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SFT-HN: a novel spatial-frequency-temporal hybrid network for EEG-based emotion recognition.

Lei Zhu1, Yu Ding1, Aiai Hung1

  • 1School of Automation, Hangzhou Dianzi University, Hangzhou, 310000 China.

Cognitive Neurodynamics
|November 7, 2025
PubMed
Summary
This summary is machine-generated.

This study introduces a novel Spatial-Frequency-Temporal Hybrid Network (SFT-HN) for advanced electroencephalograph (EEG) emotion recognition. The SFT-HN model effectively fuses EEG spatial, frequency, and temporal information, achieving high accuracy in emotion classification tasks.

Keywords:
Deep learningDifferential entropyEEGEmotion recognition

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Area of Science:

  • Neuroscience
  • Artificial Intelligence
  • Signal Processing

Background:

  • Electroencephalograph (EEG) emotion recognition is crucial for brain-computer interfaces (BCIs).
  • Deep learning methods outperform traditional techniques in EEG emotion recognition.
  • Challenges remain in fusing spatial, frequency, and temporal EEG information and utilizing discriminative local patterns.

Purpose of the Study:

  • To propose a novel hybrid model, the Spatial-Frequency-Temporal Hybrid Network (SFT-HN), for enhanced EEG emotion recognition.
  • To effectively fuse spatial, frequency, and temporal information from EEG signals.
  • To leverage discriminative local patterns for improved emotion classification.

Main Methods:

  • Developed a Spatial-Frequency-Temporal Hybrid Network (SFT-HN) incorporating Spatial Frequency Residual Modules (SFRM) and an attention-based Bidirectional Long Short-Term Memory (ATBI-LSTM).
  • Utilized 4D representations of raw EEG signals to preserve spatial, frequency, and temporal information.
  • Employed split-convert-merge techniques, residual, and attention mechanisms within SFRM for spatial-frequency feature extraction.
  • Incorporated an attention mechanism in ATBI-LSTM to capture temporal dependencies.

Main Results:

  • Achieved average accuracies of 97.61% (arousal) and 97.57% (valence) on the DEAP dataset.
  • Attained an average accuracy of 97.44% on the SEED dataset.
  • Demonstrated robust generalization with an average accuracy of 96.24% on the novel FACED dataset.

Conclusions:

  • The SFT-HN model effectively integrates spatial, frequency, and temporal EEG features for superior emotion recognition.
  • The proposed model demonstrates high accuracy and robust generalization across multiple datasets.
  • The SFT-HN offers a promising advancement in EEG-based emotion recognition for BCIs.