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Objective: Deep brain stimulation is a treatment option for patients with refractory obsessive-compulsive disorder. A new generation of stimulators hold promise for closed loop stimulation, with adaptive stimulation in response to biologic signals. Here we aimed to discover a suitable biomarker in the ventral striatum in patients with obsessive compulsive disorder using local field potentials. Approach: We induced obsessions and compulsions in 11 patients undergoing deep brain stimulation treatment using a symptom provocation task. Then we trained machine learning models to predict symptoms using the recorded intracranial signal from the deep brain stimulation electrodes. Main results: Average areas under the receiver operating characteristics curve were 62.1% for obsessions and 78.2% for compulsions for patient specific models. For obsessions it reached over 85% in one patient, whereas performance was near chance level when the model was trained across patients. Optimal performances for obsessions and compulsions was obtained at different recording sites. Significance: The results from this study suggest that closed loop stimulation may be a viable option for obsessive-compulsive disorder, but that intracranial biomarkers are patient and not disorder specific. Clinical Trial: Netherlands trial registry NL7486

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