Abstract:
One area under investigation in the field of neuroscience is the
link between object perception and neural activity in visual cortical areas
of the human brain. By investigating the electrical potentials from the
ventral temporal cortical surface in humans, the Stanford University study
selected for this paper sought to collect sufficient information for
spontaneous and near-instantaneous identification of a subject’s perceptual
state. The brain signal data collection technique used by the researchers was
electrocorticography (ECoG), using ECoG arrays placed on the subtemporal
cortical surface of seven epilepsy patients. ECoG is an invasive electrogram
method, requiring access to the surface of the brain, which can be applied to
measure brain signals in response to specific stimuli. Using publicly
available human ECoG recording data previously collected and made publicly
available, this paper investigates visual object processing in the human
brain. The data are taken from a study where seven epilepsy patients were
shown house and face images in quick succession. We use those data and
filter, process, and plot selected data to investigate the correct
identification of the stimuli. We discovered that the incorrect stimuli
matches are driven by variance in the human brain activity corresponding to
the same set of stimuli. Better understanding of the visual processing
capabilities of the human brain could lead to developments in machine
learning, as well as generate recommendations for future data collection in
human visual object processing.