SSVEP Harmonic Fusion for Improved Visual Field Reconstruction with CNN
| Autor: | Kai Wen Zheng, Steve Mann, Danson Evan Garcia |
|---|---|
| Rok vydání: | 2021 |
| Předmět: |
Signal processing
Quantitative Biology::Neurons and Cognition business.industry Noise (signal processing) Computer science Noise reduction 02 engineering and technology Convolutional neural network Visual field 03 medical and health sciences 0302 clinical medicine Visual cortex medicine.anatomical_structure Harmonics 0202 electrical engineering electronic engineering information engineering medicine Harmonic 020201 artificial intelligence & image processing Computer vision Artificial intelligence business 030217 neurology & neurosurgery |
| Zdroj: | NER |
| Popis: | Steady-state visually evoked potentials (SSVEPs) occur due to a repetitive visual stimulus, which results in periodic responses from the visual cortex at the stimulus frequency and its harmonics. Prior studies show that the fundamental SSVEP frequency response can be used to produce a visual reconstruction of what is shown to the human eye. However, due to interference coming from the source and the sensing device, the resulting captured image contains salt-and-pepper noise and random value noise. This study investigates whether information present in the SSVEP harmonics is useful in denoising and enhancing the captured visual reconstruction. The proposed convolutional neural network architecture methods are compared against the SSVEP fundamental and naive additive reconstructions. The results show that combining harmonics and reconstructions from different signal processing methods into the neural network architecture enhances the resulting image. |
| Databáze: | OpenAIRE |
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