Table 8 Dataset 2 short window (\(w=0.5\,\text{s}\)) features grand average (across all participants) accuracy and F1 score of sLDA classification, shown by types of features and types of TFRs calculated with Shannon entropy
From: Detection of motor imagery based on short-term entropy of time–frequency representations
Accuracy (%) | ||||||||
|---|---|---|---|---|---|---|---|---|
TFR | tfrsp | tfrrsp | tfrgabor | tfrrgab | tfrpwv | tfrrpwv | tfrspwv | tfrrspwv |
Feature type | Entropy | Entropy | Entropy | Entropy | Entropy | Entropy | Entropy | Entropy |
EE vs EF | 52.84 | 53.09 | 52.65 | 52.85 | 52.15 | 53.05 | 51.69 | 51.79 |
EE vs base | 63.36 | 59.92 | 70.53 | 62.92 | 84.45 | 94.82 | 70.01 | 61.77 |
EF vs base | 62.66 | 60.2 | 69.49 | 63.62 | 84.3 | 95.27 | 69.02 | 61.82 |
F1 score (%) | ||||||||
|---|---|---|---|---|---|---|---|---|
EE vs EF | 51.76 | 52.38 | 51.79 | 52.73 | 53.25 | 53.12 | 51.86 | 52.13 |
EE vs base | 62.2 | 56.69 | 69.67 | 61.34 | 83.39 | 94.61 | 69.04 | 58.03 |
EF vs base | 60.9 | 57.02 | 68.69 | 61.45 | 83.11 | 95.04 | 68.23 | 58.28 |