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Emotion is closely related to healthy and abnormal mood is the alarm of our body. This paper is concentrated on the objective and accurate emotion classification using EEG signal. We propose emotional patches and combine it with the deep belief network(DBN) to achieve high-precision emotion classification. DBN is able to fit the distribution of the EEG signal and mapping the extracted feature to the...
In medical science, sleep stages are the main criteria to define the disorders and have crucial role on diagnostic. In this sense, accurate sleep stage classification plays important role due to provide better report on medications and diagnoses. In this study, EEG signals are classified by a rule based machine learning algorithm; Decision Tree with the ensemble and classical machine learning idea...
One of the popular features extraction methods for recognizing motor imagery EEG signal is Common Spatial Pattern (CSP). CSP is an algorithm that maximize the variance of one class and minimize the variance of other class simultaneously to discriminate two classes of multichannel EEG signals for classification purpose. However, CSP assumes that the signals on all EEG channels are functionally interconnected...
Sparse Bayesian Learning (SBL) is a widely used framework which helps us to deal with two basic problems of machine learning, to avoid overfitting of the model and to incorporate prior knowledge into it. In this work, multiple linear regression models under the SBL framework are used for the problem of multiclass classification when multiple subjects are available. As a case study, we apply our method...
Epilepsy is the fourth most frequent neurological disorder. Epileptic seizures are the result of temporary electrical disturbances in the brain. This disorder can be diagnosed by electroencephalograms (EEG). Accordingly, data mining supported by machine learning (ML) methods can be used to find patterns in EEG and to build classifiers. However, the presence of physiological abnormalities is considered...
Motor imagery BCI is a system that is very useful to help people with disabilities who can't move their limbs. These systems use brain activity patterns that are made from motor imagery without actual movement. In this paper, we proposed enhanced One Versus One (OVO) structure to classify EEG-based multi-class motor imagery signals. Also, shrinkage estimator based Common Spatial Pattern (CSP) is used...
Recent advancements in brain computer-interfacing (BCI) and neuro-robotics have played an indispensable role for people suffering from neural injuries to expect better quality of life by restoring sensory functions and replacement of neuro-muscular pathways as BCI systems work on imagination of movements to control prosthetic limbs. In this research, multiple combinations of features and classifiers...
Authentication is a crucial consideration when securing data or any kind of information system. Though existing approaches for authentication are user-friendly, they have vulnerabilities such as the possibility & criminally threatening a user. We propose a novel approach which uses Electroencephalogram (EEG) brain signals for an authentication process. Unique features of EEG data for distinguishing...
The canonical correlation analysis (CCA), double-partial least-squares (DPLS) methods and least absolute shrinkage and selection operator (LASSO) have been proven effectively in detecting the steady-state visual evoked potential (SSVEP) in SSVEP-based brain-computer interface systems. However, the accuracy of SSVEP classification can be affected by phase shifts of the electroencephalography data,...
This paper presents a systematic method to select optimal electroencephalography (EEG) channels for three mental tasks-based brain-computer interface (BCI) classification. A blind source separation (BSS) technique based on independent component analysis (ICA) with its back-projecting of the scalp map was used for selecting the optimal EEG channels. The three mental tasks included: mental letter composing,...
This paper illustrated the use of the Cubic spline Technique (CST) to analyze the EEG signals. It is provides full description of the extraction of the knots of EEG signals and then a discussion of how to select the optimum location of the knot and reducing the knots. Also the paper discussed that the feature extracted dependent on the optimal position of the knots. The initial results show the highest...
Autism is a neurodevelopmental disorder that changes the normal brain function. Several studies have reported that the patterns of brain connectivity in autistic and healthy individuals are different. In this paper the effective connectivities of autistic and healthy children were measured through Granger Causality and then applied as discriminant features to separate the two groups. To estimate the...
Modeling of human emotion with the effective frequency band of EEG signal plays a significant role in brain signal analysis and physiological research area. This paper describes an approach to model human emotion with the variations of different effective frequency bands of EEG signal during a test when subjected to sustained mental task. For this purpose, the EEG signals are collected when different...
A channel of communication for both human brain and computer system is provided via a system called Brain Computer Interface (BCI). The vital aim of BCI research is to develop a system that helps the disabled people to interact with other persons and allows their interaction with the external environments or as an additional man-machine interaction channel for healthy users. Different techniques have...
Performance of motor imagery (MI)-based brain computer interfaces (BCIs) highly depends on the extraction of features from different brain neural processes. Commonly designed BCIs use band power changes in single channel electroencephalograms (EEGs) to discriminate different MI tasks. In this paper, we studied the information about interactions of spatially separated brain areas by considering the...
Electroencephalography is most common noninvasive neuroimaging modality and it is widely used for measuring brain electrical signals. Measurement of electrical signals from the scalp requires high density electrodes and low noise amplifier. It is well known fact that neural activity increased with increasing the mental work e.g., IQ task in our case. In this paper, non-linear features have been used...
Adaptive autoregressive (AAR) modeling of the EEG time series and the AAR parameters has been widely used in Brain computer interface (BCI) systems as input features for the classification stage. Multivariate adaptive autoregressive modeling (MVAAR) also has been used in literature. This paper revisits the use of MVAAR models and propose the use of adaptive Kalman filter (AKF) for estimating the MVAAR...
This paper studies the design of the novel optometrical examination of single pole brainwave system which measure visual brainwave signals of patients with expressive language disorder by single pole brainwave acquisition device, vision tester and software. The honeycomb pattern of Snellen test chart is an eye chart that can be used to measure the visual brainwave signals of the refractive error....
Nowadays the rapid development in the area of human-computer interaction has given birth to a growing interest on detecting different affective states through smart devices. By using the modern sensor equipment, we can easily collect electroencephalogram (EEG) signals, which capture the information from central nervous system and are closely related with our brain activities. Through the training...
Electroencephalogram (EEG) recordings aroused as inputs of a motor imagery based BCI system. Eye blinks contaminate the spectral frequency of the EEG signals. Independent Component Analysis (ICA) has been already proved for removing these artifacts whose frequency band overlap with the EEG of interest. However, already ICA developed methods, use a reference lead such as the ElectroOculoGram (EOG)...
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