Spontaneous Decoding of the Timing and Content of Human Object Perception from Cortical Surface Recordings Reveals Complementary Information in the Event-Related Potential and Broadband Spectral Change

• Published in: PLoS computational biology Vol. 12; no. 1; p. e1004660
• Main Authors: Miller, Kai J., Schalk, Gerwin, Hermes, Dora, Ojemann, Jeffrey G., Rao, Rajesh P. N.
• Format: Journal Article
• Language: English
• Published: United States Public Library of Science 01.01.2016; Public Library of Science (PLoS)
• Subjects: Accuracy; Brain Mapping - methods; Brain research; Computational Biology; Electrodes; Electroencephalography - methods; Epilepsy - physiopathology; Evoked Potentials - physiology; Houses; Humans; Signal Processing, Computer-Assisted; Temporal Lobe - physiology; Visual Perception - physiology
• ISSN: 1553-7358; 1553-734X; 1553-7358
• DOI: 10.1371/journal.pcbi.1004660

The link between object perception and neural activity in visual cortical areas is a problem of fundamental importance in neuroscience. Here we show that electrical potentials from the ventral temporal cortical surface in humans contain sufficient information for spontaneous and near-instantaneous identification of a subject's perceptual state. Electrocorticographic (ECoG) arrays were placed on the subtemporal cortical surface of seven epilepsy patients. Grayscale images of faces and houses were displayed rapidly in random sequence. We developed a template projection approach to decode the continuous ECoG data stream spontaneously, predicting the occurrence, timing and type of visual stimulus. In this setting, we evaluated the independent and joint use of two well-studied features of brain signals, broadband changes in the frequency power spectrum of the potential and deflections in the raw potential trace (event-related potential; ERP). Our ability to predict both the timing of stimulus onset and the type of image was best when we used a combination of both the broadband response and ERP, suggesting that they capture different and complementary aspects of the subject's perceptual state. Specifically, we were able to predict the timing and type of 96% of all stimuli, with less than 5% false positive rate and a ~20ms error in timing.