Abstract
In order to classify different human brain states related to visual perception of ambiguous images, we use an artificial neural network (ANN) to analyze multichannel EEG. The classifier built on the basis of a multilayer perceptron achieves up to 95% accuracy in classifying EEG patterns corresponding to two different interpretations of the Necker cube. The important feature of our classifier is that trained on one subject it can be used for the classification of EEG traces of other subjects. This result suggests the existence of common features in the EEG structure associated with distinct interpretations of bistable objects. We firmly believe that the significance of our results is not limited to visual perception of the Necker cube images; the proposed experimental approach and developed computational technique based on ANN can also be applied to study and classify different brain states using neurophysiological data recordings. This may give new directions for future research in the field of cognitive and pathological brain activity, and for the development of brain-computer interfaces.
Highlights
The brain is likely the most convoluted and enigmatic research object, attracting the burning interest of the broad scientific community in diverse areas of science and technology, including neurophysiology, medicine, engineering, physics, and mathematics (Wolf, 2005; Bick and Rabinovich, 2009; Chavez et al, 2010; van Luijtelaar et al, 2011; Bear et al, 2015; Hramov et al, 2015)
We have proposed the use of an artificial neuronal network for classification and automatic recognition of human brain states associated with the perception of ambiguous images
We optimized the artificial neural network (ANN) architecture and achieved up to 95% accuracy in the classification of the EEG patterns during perception of ambiguous images
Summary
The brain is likely the most convoluted and enigmatic research object, attracting the burning interest of the broad scientific community in diverse areas of science and technology, including neurophysiology, medicine, engineering, physics, and mathematics (Wolf, 2005; Bick and Rabinovich, 2009; Chavez et al, 2010; van Luijtelaar et al, 2011; Bear et al, 2015; Hramov et al, 2015). This problem has attracted a lot of attention of various researchers, especially in connection with such important tasks as object recognition (Martin, 2007; Müler et al, 2008; Simanova et al, 2010; Isik et al, 2014) and decision making (Heekeren et al, 2008; Wang, 2008, 2012). Nowadays, these tasks are of great practical importance for the development of novel communication, computer technologies, and robotics. The underlying mechanism of image recognition is not yet well understood, the metastable visual perception is known to engage a distributed network of occipital, parietal, and frontal cortical areas (Tong et al, 2006; Sterzer et al, 2009)
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