The aim of the study is to classify single trial electroencephalogram and to estimate active
regions/locations on skull in unfamiliar/familiar face recognition task. For this purpose …

The objective of the study is to classify electroencephalogram signals recorded in a familiar
and unfamiliar face recognition experiment. Frontal views of familiar and unfamiliar face …

We analyze Electroencephalograph EEG data with several classification algorithms to
classify probe and irrelevant data. Out of eight algorithms, five foun to perform poorly …

Face recognition is a complex cognitive task that involves a distributed network of neural
sources. While some components of this network have been identified, the temporal …

The purpose of this study is to extend a feature set with non-linear features to improve
classification rate of guilty and innocent subjects. Non-linear features can provide extra …

Research on brain machine interface (BMI) has been developed very fast in recent years.
Numerous feature extraction methods have successfully been applied to …

Face perception essentially refers to an individual's ability to understand and interpret a
familiar face. This paper attempts to quantify the face perceptual ability of human subjects …

In this work, we proposed an emotional face evoked EEG signal recognition framework,
within this framework the optimal statistic features were extracted from original signals …

The face recognition of familiar and unfamiliar people is an essential part of our daily lives.
However, its neural mechanism and relevant electroencephalography (EEG) features are …

Electroencephalography (EEG) signals have widely been used for developing Brain
Computer Interface (BCI) systems. BCI systems generally record, process and extract …