Genuineness of smiles is particular interest in the field human emotions and social interactions. In this work, we develop an experimental protocol to elicit genuine fake smile expressions on 28 healthy subjects. Then, assess type using electroencephalogram (EEG) signals with convolutional neural networks (CNNs). Five different architectures (CNN1, CNN2, CNN3, CNN4, CNN5) were examined differen...

Pattern classification techniques have been widely used to differentiate neural activity associated with different perceptual, attentional, or other cognitive states, often using fMRI, but more recently with EEG as well. Although these methods have identified EEG patterns (i.e., scalp topographies of EEG signals occurring at certain latencies) that decode perceptual and attentional states on a ...

The biometrics contains emerging methods for human identification. As advances in technology, conventional techniques using fingerprint or iris have the risk of being duplicated. In this work we utilize the inter-subject differences in the electroencephalographic (EEG) signals evoked by visual stimuli for person identification. The identification procedure is divided into classification and ver...

The effectiveness of neurophysiologically triggered adaptive systems hinges on reliable and effective signal processing and cognitive state classification. While this presents a difficult technical challenge in any context, these concerns were particularly pronounced in a system designed for mobile contexts. This paper describes a neurophysiologically-derived cognitive state classification appr...

This paper focuses on the development of an automatic system for sleep analysis. The system proposed in this paper combines two phases needed in sleep analysis. In a first step, an artefact detection system selects the polysomnographic signals (EEG, EOG, EMG) that are not corrupted by artefacts. In a second step, relevant features are extracted from the selected signals and classified using a n...

The task oriented brain activity analysis and classification is a prime issue in EEG signal processing these days. The similar attempt has been done here to estimate the brain activity on the basis of power spectrum analysis. For this, the modified approach involving both Independent Component Analysis (ICA) and Principal Component Analysis (PCA) methodologies has been used in this paper to inv...

The classification of electroencephalography (EEG) signals is one of the most important methods for seizure detection. However, verification of an atypical epileptic seizure often can only be done through long-term EEG monitoring for 24 hours or longer. Hence, automatic EEG signal analysis for clinical screening is necessary for the diagnosis of epilepsy. We propose an EEG analysis system of se...

Support vector machines are powerful tools used to solve the small sample and nonlinear classification problems, but their ultimate classification performance depends heavily upon the selection of appropriate kernel and penalty parameters. In this study, we propose using a particle swarm optimization algorithm to optimize the selection of both the kernel and penalty parameters in order to impro...

The study of the electrical signals produced by neural activities of human brain is called Electroencephalography. In this paper, we propose an automatic and efficient EEG signal classification approach. The proposed approach is used to classify the EEG signal into two classes: epileptic seizure or not. In the proposed approach, we start with extracting the features by applying Discrete Wavelet...

Ahsrr-ucr-This paper describes the application of an artificial neural network (ANN) technique together with a feature extraction technique, viz., the wavelet transform, for the classification of EEG signals. Three classes of EEG signals were used: Normal, Schizophrenia (SCH), and Obsessive Compulsive Disorder (OCD). The architecture of the artificial neural network used in the classification i...