decoding sequence learning from single-trial intracranial eeg in humans解码序列学习实验颅内脑电图在人类身上.pdf

Decoding Sequence Learning from Single-Trial Intracranial EEG in Humans 1,7 . 2,3. 2,3 4 5 Marzia De Lucia * , Irina Constantinescu , Virginie Sterpenich , Gilles Pourtois , Margitta Seeck , Sophie Schwartz2,3,6 1 Department of Radiology, Vaudois University Hospital Center and University of Lausanne, Lausanne, Switzerland, 2 Department of Neuroscience, University of Geneva, Geneva, Switzerland, 3 Geneva Neuroscience Center, University of Geneva, Geneva, Switzerland, 4 Department of Experimental Clinical and Health Psychology, University of Ghent, Ghent, Belgium, 5 Department of Clinical Neurology, Geneva University Hospitals, Geneva, Switzerland, 6 Swiss Center for Affective Sciences, University of Geneva, Geneva, Switzerland, 7 Electroencephalography Brain Mapping Core, Center for Biomedical Imaging, Lausanne, Switzerland Abstract We propose and validate a multivariate classification algorithm for characterizing changes in human intracranial electroencephalographic data (iEEG) after learning motor sequences. The algorithm is based on a Hidden Markov Model (HMM) that captures spatio-temporal properties of the iEEG at the level of single trials. Continuous intracranial iEEG was acquired during two sessions (one before and one after a night of sleep) in two patients with depth electrodes implanted in several brain areas. They performed a visuomotor sequence (serial reaction time task, SRTT) using the fingers of their non- dominant hand. Our results show that the decoding algorithm correctly classified single iEEG trials from the trained sequence as belonging to either the initial training phase (day 1, before sleep) or a later consolidated phase