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A bidirectional interaction-based hybrid network architecture for EEG cognitive recognition.

Yue Zhao1, Hong Zeng2, Haohao Zheng1

  • 1School of Computer Science and Technology, Hangzhou Dianzi University, Hangzhou 310018, China.

Computer Methods and Programs in Biomedicine
|May 20, 2023
PubMed
Summary
This summary is machine-generated.

This study introduces a novel bidirectional interaction-based hybrid network (BIHN) for electroencephalography (EEG) cognitive recognition. The BIHN architecture effectively integrates cognitive and computational representations from EEG data, significantly improving brain cognitive status recognition capabilities.

Keywords:
Bidirectional interactionCognitive networksComputing networksEEGHybrid networkKnowledge distillation

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Area of Science:

  • Neuroscience
  • Artificial Intelligence
  • Biomedical Engineering

Background:

  • Simultaneous extraction of cognitive and computational representations from EEG data is crucial for enhanced brain cognitive status recognition.
  • Existing models struggle with the significant gap in information interaction between cognitive and computational representations.
  • The potential of leveraging the interaction between these two information types for improved EEG analysis remains largely unexplored.

Purpose of the Study:

  • To introduce a novel architecture, the bidirectional interaction-based hybrid network (BIHN), for improved EEG cognitive recognition.
  • To address the limitations of existing studies by effectively modeling the interaction between cognitive and computational representations.
  • To enhance the accuracy and capability of brain cognitive status recognition using EEG data.

Main Methods:

  • Developed a hybrid network architecture (BIHN) comprising a cognitive-based network (CogN) and a computing-based network (ComN).
  • Utilized Graph Convolutional Network (GCN) or Capsule Network (CapsNet) for CogN and EEGNet for ComN to extract distinct feature representations.
  • Implemented a bidirectional distillation-based coadaptation (BDC) algorithm for synergistic information exchange and coadaptation between CogN and ComN.

Main Results:

  • The BIHN architecture, using GCN + EEGNet and CapsNet + EEGNet pairs, demonstrated superior performance on the Fatigue-Awake EEG dataset (FAAD) and SEED dataset.
  • Achieved average accuracies of 78.76% (GCN + EEGNet) and 77.58% (CapsNet + EEGNet) on FAAD.
  • Attained average accuracies of 55.38% (GCN + EEGNet) and 55.10% (CapsNet + EEGNet) on SEED, outperforming models without bidirectional interaction.

Conclusions:

  • The proposed BIHN significantly enhances EEG processing and cognitive recognition capabilities.
  • The architecture effectively improves the performance of both the cognitive (CogN) and computing (ComN) networks.
  • BIHN shows promise for advancing brain-computer collaborative intelligence and applications in cognitive status monitoring.