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Deep learning-based electroencephalic decoding of the phase-lagged transcranial alternating current stimulation.

Jeongwook Kwon1, Byoung-Kyong Min1,2

  • 1Department of Brain and Cognitive Engineering, Korea University, Seoul, Republic of Korea.

Frontiers in Human Neuroscience
|July 7, 2025
PubMed
Summary

Electroencephalography (EEG) signals can decode cross-frequency coupled alternating current stimulation (CFC-tACS) types. This brain stimulation technique influences cognitive control, paving the way for advanced brain-machine interfaces.

Keywords:
brain stimulationcognitive systemdeep-learningelectroencephalographytop-down processingtranscranial alternating current stimulation

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Area of Science:

  • Neuroscience
  • Cognitive Science
  • Biomedical Engineering

Background:

  • Non-invasive brain stimulation techniques like cross-frequency coupled alternating current stimulation (CFC-tACS) aim to enhance cognitive functions.
  • Understanding the brain's response to different stimulation parameters is crucial for optimizing these techniques.
  • Electroencephalography (EEG) is a widely used tool for measuring brain activity.

Purpose of the Study:

  • To investigate if task-based EEG signals can decode different phase-lag types of online CFC-tACS.
  • To explore the potential of using EEG to monitor and decode brain states during neuromodulation.
  • To assess the feasibility of closed-loop brain-machine interfacing (BMI) systems incorporating neuromodulation.

Main Methods:

  • EEG data were collected from 21 healthy participants performing a modified Sternberg task.
  • CFC-tACS with 45° or 180° phase lags between the central executive network and default mode network was applied online.
  • A modified EEGNet model was trained on EEG signals to decode the applied CFC-tACS conditions.

Main Results:

  • The EEGNet model achieved an 81.73% decoding accuracy using parietal EEG signals.
  • Feature maps indicated that the model primarily relied on EEG beta activity in the parietal region.
  • This suggests that tACS phase-lag type influences top-down cognitive control, reflected in beta-band activity.

Conclusions:

  • Task-based EEG signals are capable of decoding online CFC-tACS stimulation types.
  • Task-related EEG spectral characteristics, particularly beta activity, can serve as indicators of neuromodulatory effects.
  • This research supports the advancement of communicative strategies in closed-loop BMI-neuromodulation systems.