Brain–Computer Interfaces - Index of

232 J.E. Huggins
correctly detected by the BCI. So, if the subject performed 25 actions, but only 19
of them were detected by the BCI, the hit percentage would be 76%. The false activation
percentage is the percentage of the detections the BCI produced that were
false (the calculation is the same as that of the false discovery rate [32] usedin
statistics). So, if the BCI produced 20 activations, but one was incorrect, the false
activation percentage would be 5%. Note that the calculation of the false activation
percentage is different from the typical calculation for a false positive percentage,
where the denominator is the number of data samples that should be classified as
no-activation. This large denominator can result in extremely low false positive
rates, despite performance that has an unacceptable amount of false positives from
the user’s perspective. By using the number of detections as the denominator, the
false activation rate should better reflect the user’s perception of BCI performance.
Finally, the HF-difference is the simple difference between the hit percentage and
the false activation percentage. So, for the example where the subject performed 25
actions, the BCI produced 20 detections, and 19 of the detections corresponded
to the actions, we have 76% hits, 5% false activations, and an HF-difference
of 71%.
Detection Results
The first reports from the UM-DBI project of the practicality of ECoG for BCI
operation used a cross-correlation template matching method (CCTM) in off-line
analysis and occurred in the late 1990s [3, 26, 33, 34]. CCTM detected eventrelated
potentials with accuracy greater than 90% and false activation rates less
than 10% for 5 of 17 subjects (giving HF-differences above 90) using an acceptance
window of 1 s before to 0.25 s after each action [27]. However, the CCTM
method had an unacceptable delay due to the use of a template extending beyond
the trigger. A partnership with the BCI group at the Technical University of Graz,
in Austria led to further off-line analyses. An adaptive autoregressive method for
detecting ERD/ERS was tested on ECoG from 3 subjects with each subject performing
the actions finger extension, pinch, tongue protrusion and lip protrusion
in a separate dataset for a total of 12 subject/action combinations. The adaptive
autoregressive method produced HF-differences above 90% for all three subjects
and for 7 of the 12 subject/action combinations [35]. A wavelet packet analysis
method found HF-differences above 90% for 8 of 21 subject/action combinations
with perfect detection (HF-difference = 100) for 4 subject/action combinations with
an acceptance window of 0.25 s before and 1 s after each action [30]. Model-based
detection method development continued at the University of Michigan, resulting in
two versions of a quadratic detector based on a two-covariance model [36, 37]. The
basic quadratic detector produced HF-differences greater than 90% for 5 of 20 subject/action
combinations from 4 of 10 subjects with an acceptance window of 0.5 s
before and 1 s after each action [37]. The changepoint quadratic detector produced
HF-differences greater than 90% for 7 of 20 subject/action combinations from 5 of
10 subjects with the same acceptance window [37].