Table 3.

AIC analysis results for different models describing how absolute force relates to LFP beta activity during sustained contraction

Model	Predictor	Effects	df	AIC	Δi(AIC)	wi(AIC)	$\frac{w_i\left(AIC\right)}{w_3\left(AIC\right)}$
1	Beta + finger	Beta: k = 0.039 ± 0.005, p < 0.001; finger: k = 2.626 ± 0.203, p < 0.001	5	7183.41	29.76	2.081e-7	p < 0.001
2	Beta + finger + beta*finger	Beta: k = 0.027 ± 0.006, p < 0.001; finger: k = 2.593 ± 0.203, p < 0.001; beta*finger: k = 0.033 ± 0.010, p = 0.0017	6	7175.52	21.87	1.076e-5	p < 0.001
3	Beta*MVC + finger	Beta*MVC: k = 0.013 ± 0.001, p < 0.001; finger: k = 2.609 ± 0. 202, p < 0.001	5	7153.65	0	0.604	—
4	Beta*MVC + finger + beta*finger	Beta*MVC: k = 0.013 ± 0.001, p < 0.001; finger: k = 2.609 ± 0. 202, p < 0.001; beta*finger: k = 0.0056 ± 0.0037, p = 0.418	6	7154.49	0.84	0.396	0.656

Model 3 was the model with the minimal AIC value and the extra interaction term in Model 4 did not further increase the prediction power of the model. The Akaike weight (w_i(AIC)) and the relative Akaike weight $\frac{w_i\left(AIC\right)}{w_3\left(AIC\right)}$ showed that Model 3 had the highest probability of being the best model.