Eye fixation-related potential (EFRP) measures electrical brain activity in response to eye fixations. The aim of the current study was to investigate whether the EFRPs vary during consecutive eye fixations while subjects were performing an object identification task. Eye fixations evoked P1 and N1 components at the occipital and parietal recording sites. The latency of P1 component increased during consecutive fixations. The amplitude of P1 increased and the amplitude of N1 decreased during consecutive fixations. The results indicate that EFRPs are modulated during consecutive fixations, suggesting that the current technique may provide a useful tool to study temporal dynamics of visual perception and processes underlying object identification.

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Objectives: To identify and select the customers' liked products by introducing a new product recommendation system. Methods: This work proposes a new product recommendation system that incorporates a new feature optimization method called Sentiment weighted Horse herd Optimization Algorithm (SHOA) to identify the most suitable words that help perform effective prediction. This work's prediction process is carried out by applying a newly proposed Deep Belief Network incorporating fuzzy temporal features. This work uses two different Amazon datasets. The first dataset contains 51, 00,000 review comments about various products, including books and movies. The second dataset is built with 82,00,000 review comments on Toys and Games. These data sets consider the product id and review rate important features and are used to compare with all other available works through experimental results. Findings:The experiments have been conducted using the Amazon dataset and proved better than other recommendation systems in terms of effectiveness and efficiency through Precision, Recall, Serendipity and nDCG value. Novelty: The introduction of a new DBN with Fuzzy Temporal rules and the newly developed SHOA is novel in this work to recommend suitable products to the customer.