Phase-interpolated averaging for analyzing electroencephalography and magnetoencephalography epochs

Area of Science:

• Neuroscience
• Biophysics
• Signal Processing

Background:

• Electroencephalography (EEG) and magnetoencephalography (MEG) signal analysis relies on averaging epochs to reveal characteristic waveforms.
• Current models for EEG/MEG generation often assume evoked, phase-modulated, or amplitude-modulated activities occur in isolation or concurrently.
• Investigating the precise interplay of these distinct neural activities requires advanced analytical techniques.

Purpose of the Study:

• To introduce and validate a novel two-stage stimulus-locked averaging method, termed phase-interpolated averaging.
• To differentiate and quantify simultaneously occurring evoked, amplitude-modulated, and phase-modulated neural activities.
• To provide a more accurate understanding of the underlying neural processes generating EEG and MEG signals.

Main Methods:

• Developed a two-stage stimulus-locked averaging technique: phase-interpolated averaging.
• Stage 1: Interpolated virtual EEG/MEG epochs onto a phase-grid from measured epochs.
• Stage 2: Applied discrete Fourier transform to virtual epochs; analyzed Fourier terms and interpolation condition number.

Main Results:

• Simulations demonstrated that the zeroth Fourier term isolates evoked activity, the first term isolates amplitude-modulated activity, and interpolation condition number reflects phase-modulated activity.
• Preliminary EEG analysis indicated that evoked activity is often smaller than conventionally estimated.
• Evoked and phase-modulated activities were found to coexist, with amplitude-modulated activity occasionally associating with both.

Conclusions:

• Phase-interpolated averaging successfully separates and quantifies evoked, amplitude-modulated, and phase-modulated neural activities.
• This method reveals that these three types of neural activity can coexist, challenging previous assumptions of isolated occurrence.
• The findings offer a more nuanced view of neural signal generation and provide a powerful tool for EEG/MEG research.