Time-frequency analysis of movement-related spectral power in EEG during repetitive movements: a comparison of methods

Abstract

During dynamic voluntary movements, power in the alpha- and beta-bands resulting from synchronized neuronal activity is modulated in a manner that is time-locked to movement onset. These signals can be readily recorded from the scalp surface using electroencephalography. Abnormalities in the magnitude and timing of these oscillations are present in a wide variety of movement disorders including Parkinson's disease and dystonia. Most studies have examined movement-related oscillations in the context of single discrete movements, yet marked impairments are often seen during the performance of repetitive movements. For this reason, there is considerable need for analysis methods that can resolve the modulation of these oscillations in both the frequency and time domains. Presently, there is little consensus on which is the most appropriate method for this purpose. In this paper, a comparison of commonly used time-frequency methods is presented for the analysis of movement-related power in the alpha- and beta-bands during repetitive movements. The same principles hold, however, for any form of repetitive or rhythmic input-output processes in the brain. In particular, methods based on band-pass filtering, the short-time Fourier transform (STFT), continuous wavelet transform and reduced interference distributions are discussed. The relative merits and limitations in terms of spectral or temporal resolution of each method are shown with the use of simulated and experimental data. It is shown that the STFT provides the best compromise between spectral and temporal resolution and thus is the most appropriate approach for the analysis and interpretation of repetitive movement-related oscillations in health and disease.

Figure 1

Examples of simulated signals used to determine temporal and spectral resolution. (a) spectral resolution test signal consisting of two sinusoids at frequencies of 20 and 24 Hz. (b) temporal resolution test signal showing 20 Hz bursts separated by intervals of inactivity of 39 ms duration (i.e., 10 samples). (c) test signal simulating repetitive movement at 2 Hz for qualitative analysis of TFR consisting of 0.25 s bursts of 10 and 20 Hz activity followed by 0.25 s of inactivity.

Figure 2

Plot showing criteria for spectral resolution. f₁ and f₂ are resolved if power between the two frequencies B is less than half that of the lower of A or C.

Figure 3

Examples of spectra for a pair of frequencies at 20 and 26 Hz at time T = 0 s (a) band-pass filter method (elliptic filter); (b) band-pass filter method (Butterworth); (c) STFT-128 (0.5 s window); (d) STFT-64 (0.25 s window); (e) CWT; (f) RID.

Figure 4

Plots showing time course of a signal with 31.3 ms (8 samples) intervals between activity bursts at f = 10 Hz: (a) band-pass filter method (elliptic filter); (b) band-pass filter method (Butterworth); (c) STFT-128 (0.5 s window); (d) STFT-64 (0.25 s window); (e) CWT; (f) RID.

Figure 5

Plots showing time-frequency energy distribution of each method for signal with frequency components at 10 and 20 Hz comprising 0.25 s repeated bursts of activity followed by a similar period of inactivity. (a) band-pass filter method (elliptic filter); (b) band-pass filter method (Butterworth); (c) STFT-128 (0.5 s window); (d) STFT-64 (0.25 s window); (e) CWT (Solid black line = Cone of Influence); (f) RID.

Figure 6

Plots showing time-frequency energy distribution of each method for experimental EEG recorded over the primary motor cortex during repetitive movements of the index finger at a frequency of 2 Hz. (a) band-pass filter method (elliptic filter); (b) band-pass filter method (Butterworth); (c) STFT-128 (0.5 s window); (d) STFT-64 (0.25 s window); (e) CWT (Solid black line = Cone of Influence); (f) RID.