Alternatives to Sine Carrier in Auditory BCI: Exploring Machine Learning Strategies for Assessing Modulation Detectability in EEG

Abstract

In this paper, the use of non-sinusoidal amplitude-modulated stimuli is assessed for Brain-Computer Interfaces (BCIs) based on Steady-State Auditory Evoked Potentials (SSAEPs). Three different stimuli are compared to the frequently used 1-kHz pure tone: Brownian noise, cicada song and cat's purr. While these alternative sounds are intended to be more pleasant for listeners, they may impact the detectability of the modulation frequency in ElectroEncephaloGraphic (EEG) signals. Stimuli are equalized in loudness using an Head And Torso Simulator (HATS). The experiment is conducted at two loudness levels (50 and 56 phons), with 24 subjects participating in each condition. Hearing capacity is assessed prior to the experiment, using an audiometry test and questionnaires. For each stimulus, detection is performed by using 10 different classifiers: a linear discriminant analysis, deep learning networks and Riemannian classifiers including tangent space-based algorithms. These latter consistently outperformed alternative approaches. Pure tones provide the highest accuracy of detection (above 83%), whereas cicada song only achieve 60%. Classification using the proposed models fails for Brownian noise and cat's purr, with accuracy at a chance level. Additionally, increasing the loudness of the stimuli does not enhance the detectability of the modulation frequency for any stimulus. Amplitude modulation, frequency content and temporal characteristics of stimuli are further analyzed for explanation. These findings provide practical recommendations for auditory BCI classification and audio stimuli design.