Objective detection of auditory steady-state evoked potentials based on mutual information.

Objective: Recently, we developed a metric to objectively detect human auditory evoked potentials based on the mutual information (MI) between neural responses and stimulus spectrograms. Here, the MI algorithm is evaluated further for validity in testing the auditory steady-state response (ASSR), a sustained potential used in objective audiometry.
Design: MI was computed between spectrograms of ASSRs and their evoking stimuli to quantify the shared time-frequency information between neuroelectric activity and stimulus acoustics. MI was compared against two traditional ASSR detection metrics: F-test and magnitude-squared coherence (MSC).
Study Sample: Using an empirically derived threshold (⊖MI=1.45), MI was applied as a binary classifier to distinguish actual biological responses recorded in human participants (n=11) from sham recordings, containing only EEG noise (i.e., non-stimulus-control condition).
Results: MI achieved high overall accuracy (>90%) in identifying true ASSRs from sham recordings, with true positive/true negative rates of 82/100%. During online averaging, comparison with two other indices (F-test, MSC) indicated that MI could detect ASSRs in roughly half the number of trials (i.e., ∼400 sweeps) as the MSC and performed comparably to the F-test, but showed slightly better signal detection performance.
Conclusions: MI provides an alternative, more flexible metric for efficient and automated ASSR detection.