Optimising Ground Penetrating Radar data interpretation: A hybrid approach with AI-assisted Kalman Filter and Wavelet Transform for detecting and locating buried utilities

Afrasiabi, Arasti; Faramarzi, Asaad; Chapman, David; Keshavarzi, Alireza

doi:10.1016/j.jappgeo.2024.105567

Journal of Applied Geophysics

2025

DOI: 10.1016/j.jappgeo.2024.105567

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Optimising Ground Penetrating Radar data interpretation: A hybrid approach with AI-assisted Kalman Filter and Wavelet Transform for detecting and locating buried utilities

Arasti Afrasiabi,

Asaad Faramarzi,

David Chapman

et al.

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An Ensemble Deep Learning Approach for EEG-Based Emotion Recognition Using Multi-Class CSP

Yousefipour,

Rajabpour,

Abdoljabbari

et al. 2024

Biomimetics

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In recent years, significant advancements have been made in the field of brain–computer interfaces (BCIs), particularly in the area of emotion recognition using EEG signals. The majority of earlier research in this field has missed the spatial–temporal characteristics of EEG signals, which are critical for accurate emotion recognition. In this study, a novel approach is presented for classifying emotions into three categories, positive, negative, and neutral, using a custom-collected dataset. The dataset used in this study was specifically collected for this purpose from 16 participants, comprising EEG recordings corresponding to the three emotional states induced by musical stimuli. A multi-class Common Spatial Pattern (MCCSP) technique was employed for the processing stage of the EEG signals. These processed signals were then fed into an ensemble model comprising three autoencoders with Convolutional Neural Network (CNN) layers. A classification accuracy of 99.44 ± 0.39% for the three emotional classes was achieved by the proposed method. This performance surpasses previous studies, demonstrating the effectiveness of the approach. The high accuracy indicates that the method could be a promising candidate for future BCI applications, providing a reliable means of emotion detection.

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An Ensemble Deep Learning Approach for EEG-Based Emotion Recognition Using Multi-Class CSP

Yousefipour,

Rajabpour,

Abdoljabbari

et al. 2024

Biomimetics

View full text Add to dashboard Cite

show abstract

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Optimising Ground Penetrating Radar data interpretation: A hybrid approach with AI-assisted Kalman Filter and Wavelet Transform for detecting and locating buried utilities

Cited by 1 publication

References 27 publications

An Ensemble Deep Learning Approach for EEG-Based Emotion Recognition Using Multi-Class CSP

An Ensemble Deep Learning Approach for EEG-Based Emotion Recognition Using Multi-Class CSP

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