Single-trial extraction of event-related potentials (ERPs) and classification of visual stimuli by ensemble use of discrete wavelet transform with Huffman coding and machine learning techniques.

Background: Presentation of visual stimuli can induce changes in EEG signals that are typically detectable by averaging together data from multiple trials for individual participant analysis as well as for groups or conditions analysis of multiple participants. This study proposes a new method based on the discrete wavelet transform with Huffman coding and machine learning for single-trial analysis of evenal (ERPs) and classification of different visual events in the visual object detection task.

Methods: EEG single trials are decomposed with discrete wavelet transform (DWT) up to the [Formula: see text] level of decomposition using a biorthogonal B-spline wavelet.

Hemispheric Asymmetries in Electroencephalogram Oscillations for Long-Term Memory Retrieval in Healthy Individuals.

The hemispherical encoding retrieval asymmetry (HERA) model, established in 1991, suggests that the involvement of the right prefrontal cortex (PFC) in the encoding process is less than that of the left PFC. The HERA model was previously validated for episodic memory in subjects with brain traumas or injuries. In this study, a revised HERA model is used to investigate long-term memory retrieval from newly learned video-based content for healthy individuals using electroencephalography.

Exploring EEG Effective Connectivity Network in Estimating Influence of Color on Emotion and Memory.

Color is a perceptual stimulus that has a significant impact on improving human emotion and memory. Studies have revealed that colored multimedia learning materials (MLMs) have a positive effect on learner's emotion and learning where it was assessed by subjective/objective measurements. This study aimed to quantitatively assess the influence of colored MLMs on emotion, cognitive processes during learning, and long-term memory (LTM) retention using electroencephalography (EEG).

Neural correlates of eye contact in face-to-face verbal interaction: An EEG-based study of the extraversion personality trait.

The extraversion personality trait has a positive correlation with social interaction. In neuroimaging studies, investigations on extraversion in face-to-face verbal interactions are still scarce. This study presents an electroencephalography (EEG)-based investigation of the extraversion personality trait in relation to eye contact during face-to-face interactions, as this is a vital signal in social interactions.

Classification of Visual and Non-visual Learners Using Electroencephalographic Alpha and Gamma Activities.

This study analyzes the learning styles of subjects based on their electroencephalo-graphy (EEG) signals. The goal is to identify how the EEG features of a visual learner differ from those of a non-visual learner. The idea is to measure the students' EEGs during the resting states (eyes open and eyes closed conditions) and when performing learning tasks.

Classification of EEG Signals Based on Pattern Recognition Approach.

Feature extraction is an important step in the process of electroencephalogram (EEG) signal classification. The authors propose a "pattern recognition" approach that discriminates EEG signals recorded during different cognitive conditions. Wavelet based feature extraction such as, multi-resolution decompositions into detailed and approximate coefficients as well as relative wavelet energy were computed.

Single Trial EEG Patterns for the Prediction of Individual Differences in Fluid Intelligence.

Assessing a person's intelligence level is required in many situations, such as career counseling and clinical applications. EEG evoked potentials in oddball task and fluid intelligence score are correlated because both reflect the cognitive processing and attention. A system for prediction of an individual's fluid intelligence level using single trial Electroencephalography (EEG) signals has been proposed.

Visual brain activity patterns classification with simultaneous EEG-fMRI: A multimodal approach.

Background: Classification of the visual information from the brain activity data is a challenging task. Many studies reported in the literature are based on the brain activity patterns using either fMRI or EEG/MEG only. EEG and fMRI considered as two complementary neuroimaging modalities in terms of their temporal and spatial resolution to map the brain activity.

A System for True and False Memory Prediction Based on 2D and 3D Educational Contents and EEG Brain Signals.

We studied the impact of 2D and 3D educational contents on learning and memory recall using electroencephalography (EEG) brain signals. For this purpose, we adopted a classification approach that predicts true and false memories in case of both short term memory (STM) and long term memory (LTM) and helps to decide whether there is a difference between the impact of 2D and 3D educational contents. In this approach, EEG brain signals are converted into topomaps and then discriminative features are extracted from them and finally support vector machine (SVM) which is employed to predict brain states.

Evaluation of passive polarized stereoscopic 3D display for visual & mental fatigues.

Visual and mental fatigues induced by active shutter stereoscopic 3D (S3D) display have been reported using event-related brain potentials (ERP). An important question, that is whether such effects (visual & mental fatigues) can be found in passive polarized S3D display, is answered here. Sixty-eight healthy participants are divided into 2D and S3D groups and subjected to an oddball paradigm after being exposed to S3D videos with passive polarized display or 2D display.

A Novel Approach Based on Data Redundancy for Feature Extraction of EEG Signals.

Feature extraction and classification for electroencephalogram (EEG) in medical applications is a challenging task. The EEG signals produce a huge amount of redundant data or repeating information. This redundancy causes potential hurdles in EEG analysis.

P300 correlates with learning & memory abilities and fluid intelligence.

Background: Educational psychology research has linked fluid intelligence with learning and memory abilities and neuroimaging studies have specifically associated fluid intelligence with event related potentials (ERPs). The objective of this study is to find the relationship of ERPs with learning and memory recall and predict the memory recall score using P300 (P3) component.

Method: A sample of thirty-four healthy subjects between twenty and thirty years of age was selected to perform three tasks: (1) Raven's Advanced Progressive Matrices (RAPM) test to assess fluid intelligence; (2) learning and memory task to assess learning ability and memory recall; and (3) the visual oddball task to assess brain-evoked potentials.

EEG based evaluation of stereoscopic 3D displays for viewer discomfort.

Background: Consumer preference is rapidly changing from 2D to 3D movies due to the sensational effects of 3D scenes, like those in Avatar and The Hobbit. Two 3D viewing technologies are available: active shutter glasses and passive polarized glasses. However, there are consistent reports of discomfort while viewing in 3D mode where the discomfort may refer to dizziness, headaches, nausea or simply not being able to see in 3D continuously.

Feature extraction and classification for EEG signals using wavelet transform and machine learning techniques.

This paper describes a discrete wavelet transform-based feature extraction scheme for the classification of EEG signals. In this scheme, the discrete wavelet transform is applied on EEG signals and the relative wavelet energy is calculated in terms of detailed coefficients and the approximation coefficients of the last decomposition level. The extracted relative wavelet energy features are passed to classifiers for the classification purpose.