Introduction Brain-Computer Interfaces (BCI) can be used for communication and motor restoration (Birbaumer & Cohen, 2007). To our knowledge, no BCI study looked at patients with dementia who have severe communication deficits. BCIs based on operant training could be problematic for patients with cognitive deficits. A paradigm shift from instrumental-operant learning to classical conditioning could possibly overcome this failure (Birbaumer, 2006). Recent findings demonstrated the possibility to classify cognitive and emotional states by the pattern classification of BOLD signals in both offline (Lee et al., 2010a, 2010b) and online situations (Sitaram et al., 2010). The present study aims to investigate the feasibility of an auditory classical conditioning paradigm within a fMRI based BCI setting. The paradigm is designed to condition individuals to associate positive and negative emotional stimuli as unconditioned stimuli (US) with congruent and incongruent word pairs as conditioned stimuli (CS), respectively. Our goal is to ascertain whether the brain signals pertaining to congruent and incongruent word pairs could be classified with more than chance accuracy using our fMRI support vector machine (SVM) with a view to apply for basic online yes/no communication in Alzheimer patients. Methods The paradigm consisted of one single session divided into six blocks, comprising the different phases of conditioning (habituation, acquisition, extinction). The US consisted of auditory emotional stimuli selected from the International Affective Digitized Sounds (IADS, Bradley & Lang, 1999). A segment of baby laughter represented the positive emotional stimulus and a segment of screaming represented the negative emotional stimulus. The CS, presented aurally, were congruent (e.g. ‘animal-elephant’) and incongruent (e.g. ‘animal-Germany’) word-pairs. The unconditioned and conditioned responses (UR and CR) were the changes in the BOLD signal pertaining to the CS and US, respectively. The first block consisted of a randomized presentation of 50 US and 50 CS. In the second and third blocks 25 congruent word pairs, immediately followed by the baby laughter, and 25 incongruent word pairs, immediately followed by the scream, were presented randomly. In the fourth and fifth blocks, respectively 40% and 20% of the CS were paired with the US. In the sixth block, only the CS was presented. Functional imaging was performed continuously during these blocks on 6 healthy subjects (4 females, 2 males, age 21-27) on a 3.0 T scanner (Siemens, Germany). To classify the signals corresponding to various conditions, namely congruent and incongruent word-pairs, a linear SVM (with the regularization parameter, C=1) was implemented. Classification performance from data was evaluated through 2-fold cross validation (CV). Based on the parameters of the trained SVM model, we analyzed the fMRI data with the Effect Mapping method (EM; Lee et al., 2010a, 2010b, Sitaram et al., 2010). To investigate the relative importance of different brain regions in decoding the conditioning brain states, feature vectors from the frontal cortex were used as input to build a separate SVM classifier. Results The Self Assessment Manikin (SAM) test showed that participants reported more negative valence and a higher arousal for the scream compared to the baby laughter. Classification of the BOLD signal as a response to the congruent and incongruent word pairs immediately followed by the emotional US showed above chance level performance (57-64%) on one subject, around chance level (50-56%) performance on three subjects, and below chance level (44-47%) performance on two subjects. Conclusions In this pilot study we have demonstrated an approach for conditioning the BOLD signal by repeated association of the emotional stimuli with semantic stimuli, resulting in a paradigm for basic yes/no communication. Further work includes improving the performance of the classifier by feature selection, an online implementation of the system, and its testing on patients. References: Birbaumer, N. (2006), ‘Brain-computer-interface research: coming of age’, Clinical Neurophysiology, vol. 117, pp. 479-483. Birbaumer, N. & Cohen, L. G. (2007), ‘Brain-computer interfaces: communication and restoration of movement in paralysis’, The Journal of Physiology, vol. 579, no. 3, pp. 621-636. Bradley, M. M. & Lang, P. J. (1999), ‘International Affective Digitized Sounds (IADS): Stimuli, instruction manual and affective ratings’, University of Florida, Gainesville. Sitaram R, Lee S, Ruiz S, Rana M, Veit R, Birbaumer N. Real-time support vector classification and feedback of multiple emotional brain states. Neuroimage, 2010 Aug 6. Lee, S., Halder, S., Kübler, A., Birbaumer, N., Sitaram, R. Effective functional mapping of fMRI data with support-vector machines. Hum Brain Mapp. 2010a, Jan 28. Lee, S., Ruiz, S., Caria, A., Birbaumer, N., Sitaram, R Cerebral reorganization induced by real-time fMRI feedback training of the insular cortex: a multivariate investigation. Neuroreh and Neural Rep (2010b).