approach based on numerical results. So, EEG signals were recorded from 39 patients in a coma state with a new procedure proposed by us in a deep coma state (GCS: between 3 and 8). The EEG signals were divided into four sub-bands as alpha, beta, delta, and theta, and their power spectral density was calculated. As a result of power spectral analysis, 10 different features were extracted from EEG signals in the time and frequency domains. The features were statistically analyzed to differentiate the different LeOC and to relate with the GCS. Additionally, some machine learning algorithms have been used to measure the performance of the features for distinguishing patients with different GCSs in a deep coma. This study demonstrated that GCS 3 and GCS 8 patients were classified from other levels of consciousness in terms of decreased theta activity. To the best of our knowledge, this is the first study to classify patients in a deep coma (GCS between 3 and 8) with 96.44% classification performance.

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Section: Methodsmentioning

confidence: 99%

Section: Methodsmentioning

confidence: 99%

Quantitative Electroencephalography Analysis for Improved Assessment of Consciousness Levels in Deep Coma Patients Using a Proposed Stimulus Stage

et al. 2023

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Detection of amyotrophic lateral sclerosis disease from event-related potentials using variational mode decomposition method

Orhanbulucu

Latifoğlu

2021

Computer Methods in Biomechanics and Biomedical Engineering

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RSVP-based BCI for inconspicuous targets: detection, localization, and modulation of attention

Zhou,

Zhang,

Wang

et al. 2024

J. Neural Eng.

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Objective. While Brain-Computer Interface (BCI) based on Rapid Serial Visual Presentation (RSVP) is widely used in target detection, patterns of Event-Related Potential (ERP), as well as the performance on detecting inconspicuous targets remain unknown. Moreover, participant-screening methods to excluded ‘BCI-blind’ users are still lacking. Approach. A RSVP paradigm was designed with targets of varied concealment, size, and location. ERPs (e.g. P300 and N2pc) and target detection accuracy were compared among these conditions. The relationship between participants’ attention scores and target detection accuracy was also analyzed to test attention level as a criterion for participant screening. Main results. Statistical analysis showed conditions of target concealment and size had significant influences on ERP. In particular, ERP for inconspicuous targets, such as concealed and small targets, had lower amplitude and longer latency. In consistent, the accuracy of detection in inconspicuous condition was significantly lower than that of conspicuous condition. In addition, a significant association was found between attention scores and target detection accuracy for camouflaged targets. Significance. The study was the first to address ERP features among multiple dimensions of concealment, size, and location. The conclusion provided insights into the relationship between ERP decoding and properties of targets. In addition, the association between attention scores and detection accuracy implied a promising method in screening well-behaved participants for camouflaged target detection.

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Evaluation of divided attention using different stimulation models in event-related potentials

Cited by 5 publications

References 52 publications

Quantitative Electroencephalography Analysis for Improved Assessment of Consciousness Levels in Deep Coma Patients Using a Proposed Stimulus Stage

Quantitative Electroencephalography Analysis for Improved Assessment of Consciousness Levels in Deep Coma Patients Using a Proposed Stimulus Stage

Detection of amyotrophic lateral sclerosis disease from event-related potentials using variational mode decomposition method

RSVP-based BCI for inconspicuous targets: detection, localization, and modulation of attention

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