Real-time detection of event-related brain activity

Abstract

The complexity and inter-individual variation of brain signals impedes real-time detection of events in raw signals. To convert these complex signals into results that can be readily understood, current approaches usually apply statistical methods to data from known conditions after all data have been collected. The capability to provide meaningful visualization of complex brain signals without the requirement to initially collect data from all conditions would provide a new tool, essentially a new imaging technique, that would open up new avenues for the study of brain function. Here we show that a new analysis approach, called SIGFRIED, can overcome this serious limitation of current methods. SIGFRIED can visualize brain signal changes without requiring prior data collection from all conditions. This capacity is particularly well suited to applications in which comprehensive prior data collection is impossible or impractical, such as intraoperative localization of cortical function or detection of epileptic seizures.

Figure 1. SIGFRIED vs. linear regression

The use of SIGFRIED (symbolized by one Gaussian distribution modelling the blue data points (A)) can be used to discriminate between data from the two conditions (by deriving a distance metric for a new data point Xn (C)). This procedure requires data from only one class (e.g., signals recorded during rest). The use of a traditional technique, such as linear regression (B) can also be used for discrimination (D). In contrast to the detection-based SIGFRIED approach, this procedure requires data from both conditions.

Figure 2. Visualization of brain function in subject A

A: Raw ECoG signal traces (blue and red traces on top corresponding to locations marked by blue and red crosses, respectively). B: Color-coded running averages of the SIGFRIED output for the three conditions of rest, shoulder movement, and hand movement, for each of the 32 locations in the 8×4 grid.

Figure 3. Sensorimotor and language mapping using SIGFRIED in subject B

A: SIGFRIED output for the electrode marked with a square in the brain figure on the bottom right. B: Average activations calculated using SIGFRIED. Activations are color-coded. Red corresponds to the highest SIGFRIED values and gray to the lowest values. The three brain topographies were created with identical analysis and display settings.

Figure 4. Example real-time display of the SIGFRIED module in BCI2000

The output of SIGFRIED at each location is translated into the diameter of a circle. The left display shows the real-time display (i.e., the SIGFRIED values produced at a particular point in time irrespective of condition). The two displays on the right show SIGFRIED averages for two conditions.