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In this letter, the authors propose a new entropy measure for analysis of time series. This measure is termed as the state space correlation entropy (SSCE). The state space reconstruction is used to evaluate the embedding vectors of a time series. The SSCE is computed from the probability of the correlations of the embedding vectors. The performance of SSCE measure is evaluated using both synthetic...
Epilepsy is a chronic neurological disorder which occurs due to the recurring evoking of seizure which results due to the abnormal rhythmic discharge of electrical activities of the brain. This fluctuation in the electrical activities of the brain can be analyzed using EEG signal which provides valuable information about the physiological states of the brain. In this paper we propose an efficient...
P300 speller is a traditional brain computer interface paradigm and focused by lots of current BCI researches. In this paper a support vector machine based recursive feature elimination method was adapted to select the optimal channels for character recognition. The margin distance between target and nontarget stimulus in feature space was evaluated by training SVM classifier and then the features...
Recently, the research on Brain-Computer Interface (BCI) technology has achieved great progress, and the BCI system based on Motor Imagery (MI) has been intensively studied in many labs. The essential part of signal processing in BCI is how to extract the MI features in electroencephalographic (EEG) and recognize the MI task accurately. One challenge lies in that EEG signals are non-stationary, whose...
Driving fatigue is the most dangerous killer on the highway. Supervising mental vigilance is able to warn the driver and avoid some disasters. The current study mainly focuses on the power spectrum. The electroencephalography (EEG) activities in the δ(0-4 Hz), θ(4-8 Hz), α(8-13 Hz) and β(13-35Hz) bands, reflect the change of the physiological vigilance. The ratios of (θ + α)/β, α/β, (θ + α)/(α + β),...
We investigate the potential of using electrical brainwave signals during imagined speech to identify which subject the signals originated from. Electroencephalogram (EEG) signals were recorded at the University of California, Irvine (UCI) from 6 volunteer subjects imagining speaking one of two syllables, /ba/ and /ku/, at different rhythms without performing any overt actions. In this work, we assess...
This paper proposes an emotional stress recognition system with EEG signals using higher order spectra (HOS). A visual induction based acquisition protocol is designed for recording the EEG signals in five channels (FP1, FP2, T3, T4 and Pz) under two emotional stress states of participants, Calm neutral and Negatively exited. After pre-processing the signals, higher order spectra are employed to extract...
Emotion perception similar to thinking, learning and remembering is consequent of complicated brain processes which are related to specific biological metabolism. Different human's emotional states are recognizable by measuring and interpreting of human physiological signals. Bio-sensors possess a number of advantages against other emotion recognition methods as they are relatively more consistent...
This paper presents the satellite television remote control system based on brain-computer interface. The Brain Controlled Satellite Television Remote System (BCSTRS) is a real time system that can help the patients suffering from Amyotrophic Lateral Sclerosis (ALS) to select TV channels or adjust volume using their brain waves. In this paper we propose an algorithm including data acquisition and...
An automatic alarm system for detecting epileptic seizure onsets could be of great assistance to patients and medical staff. A novel approach is proposed using the Matching Pursuit algorithm as a feature extractor combined with the Support Vector Machine (SVM) as a classifier for this purpose. The combination of Matching Pursuit and SVM for automatic seizure detection has never been tested before,...
This paper examines whether an appropriate algorithm, developed for use with neonatal data, could also be used, without alteration, for the detection of seizures in adults with epilepsy. The performance of a feature extraction and SVM classifier system is evaluated on databases of 17 neonatal patients and 15 adult patients. Mean ROC curve areas of 0.96 and 0.94 for neonatal and adult databases respectively...
This work presents a multi-channel patient-independent neonatal seizure detection system based on the SVM classifier. Several post-processing steps are proposed to increase temporal precision and robustness of the system and their influence on performance is shown. The SVM-based system is evaluated on a large clinical dataset using several epoch-based and event based metrics and curves of performance...
Approximately 300,000 Americans suffer from epilepsy but no treatment currently exists. A device that could predict a seizure and notify the patient of the impending event or trigger an antiepileptic device would dramatically increase the quality of life for those patients. A patient-specific classification algorithm is proposed to distinguish between preictal and interictal features extracted from...
Although brain activation in relation to tactile stimulation is well studied, the central processing of texture coding in humans is poorly understood. To explore such dynamics, we used a robotic setup to produce well-controlled stimuli consisting of two different textures (gratings of spatial period combinations 520+1920 mum and 400+1920 mum) which were moved across a subject's finger pad during electro-encephalograpy...
In order to classify the mental tasks in brain-computer interfaces(BCI), a feature extraction method based on morphological pattern spectrum is here proposed. Flat morphological structure element is selected according to the characteristics of electroencephalography(EEG) and morphological features of different scales are obtained with pattern spectrum. Then, support vector machines(SVM) is used as...
To diagnose the structural disorders of brain, electroencephalography (EEG) is routinely used for observing the epileptic seizures in neurology clinics, which is one of the major brain disorders till today. In this work, we present a new, EEG-based, brain-state identification method which could form the basis for detecting epileptic seizure. We aim to classify the EEG signals and diagnose the epileptic...
Artificial emotion study will be of utmost importance in future artificial intelligence research. In this paper, an emotion understanding system based on brain activity and ldquoGISTrdquo is newly proposed to categorize emotions reflected by natural scenes. According to the strong relationship of human emotion and the brain activity, functional magnetic resonance imaging (fMRI) and electroencephalography...
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