Chapter

Brain-Computer Interface Research

Part of the series SpringerBriefs in Electrical and Computer Engineering pp 105-114

Date:

Near-Instantaneous Classification of Perceptual States from Cortical Surface Recordings

  • Kai J. MillerAffiliated withDepartment of Neurosurgery, Stanford UniversityProgram in Neurobiology and Behavior, University of Washington Email author 
  • , Gerwin SchalkAffiliated withNational Center for Adaptive Neurotechnologies, Wadsworth Center
  • , Dora HermesAffiliated withDepartment of Psychology, Stanford University
  • , Jeffrey G. OjemannAffiliated withProgram in Neurobiology and Behavior, University of WashingtonDepartment of Neurological Surgery, University of WashingtonCenter for Sensorimotor Neural Engineering, University of Washington
  • , Rajesh P. N. RaoAffiliated withProgram in Neurobiology and Behavior, University of WashingtonComputer Science and Engineering, University of WashingtonCenter for Sensorimotor Neural Engineering, University of Washington Email author 

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Abstract

Human visual processing is of such complexity that, despite decades of focused research, many basic questions remain unanswered. Although we know that the inferotemporal cortex is a key region in object recognition, we don’t fully understand its physiologic role in brain function, nor do we have the full set of tools to explore this question. Here we show that electrical potentials from the surface of the human brain contain enough information to decode a subject’s perceptual state accurately, and with fine temporal precision. Electrocorticographic (ECoG) arrays were placed over the inferotemporal cortical areas of seven subjects. Pictures of faces and houses were quickly presented while each subject performed a simple visual task. Results showed that two well-known types of brain signals—event-averaged broadband power and event-averaged raw potential—can independently or together be used to classify the presented image. When applied to continuously recorded brain activity, our decoding technique could accurately predict whether each stimulus was a face, house, or neither, with ~20 ms timing error. These results provide a roadmap for improved brain-computer interfacing tools to help neurosurgeons, research scientists, engineers, and, ultimately, patients.

Keywords

Human vision Electrocorticography Broadband power Event-related potential Fusiform cortex