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This paper presents new class of time-frequency (T-F) features for automatic detection and classification of epileptic seizure activities in EEG signals. Most previous methods were based only on signal features derived from the instantaneous frequency and energies of EEG signals in different spectral sub-bands. The proposed features based on image descriptors are extracted from the T-F representation...
In electroencephalogram (EEG) based biometrics, the determination of the right channel set helps improve accuracy and usability, while reducing the required number of electrodes and hence the complexity and cost of the EEG system. In this work we find a reduced set of channels designed to enhance human authentication accuracy regardless of changes in the mental task. The study shows that the resulting...
This paper presents a novel image classification method based on integration of EEG and visual features. In the proposed method, we obtain classification results by separately using EEG and visual features. Furthermore, we merge the above classification results based on a kernelized version of Supervised learning from multiple experts and obtain the final classification result. In order to generate...
Recent work has demonstrated the feasibility of extracting semantic categories directly from cortical measures (e.g., electroencephalography, EEG) during receptive tasks. Here, we automatically classify speech stimuli as either synonymous or non-synonymous with a prior prime in a speech-receptive task given only EEG data with up to 86.84% accuracy. An analysis of variance reveals no significant difference...
Brain-computer interfaces (BCI) based on steady-state-visual-evoked-potentials (SSVEP) offer higher information throughput and require shorter calibration periods than other BCI modalities. SSVEPs are oscillatory responses elicited by oscillatory visual stimuli (e.g. using flickering LEDs) that can be detected in the electroencephalogram (EEG). The SSVEP is more prominent in occipital sites and consists...
A classification system for EEG signals using wavelet decomposition to form the feature vectors is developed. Single-trial analysis loses the benefit of averaging to remove non-task related brain activity and makes it more difficult to pick out key features determining the execution of a task. Wavelet analysis is used here to localise the event-related desynchronization of voluntary movement. Classification...
Brain-Computer Interfaces are an interesting emerging technology that translates intentional variations in the Electroencephalogram (EEG) into a set of particular commands in order to control a real world machine. For this purpose it is necessary to classify EEG signals correlated with various physical or mental activities. Most of the work in BCI research is devoted to increase the accuracy of the...
This paper presents a brain-computer interface (BCI) in which the face paradigm was optimized for the visual mismatch negativity (MMN). There were 12 cells in a LCD monitor. A single letter was at the bottom of each cell. In the new paradigm, a color face appeared above each of the 12 cells randomly while the gray faces appeared in others 11 cells. A traditional face paradigm with single character...
Brain machine interface (BMI) devices facilitate communication and control of computers using signals measured from within the brain of the operators. These signals are detected using electroencephalography (EEG) devices. Research in this field aims to enable victims of ‘locked-in syndrome’ as a result of amyotrophic lateral sclerosis, spinal injury, cerebral palsy, muscular dystrophies, or multiple...
Current work in identifying statistical features of EEG response to color stimuli suggests the possibility of classification of EEG frequency responses with relation to color. In this study, we used Independent Component Analysis (ICA) to isolate color related responses from other background scene related responses and a Support Vector Machines (SVM) to classify these color related responses per colors...
We aim to develop a brain-machine interface (BMI) system that estimates user's gaze or attention on an object to pick it up in the real world. In Experiment 1 and 2 we measured steady-state visual evoked potential (SSVEP) using luminance and/or contrast modulated flickers of photographic scenes presented on a head-mounted display (HMD). We applied multiclass SVM to estimate gaze locations for every...
Electrical brain activities can be measured noninvasively using electroencephalogram (EEG). This electric signal changes for different tasks, and also changes from subject to subject. Previous studies have shown that the EEG signal is unique enough to be used as a biometric characteristic. However, it is well known that the brain activity can change according to our emotion or stress status, among...
EEG brainwaves have recently emerged as a promising biometric that can be used for individual identification, since those signals are confidential, sensitive, and hard to steal and replicate. In this study, we propose a new stimulidriven, non-volitional brain responses based framework towards individual identification. The non-volitional mechanism provides an even more secure way in which the subjects...
The growth of wireless body area sensor networks (WBASNs) has led the way to advancements In healthcare applications and patient monitoring systems; epileptic seizure lies at the heart of these promising technologies. For real-time epileptic seizure detection, wireless EEG sensors have been utilized for the purpose of data acquisition, pre-processing and transmission to the server side. The dilemma...
The behavior of many physical and biological processes and systems can be described satisfactorily by fractional order models. A new method, termed fractional linear prediction (FLP) based on fractional calculus, is used to model ictal and seizure-free EEG signals. Through numerical simulations it is demonstrated that, the EEG signal can be modeled accurately, by using a few integrals of fractional...
Emotion is a complex set of interactions among subjective and objective factors governed by neural/hormonal systems resulting in the arousal of feelings and generate cognitive processes, activate physiological changes such as behavior. Emotion recognition can be correctly done by EEG signals. Electroencephalogram (EEG) is the direct reflection of the activities of hundreds and millions of neurons...
In this paper, the classification of epileptic and non-epileptic events from multi-channel EEG data is investigated using a large number of time and frequency domain features. In contrast to most of the evaluations found in the literature, in this paper the non-epileptic class consists of two types of paroxysmal episodes of loss of consciousness namely the psychogenic non epileptic seizure (PNES)...
In recent years, EEG-based technology has become more popular in producing variety of BMI protocols for wheel chair navigation and communication systems. In this research work, as an initial step towards the development of an intelligent navigation system with a communication aid, a simple EEG data capturing procedure has been introduced using visually evoked potentials. A simple, visually evoked...
Although human cognition often occurs while moving, most studies of the dynamics of the human brain examine subjects while static and seated in a highly controlled laboratory. EEG signals have been considered to be too noisy to record brain dynamics during human locomotion. Here, we present a real-time ambulatory brain computer interface which allows us to detect gait phases and remove motion-related...
EEG based upper limb rehabilitation has limitation on the control commands of neuro-prosthetics cannot deal with human's real movements. To resolve this problem, it is important to know about neural correlation of the directions of arm movement. Previous studies classified the directions of arm movement, using center-out task, only including y-z-axis movement. In this research, 4 subjects participated...
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