Subject-independent decoding of affective states using functional near-infrared spectroscopy

Affective decoding is the inference of human emotional states using brain signal measurements. This approach is crucial to develop new therapeutic approaches for psychiatric rehabilitation, such as affective neurofeedback protocols. To reduce the training duration and optimize the clinical outputs, an ideal clinical neurofeedback could be trained using data from an independent group of volunteers before being used by new patients. Here, we investigated if this subject-independent design of affective decoding can be achieved using functional near-infrared spectroscopy (fNIRS) signals from frontal and occipital areas. For this purpose, a linear discriminant analysis classifier was first trained in a dataset (49 participants, 24.65 +/- 3.23 years) and then tested in a completely independent one (20 participants, 24.00 +/- 3.92 years). Significant balanced accuracies between classes were found for positive vs. negative (64.50 +/- 12.03%, p<0.01) and negative vs. neutral (68.25 +/- 12.97%, p<0.01) affective states discrimination during a reactive block consisting in viewing affective-loaded images. For an active block, in which volunteers were instructed to recollect personal affective experiences, significant accuracy was found for positive vs. neutral affect classification (71.25 +/- 18.02%, p<0.01). In this last case, only three fNIRS channels were enough to discriminate between neutral and positive affective states. Although more research is needed, for example focusing on better combinations of features and classifiers, our results highlight fNIRS as a possible technique for subject-independent affective decoding, reaching significant classification accuracies of emotional states using only a few but biologically relevant features.

Automated summary

The study demonstrated the possibility of inferring human emotional states using brain signal measurements, specifically with fNIRS signals and a specific classifier. By utilizing a small number of biologically relevant features, significant classification accuracies of emotional states were achieved in a subject-independent manner. Further research is needed to explore better combinations of features and classifiers for improved results in affective decoding.