C - 24EEG Net Tolerance Training for an Adult with Level Three Autism Spectrum Disorder

O'Grady, Jason S; Francis, John; Gordon, Barry; Davis, Kim

doi:10.1093/arclin/acy061.177

Cited by 2 publications

(2 citation statements)

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“…To classified the cognitive training trace, three commonly EEG classification models used at present were compared: EEG-Net [33], 4-CNN [34], and EEG-GRUNet [35].…”

Section: Eeg Signal Classification Resultsmentioning

confidence: 99%

Navigation Learning Assessment Using EEG-Based Multi-Time Scale Spatiotemporal Compound Model

Wang,

Liu,

et al. 2024

IEEE Trans. Neural Syst. Rehabil. Eng.

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This study presents a novel method to assess the learning effectiveness using Electroencephalography (EEG)-based deep learning model. It is difficult to assess the learning effectiveness of professional courses in cultivating students' ability objectively by questionnaire or other assessment methods. Research in the field of brain has shown that innovation ability can be reflected from cognitive ability which can be embodied by EEG signal features. Three navigation tasks of increasing cognitive difficulty were designed and a total of 41 subjects participated in the experiment. For the classification and tracking of the subjects' EEG signals, a convolutional neural network (CNN)-based Multi-Time Scale Spatiotemporal Compound Model (MTSC) is proposed in this paper to extract and classify the features of the subjects' EEG signals. Furthermore, Spiking neural networks (SNN) -based NeuCube is used to assess the learning effectiveness and demonstrate cognitive processes, acknowledging that NeuCube is an excellent method to display the spatiotemporal differences between spikes emitted by neurons. The results of the classification experiment show that the cognitive training traces of different students in solving three navigational problems can be effectively distinguished. More importantly, new information about navigation is revealed through the analysis of feature vector visualization and model dynamics. This work provides a foundation for future research on cognitive navigation and the training of students' navigational skills.

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“…To classified the cognitive training trace, three commonly EEG classification models used at present were compared: EEG-Net [33], 4-CNN [34], and EEG-GRUNet [35].…”

Section: Eeg Signal Classification Resultsmentioning

confidence: 99%

Navigation Learning Assessment Using EEG-Based Multi-Time Scale Spatiotemporal Compound Model

Wang,

Liu,

et al. 2024

IEEE Trans. Neural Syst. Rehabil. Eng.

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“…To verify the validity of MTSC, three EEG classification models commonly used at present were compared: EEG-Net [28], 4-CNN [29], and EEG-GRUNet [30]. The experimental samples were input into the three classification models and MTSC.…”

Section: Eeg Signal Classification Resultsmentioning

confidence: 99%

Navigation Learning Assessment Using EEG-based Multi-Time Scale Spatiotemporal Compound Model

Wang¹,

Liu²,

Li³

et al. 2023

Preprint

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<p> In this paper, Electroencephalography (EEG) from physiological indicators was introduced to the assessment of teaching effectiveness in the higher education. The Multi-Time Scale Spatiotemporal Compound Model (MTSC) was proposed to extract and classify the features of participants’ EEG signals. The presented MTSC objectively elucidated that the Hand on Modular Engineering Project-Based (HMEP-based) navigation course reshapes students’ innovation ability. </p>

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C - 24EEG Net Tolerance Training for an Adult with Level Three Autism Spectrum Disorder

Cited by 2 publications

References 0 publications

Navigation Learning Assessment Using EEG-Based Multi-Time Scale Spatiotemporal Compound Model

Navigation Learning Assessment Using EEG-Based Multi-Time Scale Spatiotemporal Compound Model

Navigation Learning Assessment Using EEG-based Multi-Time Scale Spatiotemporal Compound Model

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