Abstract
Visual decoding is a critical way to understand the face perception mechanism of the brain in the neuroscience field. Magnetoencephalography (MEG) is a completely noninvasive measurement technique that provides information about brain cortical functioning using the magnetic field of neuronal electrical activity. These neuromagnetic signals measured by MEG may emerge as a result of a visual stimulus during the brain decoding process for the face perception mechanism. In this research study, two classes of visual stimuli (face/scrambled face), including 9414 trials in total, were used. In order to obtain meaningful data from noisy MEG recordings, feature extraction approaches and classification systems are required. For the purpose of feature extraction, the Riemannian approach, characterized by its competitive nature, has been used. A probabilistic neural network and a multilayer neural network structures were proposed to classify magnetoencephalography signals. The obtained results were presented comparatively with the results of prior studies using the same dataset. The classification accuracies of 82.36% and 77.78% were achieved for the probabilistic neural network and multilayer neural network, respectively. Moreover, the probabilistic neural network classifier could be expected to be an alternative method to other competing methods. Because PNN does not use the back-propagation algorithm, so there is no need to train the network with the whole dataset. Thus, the classification process is performed faster.
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This study was supported by Scientific Research Project Unit of the Bandırma Onyedi Eylül University under Project No: BAP-18-MF-1003-005.
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Cetin, O., Temurtas, F. A comparative study on classification of magnetoencephalography signals using probabilistic neural network and multilayer neural network. Soft Comput 25, 2267–2275 (2021). https://doi.org/10.1007/s00500-020-05296-7
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DOI: https://doi.org/10.1007/s00500-020-05296-7