Scholarlink

Electroencephalography (EEG) signals are often utilized to study cognitive processes and brain diseases. The non-stationary and non-linear nature of EEG signals makes their analysis difficult. A deep learning framework that suggested classifying seizures based on scalp EEG signals and automating cognitive tasks. We use a pre-processing module based on the Hilbert–Huang Transform (HHT) and Variational Mode Decomposition (VMD) to extract features from raw EEG data. We present an approach that combines deep learning with HHT and VMD for rapid and precise seizure detection. Our method detects seizures with an astounding 98.40% accuracy. Our suggested techniques have great potential for quantifying brain wave patterns and advancing neuroscience research, even outside of classification applications.

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 for Cognitive Task and Seizure Classification with Hilbert–Huang Transform and Variational Mode Decomposition

  • 会议论文

  • 发表日期：

    2025年

查看全文
  • Shraddha Jain,
  • Rajeev Srivastava

摘要

Electroencephalography (EEG) signals are often utilized to study cognitive processes and brain diseases. The non-stationary and non-linear nature of EEG signals makes their analysis difficult. A deep learning framework that suggested classifying seizures based on scalp EEG signals and automating cognitive tasks. We use a pre-processing module based on the Hilbert–Huang Transform (HHT) and Variational Mode Decomposition (VMD) to extract features from raw EEG data. We present an approach that combines deep learning with HHT and VMD for rapid and precise seizure detection. Our method detects seizures with an astounding 98.40% accuracy. Our suggested techniques have great potential for quantifying brain wave patterns and advancing neuroscience research, even outside of classification applications.