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Functional Near Infrared Spectroscopy (fNIRS) has been proposed as a means to detect mental stress by measuring the concentration change of oxygenated hemoglobin (HbO) and deoxygenated hemoglobin (HbR) on the prefrontal cortex. In this study, simultaneous measurement of EEG-fNIRS on five healthy subjects was performed. We investigated the correlation between the hemodynamic responses and EEG alpha...
A person with sleep disorder such as apnea will stop breathing for a while during sleep. If frequently occurs, sleep disorder is dangerous for health. An early step for diagnosing apnea is by classifying the sleep stages during sleep. This study explores some shallow classifiers and their feasibility applied to sleep data. Recently, a sleep stages classification system that use deep unsupervised features...
Vigilance analysis associated with safe driving based on EEG has drawn considerable attention of researchers in recent years. Preventing traffic accidents caused by low level vigilance is highly desirable. This paper presents a novel vigilance analysis system by evaluating electroencephalographic (EEG) changes. EEG signals are preprocessed with independent component analysis to eliminate noise from...
The ability of the brain to categorize or group visual stimuli based on common features is a fundamental principle of cognition. This categorization is very fast and occurs in few millisecond time scales. Due to the excellent temporal resolution, on the order of millisecond, of the Electroencephalogram (EEG), categorization of images containing visual objects can be effectively recognized using Event...
Epilepsy is common disorder of the brain that affects the cerebral cortex. EEG is one of the main tools for diagnosis neuron activities and brain disorders. EEG signals store important information that is useful for diagnosis neural diseases. In this work, implementation of epilepsy classifier using neural network was employed. Identification of three classes EEG signals: seizure free, pre-seizure...
This brief presents a low-power, flexible, and multichannel electroencephalography (EEG) feature extractor and classifier for the purpose of personalized seizure detection. Various features and classifiers were explored with the goal of maximizing detection accuracy while minimizing power, area, and latency. Additionally, algorithmic and hardware optimizations were identified to further improve performance...
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...
This paper compares two supervised learning algorithms for predicting the sleep stages based on the human brain activity. The first step of the presented work regards feature extraction from real human electroencephalography (EEG) data together with its corresponding sleep stages that are utilized for training a support vector machine (SVM), and a fuzzy inference system (FIS) algorithm. Then, the...
Learning and memory are two related mental processes. EEG is a brain mapping technique, which can record brain states directly and can be used to assess learning and memory recall. In this paper, we will assess the effects of 2D and 3D educational contents on learning and memory recall by analyzing the brain states during recall tasks using EEG signals. 34 subjects learn same 2D and 3D educational...
In this paper, we have combined electroencephalography (EEG) and functional near-infrared spectroscopy (fNRIS) to make a hybrid EEG-NIRS based system for brain-computer interface (BCI). The EEG electrodes were placed on the motor cortex region and the NIRS optodes were set on the prefrontal region. The data of four subjects was acquired using mental arithmetic tasks and motor imageries of the left-...
Current interests in BCI development mainly derive from rehabilitation engineering, assistant control for normal people, entertainment, brain cognition, and so on. This paper has developed an online mobile robot control system by BCI based on motor imagery (MI). In the system, the user can give control command by 'thinking'. The EEG signals from the user are acquired by a biological signal acquisition...
Currently, sleep disorders are considered as one of the major human life issues. There are several stable physiological stages that the human brain goes through during sleep. Nowadays, many biomedical signals such as EEG, ECG, EMG, and EOG offer useful details for clinical setups that are used in identifying sleep disorders. In this work, we propose an efficient technique that could be implemented...
Epilepsy is a crucial neurological disorder in which patients experience epileptic seizure caused by abnormal electrical discharges from the brain. It is highly common in children and adults at the age of 65–70. Around 1 % of the world's population is affected by this disease. The mechanism of epilepsy is still incomprehensible to researchers; however, 80% of the seizure activity can be treated effectively...
For the BCI research to classify the different imagined movements of both left and right hands, a method using wavelet packet decomposition for feature extraction and using SVM for pattern classification was adopted. Firstly discusses the wavelet packet transform in depth and brings out an idea of taking wavelet packet coefficients' variance as feature into account, then extracts the feature serials...
Epileptic seizure prediction, with at least some minutes in advance, would improve substantially the quality of life of patients with refractory epilepsy. This is addressed as a classification problem were the brain state is classified using a number of features extracted from the EEG. Methods based on computational intelligence, like support vector machines (SVM), are applied to build up classifiers...
Clinical electroencephalography (EEG) is routinely used to monitor brain function in critically ill patients, and specific EEG waveforms are recognized by clinicians as signatures of abnormal brain. These pathologic EEG waveforms, once detected, often necessitate accute clinincal interventions, but these events are typically rare, highly variable between patients, and often hard to separate from background,...
This paper reports the investigations and experimental procedures conducted for designing an automatic sleep classification tool basedconly in the features extracted with wavelets from EEG, EMG and EOG (electro encephalo-mio- and oculo-gram) signals, without any visual aid or context-based evaluation. Real data collected from infants was processed and classified by several traditional and bio-inspired...
We present the first step towards a brain computer interface (BCI) for communication using real-time functional magnetic resonance imaging (fMRI). The subject in the MR scanner sees a virtual keyboard and steers a cursor to select different letters that can be combined to create words. The cursor is moved to the left by activating the left hand, to the right by activating the right hand, down by activating...
Human sleep is divided into two segments, Rapid Eye Movement (REM) sleep and Non-REM (NREM) sleep. NREM sleep is further divided into 4 stages. Sleep staging attempts to identify these stages based on the signals collected in PSG. Significant information can be derived from the EEG signals collected during PSG. Wavelet coefficients are extracted from EEG signals. In order to reduce the amount of data...
This paper dealt with the following topics: CT imaging; medical image processing; eye; neurophysiological disease; EEG; brain-computer interfaces; patient treatment; biomedical ultrasonics; respiratory motion; medical disorder; MRI; ultrasonic surgery; SVM; biomechanics; hidden Markov model; coronary angiography; and other related topics on medical image analysis.
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