Table 3.
Regression results for the full model
| Variable | Coefficient | SE | t Statistic | Probability |
|---|---|---|---|---|
| Intercept (state EC) | 59.56% | 1.17% | 50.83 | <1e-5 |
| Power | 12.23% | 1.44% | 8.47 | <1e-5 |
| Status EO | −1.42% | 1.58% | −0.90 | 0.37 |
| Status task | −0.94% | 1.52% | −0.62 | 0.54 |
| SNR | 0.47% | 0.11% | 4.40 | 0.00001 |
| Power:StatusEO | 1.90% | 1.99% | 0.96 | 0.34 |
| Power:StatusTask | −2.58% | 1.92% | −1.34 | 0.18 |
| Power:SNR | −0.49% | 0.09% | −5.29 | <1e-5 |
| StatusEO:SNR | 0.11% | 0.14% | 0.79 | 0.43 |
| StatusTask:SNR | 0.24% | 0.15% | 1.53 | 0.13 |
| Power:StatusEO:SNR | −0.09% | 0.13% | −0.67 | 0.50 |
| Power:StatusTask:SNR | 0.07% | 0.13% | 0.55 | 0.59 |
Coefficients denote the change in EEG phase prediction accuracy as a percentage of 180°. For example, a +10% value would indicate that phase prediction accuracy increased by 10% of 180°, namely, by 18°. Significant predictors (p < 0.05) are shown in boldface.