'macro' is suitable average metric for f1-score of binary MI classification.

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Since binary f1-score consider precision and recall, it does not consider TN.
Let me consider right hand class as the positive and left hand class as the negative. For this case, binary f1-score ignores left hand class.
Conversely, when the positive and negative labels are changed, the f1-score is different.
Because the binary MI classification is not a positive-negative problem, we should treat both class equally.
Therefore I suggest use macro f1-score for the binary MI classification.

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Thank you for your suggestion