Resting-Awake EEG Amplitude Modulation can Predict Performance of an fNIRS-Based Neurofeedback Task

Affective neurofeedback emerges as an innovative approach to relieve symptoms of psychiatric disorders. However, some users are unable to control these systems and are named neurofeedback illiterates. In this paper, we propose a cross-modality approach to predict affective neurofeedback illiteracy using electroencephalography (EEG) amplitude modulation measures. Thirty-one subjects were submitted to five-minute resting state protocol with EEG records, followed by functional near-infrared spectroscopy (fNIRS)-based affective neurofeedback task. EEG amplitude modulation measures were extracted from the resting block and correlated with the task performance. The gamma-m-alpha modulation from one channel presented a negative correlation with performance (r=-0.639, p=0.006). When used as input for a support vector regression model, this feature predicted performance with a mean absolute error of 13.78%. Our findings suggest this cross-modality feature is related to the affective lateralization effect, as well as with the EEG-hemodynamics coupling during resting-state. This feature might be a promising tool to predict performance and choose the best strategy for future therapeutics using affective neurofeedback.