Master ’ s Thesis by Adnan Muhammad Niazi * ‡ s 1029789 External Supervisors

REAL-time functional magnetic resonance (rtfMRI) imaging is a relatively new technique to compute moment-to-moment changes in brain activations corresponding to a particular experimental manipulation. When coupled with modern machine learning techniques, rtfMRI becomes a powerful tool to read brain states in real time. Two separate experiments were conducted in this study to answer two broad research goals. The first experiment was conducted to investigate if visual perception and imagination can be decode in real-time and if presenting neurofeedback of the decoding results can help improved decoding/task performance. 25 elastic net logistic regression classifiers were trained on fMRI responses for 100 random flickering random patterns on a 5 x 5 grid in order to learn the stimulus to cortical activation mapping. The trained classifiers were then used to predict novel stimuli perceived and imagined on the same 5 x 5 grid used in training. Each perception and imagination conditions were performed twice once with feedback and once without it. The results of the study indicate that visual perception can be decoded in real-time with an average accuracy of 55%. No significant decoding accuracy was obtained for imagined stimuli. Furthermore, lower decoding accuracies were obtained for conditions with feedback than the same conditions