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Related Experiment Video

Updated: May 21, 2026

Artificial Intelligence-Based System for Detecting Attention Levels in Students
06:37

Artificial Intelligence-Based System for Detecting Attention Levels in Students

Published on: December 15, 2023

Real-time emotion recognition based on EEG signals using a hybrid batch-stream architecture.

Mohammad Hosein Houshmand1, Boshra Pishgoo1

  • 1K N Toosi University of Technology Faculty of Computer Engineering Tehran, Tehran, Iran.

Neural Networks : the Official Journal of the International Neural Network Society
|May 19, 2026
PubMed
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This study introduces a hybrid brain-computer interface (BCI) framework for emotion recognition, balancing accuracy and speed. It intelligently switches between batch and streaming modes, improving real-time performance.

Area of Science:

  • Neuroscience
  • Computer Science
  • Artificial Intelligence

Background:

  • Emotion recognition using Brain-Computer Interface (BCI) systems is gaining traction.
  • Current BCI models face a trade-off between offline (batch) processing accuracy and online (streaming) processing speed.
  • Batch processing offers high accuracy but is slow; streaming offers real-time performance but lower accuracy.

Purpose of the Study:

  • To develop a hybrid framework integrating batch and streaming BCI paradigms.
  • To address the accuracy-speed trade-off in real-time emotion recognition.
  • To introduce a probabilistic intelligent switching mechanism for dynamic output selection.

Main Methods:

  • Proposed a hybrid batch-streaming framework for BCI emotion recognition.
Keywords:
Brain–computer interfaceConcept driftEmotion recognitionHybrid batch-stream architectureReal-time EEG signal processingTime-Accuracy Trade-off

Related Experiment Videos

Last Updated: May 21, 2026

Artificial Intelligence-Based System for Detecting Attention Levels in Students
06:37

Artificial Intelligence-Based System for Detecting Attention Levels in Students

Published on: December 15, 2023

  • Implemented a probabilistic intelligent switching mechanism based on historical streaming module performance.
  • Evaluated the framework on DEAP, AMIGOS, and SEED benchmark datasets.

    • Main Results:

    • Achieved classification accuracies of 85% (DEAP), 94% (AMIGOS), and 74% (SEED).
    • Demonstrated effective balancing of classification accuracy and computational efficiency.
    • Investigated the performance of the switching mechanism and system components against concept drift.

    Conclusions:

    • The proposed hybrid framework successfully mitigates the limitations of individual batch and streaming BCI approaches.
    • The intelligent switching mechanism dynamically optimizes performance based on reliability.
    • This hybrid approach is expected to be influential in future feedback-based BCI systems.