With its default parameters and our epochs of -200 to 800 ms, this algorithm will start by finding the difference in amplitude between the most positive and most negative points (the peak-to-peak voltage) between -200 and 0 ms (a 200-ms window). The first is called the moving window peak-to-peak algorithm, and it’s illustrated in Figure 8.3. In this exercise, we’ll look at two artifact detection algorithms that take this into account and work much better for blink detection. We can improve blink detection by taking into account the fact that blinks are relatively short-term changes in voltage that typically last ~200 ms. As a result, it ends up flagging trials that don’t contain blinks and misses some of the true blinks. It treats any large voltage as a blink, not taking into account the shape of the blink waveforms. The simple voltage threshold algorithm that we have used so far in this chapter is an overly simplistic way of detecting blinks.

Specifically, you can use an artifact detection algorithm that is better designed to isolate blinks from other kinds of voltage deflections. However, there is something you can change that can improve both the hit rate and the false alarm rate. If you change the threshold to make one better, this inevitably makes the other one worse. In a previous exercise, we saw that changing the threshold for rejection affects both the hit rate (the proportion of artifacts detected) and the false alarm rate (the proportion of non-artifact trials that are flagged for rejection).

Blink psychology book