Brain–Computer Interfaces - Index of

Digital Signal Processing and Machine Learning 325
Fig. 9 A ROC curve extracted from [52]. The circled point on the curve marks the point of equal
balance of sensitivity and selectivity
where parameter θ represents the threshold, TP(θ), FN(θ), TN(θ), and FP(θ) are
the numbers of true positives, false negatives, true negatives, and false positives
respectively, which are determined by θ. All these values are counted in samples.
Figure 8 illustrates the above parameters. The corresponding ROC curve is
presented in Fig. 9.
7 An Example of BCI Applications: A P300 BCI Speller
The previous sections described some important signal processing and machine
learning techniques used in BCIs. This section presents an example with a P300
BCI speller. The data were collected from 10 subjects using a P300-based speller
paradigm as described in [54]. In the experiment, a 6-by-6 matrix that includes characters
such as letters and numbers is presented to the user on a computer screen
(Fig. 10). The user then focuses on a character while each row or column of the
matrix flashes in a random order. Twelve flashes make up one run that covers all
the rows and columns of the matrix. Two of these twelve flashes in each run contain
the desired symbol, which produces a P300. The task of the speller is to identify the
user’s desired character based on the EEG data collected in the these flashes.
The dataset contains training data and testing data collected from 10 subjects. The
system is trained separately for each user using a common phrase with 41 characters
“THE QUICK BROWN FOX JUMPS OVER LAZY DOG 246138 579”. The same
phrase is also used in testing data collection with random word order.