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In this paper, a new model of focal and non-focal electroencephalography classification is carried out using a deep neural network (DNN). The Convolution Architecture For Feature Extraction (Caffe) framework with three different models (LeNet, AlexNet, and GoogLeNet) are applied, where the DNN is trained with different training epoch values (TEs). The performance of discriminating the focal and non-focal...
Attention Deficit Hyperactivity Disorder (ADHD) and Attention Deficit Disorders (ADD) are two of the most spread mental disorders characterized by the lack of attention and focus. One way to measure focus is through Electroencephalogram (EEG) signals that can be read using the new wireless EEG reading devices often used by Brain-computer Interface (BCI) researchers. In parallel, serious games have...
This paper presents an automated method for seizure detection in EEGs using an increment entropy (IncrEn) and support vector machines (SVMs). The IncrEn is a measure of the complexity of time series, which characterizes both the permutation of values and the temporal order of values. The IncrEn is used to extract features of epileptic EEGs and normal EEGs. The SVMs are employed to classify seizure...
Attention Deficient Hyperactivity Disorder (ADHD) is a neurological condition characterized by cognitive task difficulty, impulsivity/hyperactivity and loss of focus. Neurofeedback (NF) therapy has emerged as a promising treatment for ADHD, where a Brain-Computer Interface (BCI) employing electroencephalography (EEG) is used. This paper presents a review of 23 studies on BCI-based ADHD treatment;...
EEG PSD is often used as a measure of cognitive function and emotional state while MI is a reflection of functional connectivity in the brain. Analysis of both quantities in a child with severe disabilities suggests that PMT may be associated with an increase in cognitive performance, as well as connectivity between the frontal and parietal lobes.
In this project, a conceptual design of a robotic exoskeleton for the neurological rehabilitation of temporomandibular disorder (TMD) is presented. Here, a PC based GUI interface and EMG and EEG based feedback system is used. The proposed system presents a lightweight, portable solution aimed at promoting user engagement in the rehabilitation process. The robotic exoskeleton provides a method of delivering...
Event-related potentials (ERP)s are electrophysiological responses that are commonly used for detecting the brain response to external stimuli. In this paper, we propose to use the sparse common component and innovations model (SCCI) to extract ERPs from multiple EEG signals recorded across closely located electrodes. This model finds the sparse representation of the common component of the signals...
Previous works show that the electrocardiogram is a promising signal to be used as a biometric trait. The nonlinear methods for computing the dynamical properties of ECG signal, have been previously used. Since each of the large scale features of recurrence plots of ECG is related quite simply to time-domain features, they can provide good result in biometric system. In this paper we apply Rescaled...
This paper introduces a pre-training technique for learning discriminative features from electroencephalography (EEG) recordings using deep neural networks. EEG data are generally only available in small quantities, they are high-dimensional with a poor signal-to-noise ratio, and there is considerable variability between individual subjects and recording sessions. Similarity-constraint encoders as...
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...
Emotion classification and recognition from electroencephalogram (EEG) signals have been studied extensively due to its potential benefits such as entertainment and health care. Concerning classification, various techniques have been developed and applied. Support Vector Machines (SVMs) has been reported as the most used because of its accuracy. Nevertheless, although SVMs has satisfactory performance,...
Smart home technology can improve the quality of life for inhabitants of homes. This technology allows the inhabitants to monitor their home and any electrical device locally or remotely via computerized central control. Despite the advances in the smart home technologies, people with disabilities - particularly those with quadriplegia, - will not be able to utilize the currently available techniques...
In brain-computer interface (BCI) research, there must be a trade-off between accuracy and speed of the BCI system, especially those based on event-related potentials (ERPs). This paper proposes a novel method which can significantly increase the spelling bit rate while also maintaining the desired accuracy. We provide an adaptive real-time stopping method based on the scores of ensemble support vector...
Reconstruction of a hand’s motion with electroencephalography (EEG) signals is a challenging problem that has not been solved yet. Most related studies rely on a motion tracking system to record a sequence of hand coordinate values paired with biosignals, in order to train a mapping function between them. For amputees, this approach is not possible. There are also only a few studies about how different...
The problem of creating a personalized seizure detection algorithm for newborns is tackled in this paper. A probabilistic framework for semi-supervised adaptation of a generic patient-independent neonatal seizure detector is proposed. A system that is based on a combination of patient-adaptive (generative) and patient-independent (discriminative) classifiers is designed and evaluated on a large database...
In recent days, the classification of abnormal brain Electroencephalogram (EEG) signals is a demanding and challenging task. For this purpose, some of the classification techniques which include Discrete Wavelet Transform (DWT), Discrete Cosine Transform (DCT) and Fast Fourier Transform (FFT) are frequently used in the existing works. But, it has some drawbacks such as, the above mentioned techniques...
Brain Computer Interfaces (BCIs) stand as a promising new technology to enrich interaction with objects in both the physical and virtual world. As BCI technology continues to mature for possibly becoming a essential part of the future communication and interaction with people and objects, more attention is needed for effectively and accurately authenticating the very user of the input device. We attempt...
As of today, ADHD is diagnosed through subjective evaluation of symptoms, which overlap with those of other conditions. To address this issue and increase the accuracy of diagnoses, quantitative methods for the diagnosis of ADHD have been developed in the last 20 years. Recently, the US FDA approved the use of one of these methods, the θ/β power ratio (TBPR), in a device intended to support the diagnosis...
Immersive, head-mounted virtual reality (HMD-VR) can be a potentially useful tool for motor rehabilitation. However, it is unclear whether the motor skills learned in HMD-VR transfer to the non-virtual world and vice-versa. Here we used a well-established test of skilled motor learning, the Sequential Visual Isometric Pinch Task (SVIPT), to train individuals in either an HMD-VR or conventional training...
Brain-Computer Interface (BCI) research hopes to improve the quality of life for people with severe motor disabilities by providing a capability to control external devices using their thoughts. To control a device through BCI, neural signals of a user must be translated to meaningful control commands using various machine learning components, e.g. feature extraction, dimensionality reduction and...
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