Non-stationary distributed source approximation: an alternative to improve localization procedures

Abstract

Localization of the generators of the scalp measured electrical activity is particularly difficult when a large number of brain regions are simultaneously active. In this study, we describe an approach to automatically isolate scalp potential maps, which are simple enough to expect reasonable results after applying a distributed source localization procedure. The isolation technique is based on the time-frequency decomposition of the scalp-measured data by means of a time-frequency representation. The basic rationale behind the approach is that neural generators synchronize during short time periods over given frequency bands for the codification of information and its transmission. Consequently potential patterns specific for certain time-frequency pairs should be simpler than those appearing at single times but for all frequencies. The method generalizes the FFT approximation to the case of distributed source models with non-stationary time behavior. In summary, the non-stationary distributed source approximation aims to facilitate the localization of distributed source patterns acting at specific time and frequencies for non-stationary data such as epileptic seizures and single trial event related potentials. The merits of this approach are illustrated here in the analysis of synthetic data as well as in the localization of the epileptogenic area at seizure onset in patients. It is shown that time and frequency at seizure onset can be precisely detected in the time-frequency domain and those localization results are stable over seizures. The results suggest that the method could also be applied to localize generators in single trial evoked responses or spontaneous activity.

Figure 1

Simulated sources positions, time courses, frequency spectrum and time‐frequency behavior. The tip of each arrow marks the position of the source on the sagittal slices of an averaged brain The arrows link the sources to their corresponding spectra (shown at the left), time course (top) and time‐frequency coefficients.

Figure 2

A: Time‐frequency energy. B: Scalp Map simplicity test for the data generated by the combination of the three sources shown in Figure 1.

Figure 3

Source localization obtained using ELECTRA for the three time‐frequency pairs selected according to Figure 2. A: Localization for t = 40, f = 0.1; (B) for t = 80; f = 0.1; (C) for t = 40; f = 0.4. Maxima are encircled to facilitate discrimination given the focalization of the reconstructed sources.

Figure 4

A: Scalp Map simplicity test and time‐frequency energy for the seizure. B: A typical seizure for the patient. Note that the time‐frequency plots are slightly shifted (64 frames) with respect to the seizure plot due to the use of a sliding window in the computation of the time‐frequency representation. Despite the shift, spikes and seizure onset are clearly detected by the time‐frequency energy plot. The arrows mark the time of seizure onset in the time‐frequency energy (A) and the EEG (B).

Figure 5

Idem to Figure 4 for a second seizure of the patient, i.e., (A) Scalp Map simplicity test and time‐frequency energy for the seizure shown in (B). Note that the pattern at seizure onset on the time‐frequency energy plot is nearly identical to that of a different seizure shown in Figure 4A.

Figure 6

ELECTRAs localization results using the source isolation approach at time‐frequency pair marked with an arrow in the time‐frequency energy plot shown in Figure 4A and identified as time of seizure onset. Lighter colors indicate strongest electrical activation that is restricted for this time‐frequency pair to the left frontal lobe.

Figure 7

ELECTRAs localization results using the source isolation approach at time‐frequency pair marked with an arrow in the time‐frequency energy plot shown in Figure 5A and identified as time of seizure onset. Note that except for the intensity of the activity (see scale) the localization results are identical to.

Figure 8

ELECTRA solution for the raw EEG data at the time of seizure onset marked with an arrow in Figure 4B. Note the more sparse character of the solution when compared with the solution shown in Figures 6 and 7 with bilateral activation in frontal lobes and other cortical sites.