Synthetic-Neuroscore: Using A Neuro-AI Interface for Evaluating Generative Adversarial Networks

Generative adversarial networks (GANs) are increasingly attracting attention in the computer vision, natural language processing, speech synthesis and similar domains. Arguably the most striking results have been in the area of image synthesis. However, evaluating the performance of GANs is still an open and challenging problem. Existing evaluation metrics primarily measure the dissimilarity between real and generated images using automated statistical methods. They often require large sample sizes for evaluation and do not directly reflect human perception of image quality. In this work, we describe an evaluation metric we call Neuroscore, for evaluating the performance of GANs, that more directly reflects psychoperceptual image quality through the utilization of brain signals. Our results show that Neuroscore has superior performance to the current evaluation metrics in that: (1) It is more consistent with human judgment; (2) The evaluation process needs much smaller numbers of samples; and (3) It is able to rank the quality of images on a per GAN basis. A convolutional neural network (CNN) based neuro-AI interface is proposed to predict Neuroscore from GAN-generated images directly without the need for neural responses. Importantly, we show that including neural responses during the training phase of the network can significantly improve the prediction capability of the proposed model. Materials related to this work are provided at https://github.com/villawang/Neuro-AI-Interface

Brain-Switches for Asynchronous Brain−Computer Interfaces: A Systematic Review

A brain–computer interface (BCI) has been extensively studied to develop a novel communication system for disabled people using their brain activities. An asynchronous BCI system is more realistic and practical than a synchronous BCI system, in that, BCI commands can be generated whenever the user wants. However, the relatively low performance of an asynchronous BCI system is problematic because redundant BCI commands are required to correct false-positive operations. To significantly reduce the number of false-positive operations of an asynchronous BCI system, a two-step approach has been proposed using a brain-switch that first determines whether the user wants to use an asynchronous BCI system before the operation of the asynchronous BCI system. This study presents a systematic review of the state-of-the-art brain-switch techniques and future research directions. To this end, we reviewed brain-switch research articles published from 2000 to 2019 in terms of their (a) neuroimaging modality, (b) paradigm, (c) operation algorithm, and (d) performance

Decision Making and the Brain: Neurologists' View

The article reflects the fact, that concepts like decision making and free will have entered the field of cognitive neuroscience towards the end of 20th century. It gives an overview of brain structures involved in decision making and the concept of free will; and presenting the results of clinical observations and new methods (functional neuroimaging, electrophysiology) it postulates possible mechanisms of these processes. We give a review of the neuroanatomy, specially discussing those parts of the brain important to the present topic, because the process of decision making is dependent on deep subcortical as well as superficial cortical structures. Dopamine has a central role in the in process of reward related behaviour and hedonism. A list of brain structures, related to dopamine action, is also given. The article especially concentrates on the Single Photon Emission Computer Tomography studies in patients with Parkinson's disease (neuroimaging), as well as to the studies concerning the Readiness Potential and Endogeneous Potential P300 (electrophysiology). In the end, we discuss the volition, whose functional anatomy overlaps with the functional anatomy of free will and decision making processes.cognitive neuroscience, brain, decision making, free will, electrophysiology, functional imaging, dopamine