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This paper describes a research project conducted to correlate emotion to film rating classification using EEG signals measured by Emotiv device. Certain films contain inappropriate content that call for the need to classify their ratings. Every Television of Film Review Board rates a certain film based on their general criteria. Since emotion plays a huge role in decision making, this paper conducts...
Cognition emerges from complex interaction amongst widespread brain areas. In this paper, we use a novel methodology for temporal networks quantification for EEG. We model the spatiotemporal structure of dependencies across different electrodes with respect to a single electrode as a local probability density function. This enables immediately the use of information theoretic quantities (information...
In this paper, the use of mutual information and the Learn++.NSE algorithm is proposed to create an EEG SSVEP BCI system that can select and utilize data sets originating from a group of users. In typical BCI systems, the nonstationarity in the EEG prevents the system from blindly applying training data from other users to the incoming data. Mutual information is introduced to select previous data...
The detection of steady state visual evoked potentials (SSVEPs) has been identified as an effective solution for brain computer interface (BCI) systems as well as for neurocognitive investigations of visually related tasks. SSVEPs are induced at the same frequency as the visual stimuli and can be observed in the scalp-based recordings of electroencephalogram signals, though they are one component...
The stimulus flickering at specific frequencies, or as known as steady-state visually evoked potential (SSVEP), can be recorded on an occipital area of the brain. SSVEP is used to interpret the EEG signal to detect the desired goal of the experiment. In this paper, we aim to investigate SSVE signal by means of magnitude-squared coherence (MSC) analysis between the red and green visual stimuli. In...
The correlation between visual perception and the evoked emotional response has been investigated extensively in the area of cognitive psychology using the method of electroencephalography (EEG). Activities in the visual cortex have been found to be correlated with psychophysical performance, visual attention, and subjective perceptual experience. While electroencephalography could record the cerebral...
The paper presents multiple features analysis of cognitive load case study. The set of features applied in the research covers response times, committed errors, EEG spectral data as well as pupillometry and eye-tracking (ET) data including fixations, saccades and blinks. The experiment took the form of eleven intervals: six containing arithmetic tasks and five breaks. Two correlation analyses were...
In this paper, we aimed to get the trends of the KANSEI, which is alike “how to feel”, values of the memorable TV commercials (CMs) using the electroencephalogram (EEG) while subjects watch TV CMs. KANSEI is Japanese word because of studying at first in Japan. The questionnaire has been used as conventional evaluation method of TV CMs. This method is subjective evaluation, so it is difficult to know...
The present study provides a new framework for comparing functional brain connectivity between a continuous and missing sample of meditative EEG signal. The EEG signal acquired during meditation (Kriya Yoga) and after the removal of motifs as EOG spikes, few significant parameters of functional connectivity have been found out. Three essential parameters, i.e. Clustering coefficient, Global efficiency,...
The inherent intangible nature, complexity, context-specific interpretations of emotions make it difficult to quantify and model affective space. Dimensional theory is one of the effective methods to describe and model emotions. Despite recent advances in affective computing, modeling continuous affective space remains a challenge. Here, we present a computational framework to study the role of functional...
In this paper, the detrended fluctuation analysis (DFA) is used to quantify the fractal-like scaling properties of the EEG signals. The power-law exponent of DFA of the EEG time series was used to distinguish subjects with epilepsy from healthy controls. The performance of the proposed method was evaluated using the receiver operating characteristics (ROC) curves. The obtained results show that the...
Automatic emotion recognition based on multi-channel neurophysiological signals, as a challenging pattern recognition task, is becoming an important computer-aided method for emotional disorder diagnoses in neurology and psychiatry. Traditional approaches require designing and extracting a range of features from single or multiple channel signals based on extensive domain knowledge. This may be an...
The xDAWN algorithm is well-known as a method for designing spatial filters to improve signal-to-noise ratio and to reduce the dimension of observed EEG signals. This paper proposes a method for spatially smoothing xDAWN spatial filters to give a robustness against small sample problem. The proposed method gives a subspace constraint to the parameter space of the spatial filters. This subspace is...
It has been reported that chronic heroin intake induces changes in central nervous system of human brain; however, few studies investigate the carry-over adverse effects on brain after heroin withdrawal. In this work we examined the alpha rhythms of resting-state Electroencephalogram (EEG) signals to measure the neuroelectrical differences between the heroin addicts after heroin withdrawal and normal...
Emotional processing of ex-combatants is affected by chronic exposure to violent events. For a successful reintegration into society, it is necessary to discriminate their brain responses from civilian people, as a first stage to develop treatment strategies. This paper presents a comparative analysis between a Multilayer Perceptron Neural Network and a Fuzzy C-Means classifier to differentiate ex-combatant...
In this paper, we propose a data-driven framework to objectively evaluate the dynamics of brain functional connectivity, during task execution, from electroencephalography (EEG) recordings. The proposed framework consists of two main steps: first, EEG recordings, in the sensor space, are segmented into intervals during which the spatial distribution of functionally connected networks, in the source...
This paper presents a novel patient-specific algorithm for prediction of seizures in epileptic patients. Spectral power features, including relative spectral powers and spectral power ratios, and cross correlation coefficients between all pairs of electrodes, are extracted as two independent feature sets. Both feature sets are selected independently in a patient-specific manner by classification and...
Brain-computer interfaces (BCIs) promise to promote a novel access channel for functional independence for individuals with severe speech and physical impairment (SSPI) that can occur as a result of numerous neurological diseases and injuries. Current BCI systems lack the robustness and accuracy to allow individuals with SSPI to complete tasks required for independent living (e.g. communication or...
Despite the phenomenal advances in the computational power of electronic systems, human-machine interaction has been largely limited to simple control panels, such as keyboards and mice, which only use physical senses. Consequently, these systems either rely critically on close human guidance or operate almost independently. A richer experience can be achieved if cognitive inputs are used in addition...
The researches relevant to age-related changes have been the significant subject causing more and more attraction. This study is to investigate age-related changes of event-related potentials with detrended partial cross-correlation analysis (DPCCA) [11,12] to explore the discrimination between the young and the older. We hypothesized that the DPCCA coefficients were affected by age-related changes...
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