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Multi-person feature fusion transfer learning-based convolutional neural network for SSVEP-based collaborative BCI.

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Summary

Collaborative brain-computer interface (BCI) systems improve electroencephalography (EEG) classification accuracy by fusing multi-person data. Multi-person parallel feature connection in steady-state visually evoked potential collaborative BCI (SSVEP-cBCI) shows superior performance over single-person systems.

Keywords:
collaborative BCIconvolutional neural networkfeature fusionsteady-state visually evoked potentialtransfer learning

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Area of Science:

  • Neuroscience
  • Biomedical Engineering
  • Machine Learning

Background:

  • Conventional single-person brain-computer interface (BCI) systems suffer from low signal-to-noise ratio and individual variability.
  • Developing robust BCI systems requires addressing these limitations for improved performance and reliability.

Purpose of the Study:

  • To introduce a novel centralized steady-state visually evoked potential collaborative BCI (SSVEP-cBCI) system.
  • To develop and evaluate three distinct feature fusion methods for multi-person electroencephalography (EEG) data.
  • To compare the classification accuracy of multi-person SSVEP-cBCI against single-person systems using a transfer learning-based convolutional neural network (TL-CNN).

Main Methods:

  • An SSVEP-cBCI system was designed to integrate EEG features from multiple individuals performing the same task.
  • Three feature fusion techniques were implemented: parallel connection, serial connection, and multi-person averaging.
  • A transfer learning-based convolutional neural network (TL-CNN) was employed for EEG classification, utilizing both benchmark and collected datasets.

Main Results:

  • Multi-person SSVEP-cBCI modes significantly outperformed single-person BCI systems in classification accuracy.
  • Within a 3-second window, classification accuracy increased from 90.6% in single-person mode to 96.6% using a two-person parallel connection fusion method.
  • The TL-CNN approach demonstrated effectiveness in enhancing SSVEP-cBCI performance across different feature fusion strategies.

Conclusions:

  • Multi-person feature fusion methods combined with TL-CNN effectively enhance SSVEP-cBCI classification performance.
  • The parallel connection feature fusion method yielded the best classification results among the evaluated techniques.
  • The choice of feature fusion method can be tailored to specific application scenarios to optimize collaborative BCI performance.