FFT Data Analysis

Grand Average Data (n = 24 participants).

Individual Data (n = 24 participants).

ANOVA Analysis of Individual Mean Z-scores for all Duty Cycle conditions : independent variable Stimulus Zscore

Shapiro-Wilk Test for Normality by Duty Cycle
DutyCycle W p_value
25.W 25 0.9504829 0.2775412
50.W 50 0.9124321 0.0398379
75.W 75 0.9684628 0.6291927
100.W 100 0.9511739 0.2872247
Levene’s Test for Homogeneity of Variance
Df F.value Pr..F.
3 0.6434267 0.5890433
92 NA NA
Repeated Measures ANOVA Table for Mean Z-scores by Duty Cycle, with Eta Squared
numDF denDF F-value p-value Eta Squared
(Intercept) 1 71 23.457634 0.0000072 NA
stimcochZscore 1 71 4.464571 0.0381215 0.0591612

ANOVA Analysis of Individual Mean Z-scores for all Duty Cycle conditions : independent variable Duty Cycle Condition.

Repeated Measures ANOVA Table for Mean Z-scores by Duty Cycle, with Eta Squared
numDF denDF F-value p-value Eta Squared
(Intercept) 1 69 23.457634 0.0000075 NA
DutyCycle 3 69 1.649764 0.1859290 0.0669282

T-test against stim Zscore

T-test Results and Cohen’s d by Duty Cycle
DutyCycle t_statistic p_value mean_diff cohen_d
t 25 7.338818 0.0000002 0.7080176 1.4980299
t1 50 4.599911 0.0001262 0.4885974 0.9389529
t2 75 5.227172 0.0000266 0.4567302 1.0669921
t3 100 3.875900 0.0007652 0.3769385 0.7911648

Summary :

  • Normality: The meanZscore is normally distributed in the 25%, 75%, and 100% DutyCycle groups, but not in the 50% group.
  • Homogeneity of Variance: The assumption of homogeneity of variances is satisfied.
  • Repeated measures ANOVA: Significant effect of stim Zscore with medium effect size on meanZscore, but no post hoc possible (continuous variable). When change the independent variable to categorical DutyCycle, NO significant effect on meanZscore (p = 0.866) but medium effect size.
  • t-tests against stimulus z-score : For all Duty Cycle conditions, the mean Z-scores are significantly different from the stimulus Z-scores, with large effect sizes for all conditions.

Stability Data Analysis.

Statistical Analysis

Friedman test

Shapiro-Wilk Test for Normality by Duty Cycle
DutyCycle W p_value
25.W 25 0.8911232 0.0140223
50.W 50 0.8554714 0.0027667
75.W 75 0.8668455 0.0045657
100.W 100 0.8522073 0.0024030
Levene’s Test for Homogeneity of Variance
Df F.value Pr..F.
3 0.5530682 0.6473648
92 NA NA

Friedman with Duty Cycle as independent variable

Friedman Test Results for Mean Z-scores by Duty Cycle
Test Chi_Squared df p_value Kendall_W
df Friedman 8.85 3 0.0313531 0.1229167
Post-hoc Nemenyi Test Results
Comparison 25 50 75
50 50 1.0000000 NA NA
75 75 0.0496762 0.0496762 NA
100 100 0.7457724 0.7457724 0.3986314

Summary :

  • Normality: The R value does not follow a normal distribution in any of the duty cycle groups.
  • Homogeneity of Variance: The assumption of homogeneity of variances is satisfied
  • Friedman rank sum test: significant differences in the Rvalue distributions across the duty cycle groups, BUT small effect size !!.
  • Post-hoc analysis: significant differences between the 75% duty cycle and the 25% and 50%. Other pairwise comparisons are not significant.

Correlation between FFT and Stability Analysis of Tapping

Spearman Correlation between Stability and FFT Z-scores by Duty Cycle
DutyCycle correlation p_value
25 0.9034783 2.7e-06
50 0.8843478 2.7e-06
75 0.8782609 2.6e-06
100 0.9173913 2.5e-06 
## `geom_smooth()` using formula = 'y ~ x'

## `geom_smooth()` using formula = 'y ~ x'

Correlation between EEG FFT and TAP FFT

Spearman Correlation between EEG and Tap Z-scores by Duty Cycle
DutyCycle correlation p_value
25 0.2982609 0.1566372
50 0.1895652 0.3732832
75 0.4443478 0.0307593
100 0.2626087 0.2142305 
## `geom_smooth()` using formula = 'y ~ x'

## `geom_smooth()` using formula = 'y ~ x'

Correlation between EEG FFT and Stability

Spearman Correlation between EEG and Tap Z-scores by Duty Cycle
DutyCycle correlation p_value
25 0.1852174 0.3845155
50 0.2878261 0.1721935
75 0.3704348 0.0755451
100 0.2252174 0.2886250 
## `geom_smooth()` using formula = 'y ~ x'

## `geom_smooth()` using formula = 'y ~ x'