Classification of affirmative and negative brain responses within an fMRI classical conditioning paradigm using Effect Mapping for feature selection

Several neuroimaging studies have provided strong evidence of the possibility to decode mental states from brain activity. Compared to strictly location-based analysis, pattern classification can reveal new information about the way cognitive, emotional, and perceptual states are encoded in patterns of brain activity. The last years have also seen the development of advanced algorithms, which markedly improved the possibility to perform pattern classification. By relying on mental state classification, brain-computer interfaces (BCIs) allow individuals who have lost the ability to communicate verbally to convey basic thoughts and emotions. The aim of our study was to discriminate between brain responses associated to affirmative and negative thinking in 10 subjects, in order to develop a BCI that could be used for basic yes/no communication. This discrimination could be achieved using a classical conditioning paradigm, i.e. associating affirmative and negative responses (the conditioned stimuli, CS), respectively associated to congruent and incongruent word-pairs, to pleasant and unpleasant emotional stimuli (the unconditioned stimuli, US) together with Effect Mapping (EM), based on Support Vector Machine (SVM). Using EM as the classifier of the affirmative and negative responses, a classification accuracy of over 90% was reached