Scholarlink

The growing interest in improved rehabilitation systems and assistive technologies for individuals with motor impairments necessitates the need for new applications of Deep Learning approaches for Brain–Computer Interface (BCI) implementation. This study investigates the application of Deep Learning techniques, specifically the Hierarchical 3D Convolutional Neural Network (H3DCNN) model, for enhancing classification systems utilizing electroencephalography (EEG) data. As such, topographic maps were extracted from EEG signals in a real motion task experiment integrating 4 different motions. The H3DCNN model was then employed in a step-wise fashion to classify and decode the EEG signals, demonstrating its effectiveness in distinguishing between different movement intentions. Moreover, three different optimizers were implemented, including RMSprop, Adam, and Stochastic Gradient Descent (SGD), to further assess and enhance the model performance. The findings indicate that the integration of advanced deep learning techniques can significantly enhance the accuracy and reliability of BCI systems, with RMSprop and SGD showing superior results in terms of accuracy. Moreover, our results illustrate the possibility of decoding neural mechanisms via deep learning paradigms, paving the way for future developments in BCI applications, thus aiming to improve the quality of life for individuals with motor impairments.

错误：搜索内容不能为空，请输入英文关键词
错误：关键词超出字数限制，请精简
高级检索

Deep Learning Discrimination for BCI Implementation Using 3D Convolutional Neural Network and EEG Topographic Maps

  • 会议论文

  • 发表日期：

    2026年

查看全文
  • Stavros-Theofanis Miloulis,
  • Ioannis Kakkos,
  • Ioannis Zorzos,
  • Aikaterini Karampasi,
  • Athanasios Anastasiou,
  • Pantelis Asvestas,
  • Errikos C. Ventouras,
  • Ioannis Kalatzis,
  • George K. Matsopoulos

摘要

The growing interest in improved rehabilitation systems and assistive technologies for individuals with motor impairments necessitates the need for new applications of Deep Learning approaches for Brain–Computer Interface (BCI) implementation. This study investigates the application of Deep Learning techniques, specifically the Hierarchical 3D Convolutional Neural Network (H3DCNN) model, for enhancing classification systems utilizing electroencephalography (EEG) data. As such, topographic maps were extracted from EEG signals in a real motion task experiment integrating 4 different motions. The H3DCNN model was then employed in a step-wise fashion to classify and decode the EEG signals, demonstrating its effectiveness in distinguishing between different movement intentions. Moreover, three different optimizers were implemented, including RMSprop, Adam, and Stochastic Gradient Descent (SGD), to further assess and enhance the model performance. The findings indicate that the integration of advanced deep learning techniques can significantly enhance the accuracy and reliability of BCI systems, with RMSprop and SGD showing superior results in terms of accuracy. Moreover, our results illustrate the possibility of decoding neural mechanisms via deep learning paradigms, paving the way for future developments in BCI applications, thus aiming to improve the quality of life for individuals with motor impairments.