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Auditory evoked potentials (AEPs) have been recorded at high stimulus rates during sleep using continuous loop averaging deconvolution (CLAD) sequences. AEP transient signals are obtained via frequency domain deconvolution of overlapped responses. Simultaneous acquisition of auditory brainstem response (ABR), middle latency response (MLR), and long latency response (LLR) is obtained at an average...
Localization of the cognitive activity in the brain is one of the major problems in neuroscience. Current techniques for neuro-imaging are based on functional magnetic resonance imaging (fMRI), positron emission tomography (PET), and event related potential (ERP) recordings. The highest temporal resolution is achieved by ERP, which is crucial for temporal localization of activities. However, the spatial...
Binocular rivalry is a visual perceptual phenomenon which occurs when two incongruent stimuli are viewed by a subject through each eye, but only one of them is perceived at a time, with a switch in perception every few seconds, which reflects the alternation of perceptual dominance. To investigate the correlation between contrast-related perception and neural activities, the subjects' EEGs were recorded...
Artifact removal is an essential part in electroencephalogram (EEG) recording and the raw EEG signals require preprocessing before feature extraction. In this work, we implemented three filtering methods and demonstrated their effects on the performance of different classifiers. Bandpass digital filtering, median filtering and facet method are three preprocessing approaches investigated in this paper...
In this paper, autoregressive modeling technique and neural network based modeling techniques are used to model and simulate electroencephalogram (EEG) signals. EEG signal modeling is used as a tool to identify pathophysiological EEG changes potentially useful in clinical diagnosis. The normal, background and epileptic EEG signals are modeled and the dynamical properties of the actual and modeled...
The issue of subject-specific parameter selection in an electroencephalogram (EEG)-based brain-computer interface (BCI) is tackled in this paper. Hjorth- and Barlow-based feature extraction procedures (FEPs) are investigated along with linear discriminant analysis (LDA) for classification. These are well-known nonparametric FEPs but their simplicity prevents them from matching the performance of more...
In the analysis of epileptic electroencephalographic (EEG) and magnetoencephalography (MEG) data, spike separation is diagnostically important because localization of epileptic focus often depends on accurate extraction of spiky activity from the raw data. In this paper, we present a method to automatically extract spikes using the wavelet transform combined with morphological filtering based on a...
An epileptic seizure detector's performance definitely depends on features extraction and selection. In this study, we present the short-time average magnitude difference function (sAMDF) as a computationally efficient feature to distinguish seizures from EEG and it is compared with the frequently used curve length. We also suggest using a subspace based approach for feature selection that exploits...
Epileptic patients often show interictal epileptic discharges (IED's) in the electroencephalogram (EEG) recorded between seizures. This epileptiform activity is in many cases related to the location of the seizure onset, and is believed to reflect the frequency of the seizures. We present a fully automated technique that is able to extract the IED's from the EEG, despite the obscuring artifacts. The...
A graphical user interface (GUI) implementing a novel technique of fast estimation of steady state auditory evoked potentials (SSAEPs) for rapid assessment of the functionality of the human auditory nervous system is presented. The proposed signal estimator has shown great promise in the fast extraction of weak signals buried under large amounts of noise such as the case with SSAEP signals. Currently,...
Eye movements and blinks may produce unusual voltage changes that propagates from the eyeball through the head as volume conductor up to the scalp electrodes, generating severe electroencephalographic artifacts. Several methods are now available to correct the distortion induced by these events on the EEG, having different advantages and drawbacks. The main focus of this work is to quantify the performance...
The issue of subject-specific parameter selection in an electroencephalogram (EEG)-based brain-computer interface (BCI) is tackled in this paper. Hjorth- and Barlow-based feature extraction procedures (FEPs) are investigated along with linear discriminant analysis (LDA) for classification. These are well-known nonparametric FEPs but their simplicity prevents them from matching the performance of more...
Auditory evoked potentials (AEPs) have been recorded at high stimulus rates during sleep using continuous loop averaging deconvolution (CLAD) sequences. AEP transient signals are obtained via frequency domain deconvolution of overlapped responses. Simultaneous acquisition of auditory brainstem response (ABR), middle latency response (MLR), and long latency response (LLR) is obtained at an average...
In this paper, autoregressive modeling technique and neural network based modeling techniques are used to model and simulate electroencephalogram (EEG) signals. EEG signal modeling is used as a tool to identify pathophysiological EEG changes potentially useful in clinical diagnosis. The normal, background and epileptic EEG signals are modeled and the dynamical properties of the actual and modeled...
The goal of this paper is to improve on single-trial classification of electro-encephalography (EEG) using linear methods. The paper proposes to combine the classification of the spatial distribution of activity with the classification of its temporal profile. The work is based on the idea that a current source in the brain has a reproducible temporal profile with a static spatial projection to the...
Epileptic patients often show interictal epileptic discharges (IED's) in the electroencephalogram (EEG) recorded between seizures. This epileptiform activity is in many cases related to the location of the seizure onset, and is believed to reflect the frequency of the seizures. We present a fully automated technique that is able to extract the IED's from the EEG, despite the obscuring artifacts. The...
Localization of the cognitive activity in the brain is one of the major problems in neuroscience. Current techniques for neuro-imaging are based on functional magnetic resonance imaging (fMRI), positron emission tomography (PET), and event related potential (ERP) recordings. The highest temporal resolution is achieved by ERP, which is crucial for temporal localization of activities. However, the spatial...
Electroencephalogram (EEG) recordings are highly susceptible to noise from electromyogram (EMG) signals of the frontalis and temporalis muscles. In this paper, we propose and evaluate a new method for detecting frontalis and temporalis muscle EMG contamination in EEG signals based on recent findings on topographic and spectral characteristics of cranial EMG
An epileptic seizure detector's performance definitely depends on features extraction and selection. In this study, we present the short-time average magnitude difference function (sAMDF) as a computationally efficient feature to distinguish seizures from EEG and it is compared with the frequently used curve length. We also suggest using a subspace based approach for feature selection that exploits...
Brainstem auditory evoked responses (BAER) are transient signals embedded in the EEG recorded from scalp electrodes, when a subject is presented with a series of acoustic clicks. These signals typically have a signal-to-noise ratio (SNR) well below -10 dB. The extraction of BAER signals from the EEG for the purpose of automatically computing features of interest from the BAER waveform(s) is described...
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