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Concurrent Electroencephalography Recording During Transcranial Alternating Current Stimulation tACS
Published on: January 22, 2016
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A computationally efficient method for the attenuation of alternating current stimulation artifacts in
Roberto Guarnieri1, Alfredo Brancucci2, Anita D'Anselmo2
1Research Center for Motor Control and Neuroplasticity, KU Leuven, Tervuursevest 101, 3001, Leuven, Belgium.
Journal of Neural Engineering
|July 28, 2020
Summary
We developed an adaptive spatial filtering method called alternating current regression (AC-REG) to effectively remove transcranial alternating current stimulation (tACS) artifacts from electroencephalography (EEG) data for closed-loop neuromodulation.
Area of Science:
- Neuroscience
- Biomedical Engineering
- Signal Processing
Background:
- Noninvasive closed-loop neuromodulation using electroencephalography (EEG) and transcranial alternating current stimulation (tACS) shows promise for neurological disorder treatment.
- A significant challenge is the effective attenuation of tACS artifacts in EEG data, hindering real-time applications.
- Computationally efficient methods are crucial for advancing closed-loop neuromodulation research and clinical translation.
Purpose of the Study:
- To introduce an original, computationally efficient method for attenuating tACS artifacts in EEG data.
- To enable reliable real-time artifact removal for closed-loop neuromodulation systems.
- To improve the accuracy of neural activity recovery during tACS experiments.
Main Methods:
- Developed an adaptive spatial filtering technique named alternating current regression (AC-REG).
- AC-REG utilizes a data buffer and principal component analysis (PCA) for continuous, time-varying filter updates.
- The method is designed for real-time application, processing new data samples as they become available.
Main Results:
- AC-REG demonstrated accurate tACS artifact attenuation across a wide frequency range (1-80 Hz) in simulations.
- Real EEG data analysis showed AC-REG superior recovery of neural activity compared to Superimposition of Moving Averages (SMA) and Helfrich method (HeM).
- AC-REG achieved significantly higher correlations in power spectrum densities between tACS on/off conditions (r=0.90) versus SMA (r=0.80) and HeM (r=0.86).
Conclusions:
- The AC-REG method offers a computationally efficient solution for tACS artifact removal in EEG.
- Its adaptive nature and low complexity make it suitable for real-time, noninvasive closed-loop neuromodulation.
- AC-REG has potential applications in both research settings and clinical interventions for neurological conditions.
