Research Article
Wavelet-Based Semblance Methods to Enhance the Single-Trial Detection of Event-Related Potentials for a BCI Spelling System
Algorithm 2
Semblance-based ERP window selection by channels (SEWS-1).
| (1) | Input: given the EEG signal matrix X, with C channels and T temporal samples | | (2) | Output: the bounds of the temporal window and for each C channel | | (3) | Set the window threshold , | | (4) | for to C do | | (5) | Compute the average of responses belonging to the target class for channel c | | (6) | Compute the average of responses belonging to the nontarget class for channel c | | (7) | Compute the and using equation (2) | | (8) | Compute the semblance using equation (4) | | (9) | Compute the dot product D using equation (5) | | (10) | Compute the standard deviation of D over the scales and standardise it between 0 and 1 | | (11) | The limit is given by the first t from the left which meets | | (12) | The limit is given by the first t from the right which meets | | (13) | end for |
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