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A Fast EEG Forecasting Algorithm for Phase-Locked Transcranial Electrical Stimulation of the Human Brain
Farrokh Mansouri1, Katharine Dunlop2, Peter Giacobbe3,4
1Institute of Biomaterial and Biomedical Engineering, University of TorontoToronto, ON, Canada.
Frontiers in Neuroscience
|August 5, 2017
Summary
This study introduces a new real-time algorithm for phase-locking transcranial alternating current stimulation (tACS) to brain oscillations detected by electroencephalography (EEG). The novel method improves brain stimulation accuracy by forecasting EEG signals for precise tACS delivery.
Area of Science:
- Neuroscience
- Biomedical Engineering
- Signal Processing
Background:
- Non-invasive brain stimulation efficacy is enhanced when phase-locked to neural oscillations.
- Transcranial alternating current stimulation (tACS) aims to synchronize with brain rhythms, but real-time phase-locking is challenging due to complex electroencephalography (EEG) signals.
Purpose of the Study:
- To develop and validate a novel algorithm for real-time phase-locked tACS delivery.
- To optimize EEG signal processing parameters for accurate phase prediction and stimulation control.
- To evaluate the algorithm's performance across different EEG frequency bands and individuals.
Main Methods:
- Developed a real-time algorithm extracting phase and frequency from EEG segments.
- Implemented a forecasting mechanism to predict EEG signals for tACS control.
- Investigated optimal EEG segment lengths and prediction horizons for various frequency bands.
- Tested the algorithm on EEG data from 5 healthy volunteers, quantifying performance using phase-locking values.
Main Results:
- The algorithm demonstrated consistent phase-locking performance across participants and recording sites.
- Optimal performance was achieved in the alpha band (8-13 Hz) with a phase-locking value of 0.77 ± 0.08.
- Performance improved with stable dominant frequencies within the target band.
- The algorithm showed faster and more accurate phase-locked stimulation compared to existing methods.
Conclusions:
- The developed algorithm effectively delivers real-time phase-locked tACS.
- This approach offers improved precision for modulating neural activity.
- The algorithm is suitable for future preclinical and clinical applications of phase-locked tACS.
