Source code for medusa.bci.metrics

import numpy as np


[docs]def itr(accuracy, n_commands, selections_per_min): """ Calculates the information transfer rate (ITR) in bits/min. This metric is widely used to assess the performance of a BCI. However, it has serious limitations for online systems: (i) it assumes that the probability of selecting all symbols is the same; (ii) the system is memoryless; (iii) a synchronous paradigm is used; (iv) users cannot correct mistakes. Parameters --------------- accuracy: float or list of floats Accuracy of the system (between 0 and 1). n_commands: int Number of possible commands to be selected. selections_per_min: float Number of selections that have been performed in a minute. Returns ---------------- itr: float or list of floats ITR corresponding to each accuracy in bits per min (bpm). """ # Special cases if accuracy == 0: # If accuracy is 0%, then ITR is 0 bpm itr = 0 elif accuracy == 1: # If accuracy is 100%, then we take the mathematical limit itr = np.log2(n_commands) * selections_per_min else: # Otherwise: common ITR formula itr = (np.log2(n_commands) + accuracy * np.log2(accuracy) + (1 - accuracy) * np.log2((1 - accuracy) / (n_commands - 1))) \ * selections_per_min return itr