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Approximately 50 million people in the world suffer from epileptic seizures. Reliable early seizure detection could bring significantly beneficial therapeutic alternatives. In recent decades, most approaches have relied on scalp EEG and intracranial EEG signals, but practical early detection for closed-loop seizure control remains challenging. In this study, we present preliminary analyses of an early...
The research presented in this article investigates the influence of subliminal prime words on peoples' judgment about images, through electroencephalograms (EEGs). In this cross domain priming paradigm, the participants are asked to rate how much they like the stimulus images, on a 7-point Likert scale, after being subliminally exposed to masked lexical prime words, with EEG recorded simultaneously...
The extraction of intended kinetic information from an EEG signal can have several applications related to the rehabilitation for subjects with various neurological disorders. However, the task is mainly constrained by the low signal-to-noise ratio for the EEG signals. It is well known that the cortical activity takes place at a very low frequency since it is characterized by the dropping of movement...
In this paper we examined the robustness of a feature-set based on time-frequency distributions (TFDs) for neonatal EEG seizure detection. This feature-set was originally proposed in literature for neonatal seizure detection using a support vector machine (SVM). We tested the performance of this feature-set with a smoothed Wigner-Ville distribution and modified B distribution as the underlying TFDs...
Over 50 million persons worldwide are affected by epilepsy. Epilepsy is a brain disorder known for sudden, unexpected transitions from normal to pathological behavioral states called epileptic seizures. Epilepsy poses a significant burden to society due to associated healthcare cost to treat and control the unpredictable and spontaneous occurrence of seizures. There is a need for a quick screening...
Mild cognitive impairment (MCI) is a neurological condition that is often the early stage of Alzheimer's disease (AD). This pilot study explores event-related multiscale entropy (MSE) measures as features for effectively discriminating between normal aging, MCI, and AD participants. Thirty two-channel scalp EEG records recorded during a working memory task from 43 age-matched participants (mean age...
Identifying artifacts in EEG data produced by the neurons in brain is an important task in EEG signal processing research. These artifacts are corrected before further analyzing. In this work, fast fixed point algorithm for Independent Component Analysis (ICA) is used for removing artifacts in EEG signals and principal component analysis (PCA) tool is used for reducing high dimensional data and spatial...
In this research we propose to use EEG signal to classify two emotions (i.e., positive and negative) elicited by pictures. With power spectrum features, the accuracy rate of SVM classifier is about 85.41%. Considering each pair of channels and different frequency bands, it shows that frontal pairs of channels give a better result than the other area and high frequency bands give a better result than...
This paper describes the method for classifying multiclass motor imagery EEG signals of brain-computer interfaces (BCIs) according to the phenomena of event-related desynchronization and synchronization (ERD/ERS). The method of one-versus-one common spatial pattern (CSP) for multiclass feature extraction was employed. And we extended two different kinds of classifiers: 1) support vector machines (SVM)...
Motor imagery based brain-computer interface (BCI) translates subject's motor intention into a control signal through electroencephalogram (EEG) pattern classification. In this paper, a large margin nearest neighbor (LMNN) method is applied for the classification of multi-class BCI based on motor imagery. The main idea of LMNN is to learn a Mahalanobis distance that tries to collapse examples in the...
Epilepsy is a common chronic neurological disorder characterized by recurrent unprovoked seizures. The electroencephalogram (EEG) signals play an important role in the diagnosis of epilepsy. In addition, multi-channel EEG signals have much more discrimination information than a single channel. However, traditional recognition algorithms of EEG signals are lack of multichannel EEG signals. In this...
Noninvasive electroencephalography (EEG) brain computer interface (BCI) systems are used to investigate intended arm reaching tasks. The main goal of the work is to create a device with a control scheme that allows those with limited motor control to have more command over potential prosthetic devices. Four healthy subjects were recruited to perform various reaching tasks directed by visual cues....
The EEG signal is very often contaminated by electrical activity external to the brain. These artefacts make the accurate detection of epileptiform activity more difficult. A scheme developed to improve the detection of these artefacts (and hence epileptiform event detection) is introduced. A structure of parallel Support Vector Machine classifiers is assembled, one classifier tuned to perform the...
Brain-computer interface (BCI) can provide communication channels which do not depend on peripheral nerves and muscles for patients with neuromuscular disorders. The goal of the paper is to validate signal processing and classification methods for Brain-Computer Interfaces (BCIs). This paper presented a method combining wavelet with Common Spatial Pattern (CSP). Use multi-resolution analysis (MRA)...
In this paper, feature selection was carried out for multi-intelligence classification, and finds key regions. We designed different multi-intelligence tasks with BCI. SVM was used to classify and select features. The experiment reveals that a band has a greater effect on imagery intelligent tasks. And the introduced feature selection algorithm succeeded to detect key regions for multi-intelligence...
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...
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