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The role of ECoG magnitude and phase in decoding position, velocity, and acceleration during continuous motor behavior.

Hammer J, Fischer J, Ruescher J, Schulze-Bonhage A, Aertsen A, Ball T - Front Neurosci (2013)

Bottom Line: The decoding accuracy based only on phase information was substantially (at least 2 fold) and significantly higher than that based only on magnitudes for position, velocity and acceleration.There was also no indication of additional magnitude-based mechanisms encoding movement information in the LFC range.Thus, our study contributes to elucidating the nature of the informative LFC of motor cortical population activity and may hence contribute to improve decoding strategies and BMI performance.

View Article: PubMed Central - PubMed

Affiliation: Bernstein Center Freiburg, University of Freiburg Freiburg, Germany.

ABSTRACT
In neuronal population signals, including the electroencephalogram (EEG) and electrocorticogram (ECoG), the low-frequency component (LFC) is particularly informative about motor behavior and can be used for decoding movement parameters for brain-machine interface (BMI) applications. An idea previously expressed, but as of yet not quantitatively tested, is that it is the LFC phase that is the main source of decodable information. To test this issue, we analyzed human ECoG recorded during a game-like, one-dimensional, continuous motor task with a novel decoding method suitable for unfolding magnitude and phase explicitly into a complex-valued, time-frequency signal representation, enabling quantification of the decodable information within the temporal, spatial and frequency domains and allowing disambiguation of the phase contribution from that of the spectral magnitude. The decoding accuracy based only on phase information was substantially (at least 2 fold) and significantly higher than that based only on magnitudes for position, velocity and acceleration. The frequency profile of movement-related information in the ECoG data matched well with the frequency profile expected when assuming a close time-domain correlate of movement velocity in the ECoG, e.g., a (noisy) "copy" of hand velocity. No such match was observed with the frequency profiles expected when assuming a copy of either hand position or acceleration. There was also no indication of additional magnitude-based mechanisms encoding movement information in the LFC range. Thus, our study contributes to elucidating the nature of the informative LFC of motor cortical population activity and may hence contribute to improve decoding strategies and BMI performance.

No MeSH data available.


Overview of the data processing and decoding algorithms (DAlg) used in the present study. Left side, decoding using as predictors the time domain LFC of the ECoG (DAlg1) or the time-resolved relative power within a selected frequency band (DAlg2). Right side, decoding in a complex-valued, time-frequency domain with the ECoG predictors having the phase and/or magnitude information (DAlg3–5).
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Figure 3: Overview of the data processing and decoding algorithms (DAlg) used in the present study. Left side, decoding using as predictors the time domain LFC of the ECoG (DAlg1) or the time-resolved relative power within a selected frequency band (DAlg2). Right side, decoding in a complex-valued, time-frequency domain with the ECoG predictors having the phase and/or magnitude information (DAlg3–5).

Mentions: Here we propose to use ECoG signal representations in a complex-valued time-frequency domain [for example by time-resolved Fourier transformation (FT)] to unfold the phase and magnitude values of the signal (see Figure 1). Importantly, we make a clear distinction between the amplitude of the LFC (the value of the low-pass-filtered ECoG signal oscillations in the time domain) and the magnitude of the Fourier descriptors at a given time and frequency (the absolute value of the complex numbers representing the FT in the time-frequency domain). An overview of the decoding algorithms is given in Figure 3.


The role of ECoG magnitude and phase in decoding position, velocity, and acceleration during continuous motor behavior.

Hammer J, Fischer J, Ruescher J, Schulze-Bonhage A, Aertsen A, Ball T - Front Neurosci (2013)

Overview of the data processing and decoding algorithms (DAlg) used in the present study. Left side, decoding using as predictors the time domain LFC of the ECoG (DAlg1) or the time-resolved relative power within a selected frequency band (DAlg2). Right side, decoding in a complex-valued, time-frequency domain with the ECoG predictors having the phase and/or magnitude information (DAlg3–5).
© Copyright Policy - open-access
Related In: Results  -  Collection

License
Show All Figures
getmorefigures.php?uid=PMC3814578&req=5

Figure 3: Overview of the data processing and decoding algorithms (DAlg) used in the present study. Left side, decoding using as predictors the time domain LFC of the ECoG (DAlg1) or the time-resolved relative power within a selected frequency band (DAlg2). Right side, decoding in a complex-valued, time-frequency domain with the ECoG predictors having the phase and/or magnitude information (DAlg3–5).
Mentions: Here we propose to use ECoG signal representations in a complex-valued time-frequency domain [for example by time-resolved Fourier transformation (FT)] to unfold the phase and magnitude values of the signal (see Figure 1). Importantly, we make a clear distinction between the amplitude of the LFC (the value of the low-pass-filtered ECoG signal oscillations in the time domain) and the magnitude of the Fourier descriptors at a given time and frequency (the absolute value of the complex numbers representing the FT in the time-frequency domain). An overview of the decoding algorithms is given in Figure 3.

Bottom Line: The decoding accuracy based only on phase information was substantially (at least 2 fold) and significantly higher than that based only on magnitudes for position, velocity and acceleration.There was also no indication of additional magnitude-based mechanisms encoding movement information in the LFC range.Thus, our study contributes to elucidating the nature of the informative LFC of motor cortical population activity and may hence contribute to improve decoding strategies and BMI performance.

View Article: PubMed Central - PubMed

Affiliation: Bernstein Center Freiburg, University of Freiburg Freiburg, Germany.

ABSTRACT
In neuronal population signals, including the electroencephalogram (EEG) and electrocorticogram (ECoG), the low-frequency component (LFC) is particularly informative about motor behavior and can be used for decoding movement parameters for brain-machine interface (BMI) applications. An idea previously expressed, but as of yet not quantitatively tested, is that it is the LFC phase that is the main source of decodable information. To test this issue, we analyzed human ECoG recorded during a game-like, one-dimensional, continuous motor task with a novel decoding method suitable for unfolding magnitude and phase explicitly into a complex-valued, time-frequency signal representation, enabling quantification of the decodable information within the temporal, spatial and frequency domains and allowing disambiguation of the phase contribution from that of the spectral magnitude. The decoding accuracy based only on phase information was substantially (at least 2 fold) and significantly higher than that based only on magnitudes for position, velocity and acceleration. The frequency profile of movement-related information in the ECoG data matched well with the frequency profile expected when assuming a close time-domain correlate of movement velocity in the ECoG, e.g., a (noisy) "copy" of hand velocity. No such match was observed with the frequency profiles expected when assuming a copy of either hand position or acceleration. There was also no indication of additional magnitude-based mechanisms encoding movement information in the LFC range. Thus, our study contributes to elucidating the nature of the informative LFC of motor cortical population activity and may hence contribute to improve decoding strategies and BMI performance.

No MeSH data available.