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Dual stimuli interface with logical division using local move stimuli.

Mengfan LiLingyu WuFang LinMiaomiao GuoGuizhi Xu
Published in: Cognitive neurodynamics (2022)
Improving information transfer rate is a key to prompt the speed of outputting instructions of the event-related potential-based brain-computer interface. Our previous study designed a dual-stimuli interface that simultaneously presents two types of different stimuli to improve the speed. While, adding more stimuli into this interface makes subject easily affected by "flanker effect" that decreases the accuracy of recognizing intention. To achieve high recognition accuracy with many stimuli, this study proposes a dual stimuli interface based on whole flash and local move (DS-WL) and two rules of stimulus arrangement to induce the brain signals. Twenty subjects participated in the experiment, and their signals are recognized by a back propagation neural network classifier. The local move induces larger and later signals of targets to help discriminate the two kinds of stimuli; the rules reduce the N200 and P300 amplitudes of non-target, which improves accuracy. This study demonstrates that the DS-WL is a useful way to shorten the instruction output cycle and speed up the instructions outputting by local move and rules.
Keyphrases
  • neural network
  • healthcare
  • white matter
  • risk assessment
  • machine learning
  • blood brain barrier
  • climate change
  • subarachnoid hemorrhage
  • cerebral ischemia