Neural network classification of EEG during camouflaged object identification.
A generalized regression neural network (GRNN) was trained to discriminate between EEGs recorded while subjects identified a camouflaged target object (picture condition) from EEGs recorded during a visually matched control task (control condition). In the picture condition subjects, three female and two male right handers, ages 23-47, viewed images depicting camouflaged target objects and signaled identification by blinking. In the control condition subjects viewed a neutral screen and blinked at will. EEGs made immediately preceding and following the blink were band-pass filtered at 2-8 Hz. The network achieved a marked increase in discriminability in the final 250 ms preceding target identification, with chance level of discrimination before and after. Network performance using scrambled data was also at chance level.
Rzempoluck
E J
EJ
Department of Psychology, Simon Fraser University, Burnaby, British Columbia, Canada. erzempol@uvic.ca
D000328:Adult; D016477:Artifacts; D001288:Attention / Q000502:physiology; D001767:Blinking / Q000502:physiology; D002540:Cerebral Cortex / Q000502:physiology*; D004193:Discrimination Learning / Q000502:physiology; D004569:Electroencephalography / Q000145:classification* / Q000295:instrumentation; D005260:Female; D006801:Humans; D008297:Male; D008875:Middle Aged; D016571:Neural Networks, Computer; D010364:Pattern Recognition, Visual / Q000502:physiology; D010470:Perceptual Masking / Q000502:physiology; D011930:Reaction Time / Q000502:physiology; D012044:Regression Analysis; D012815:Signal Processing, Computer-Assisted / Q000295:instrumentation; D014796:Visual Perception / Q000502:physiology*