Abstract
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.
Highlights
Multivariate brain decoding (MBD) might allow the inference of mental states based solely on specific brain signals’ features [1]
Considering the above-mentioned technical challenges for subject-independent affective decoding, we aimed to evaluate whether these fronto-occipital functional near-infrared spectroscopy (fNIRS) signals provide enough information to the subject-independent offline classification of affective states as a pilot investigation of the feasibility of fNIRS-based subject-independent affective decoding
Classification accuracy for the reactive task significantly exceeded chance level in “Positive vs. Negative” comparisons, with highest mean result as 64.50 ± 12.03% using 20% of features (12 channels), and in “Negative vs. Neutral” comparisons (68.25 ± 12.97%, p
Summary
Multivariate brain decoding (MBD) might allow the inference of mental states based solely on specific brain signals’ features [1]. The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript
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