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OBJECTIVE: This research explores absence seizures using data recorded from different layers of somatosensory cortex of four genetic absence epilepsy rats from Strasbourg (GAERS). Localizing the active layers of somatosensory cortex (spatial analysis) and investigating the dynamics of recorded seizures (temporal analysis) are the main goals of this research. APPROACH: We model the spike discharges of seizures using a generative spatio-temporal model. We assume that there are some states under first-order Markovian model during seizures, and each spike is generated when the corresponding state is activated. We also assume that a few specific epileptic activities (or atoms) exist in each state which are linearly combined and form the spikes. Each epileptic activity is described by two characteristics: (1) its spatial topography which shows the organization of current sources and sinks generating the epileptic activity, and (2) its temporal representation which illustrates the activation function of the epileptic activity. We show that the estimation of the model parameters, i.e. states and their epileptic activities (atoms), is similar to solving a dictionary learning problem for sparse representation. Instead of using classical dictionary learning algorithms, a new approach, taking into account the Markovian nature of the model, is proposed for estimating the models parameters, and its efficiency is experimentally verified. MAIN RESULTS: Experimental results show that there are one dominant and one unstable state with two epileptic activities in each during the seizures (temporal analysis). It is also found that the top and bottom layers of the somatosensory cortex are the most active layers during seizures (spatial analysis). The structural model is similar for all rats with a spatial topography which is the same for all rats but a temporal activation which changes according to the rat. SIGNIFICANCE: The proposed framework can be applied on any database acquired from a small area of the brain, and can provide valuable spatio-temporal analysis for neuroscientists.
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Aprendizaje Automático , Convulsiones/fisiopatología , Fenómenos Electrofisiológicos , Humanos , Modelos Neurológicos , Convulsiones/diagnóstico , Análisis Espacio-TemporalRESUMEN
This work investigates the use of mixed-norm regularization for sensor selection in event-related potential (ERP) based brain-computer interfaces (BCI). The classification problem is cast as a discriminative optimization framework where sensor selection is induced through the use of mixed-norms. This framework is extended to the multitask learning situation where several similar classification tasks related to different subjects are learned simultaneously. In this case, multitask learning helps in leveraging data scarcity issue yielding to more robust classifiers. For this purpose, we have introduced a regularizer that induces both sensor selection and classifier similarities. The different regularization approaches are compared on three ERP datasets showing the interest of mixed-norm regularization in terms of sensor selection. The multitask approaches are evaluated when a small number of learning examples are available yielding to significant performance improvements especially for subjects performing poorly.
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Mapeo Encefálico/métodos , Encéfalo/fisiología , Algoritmos , Área Bajo la Curva , Inteligencia Artificial , Interfaces Cerebro-Computador , Simulación por Computador , Potenciales Evocados , Humanos , Modelos Estadísticos , Reproducibilidad de los Resultados , Programas InformáticosRESUMEN
In many machine learning applications, like brain-computer interfaces (BCI), high-dimensional sensor array data are available. Sensor measurements are often highly correlated and signal-to-noise ratio is not homogeneously spread across sensors. Thus, collected data are highly variable and discrimination tasks are challenging. In this work, we focus on sensor weighting as an efficient tool to improve the classification procedure. We present an approach integrating sensor weighting in the classification framework. Sensor weights are considered as hyper-parameters to be learned by a support vector machine (SVM). The resulting sensor weighting SVM (sw-SVM) is designed to satisfy a margin criterion, that is, the generalization error. Experimental studies on two data sets are presented, a P300 data set and an error-related potential (ErrP) data set. For the P300 data set (BCI competition III), for which a large number of trials is available, the sw-SVM proves to perform equivalently with respect to the ensemble SVM strategy that won the competition. For the ErrP data set, for which a small number of trials are available, the sw-SVM shows superior performances as compared to three state-of-the art approaches. Results suggest that the sw-SVM promises to be useful in event-related potentials classification, even with a small number of training trials.
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Electroencefalografía/instrumentación , Electroencefalografía/métodos , Máquina de Vectores de Soporte , Interfaz Usuario-Computador , Algoritmos , Encéfalo/fisiología , Mapeo Encefálico , Electroencefalografía/clasificación , Procesamiento Automatizado de Datos , Potenciales Relacionados con Evento P300 , Humanos , Modelos Lineales , Procesos Mentales , Dinámicas no Lineales , Lectura , Reproducibilidad de los Resultados , Relación Señal-RuidoRESUMEN
It is not well established whether seizures and epilepsy after an ischaemic stroke increase the disability of patients. Seventy-two patients with delayed seizures after a hemispheric infarct (37 with a single seizure and 35 with epilepsy) were included in the study. The modified Rankin scale was used to compare disability of the patients at 1 month after stroke and at 2 weeks after single or the last seizure, in case of epilepsy. The size of the X-ray hypoattenuation zone was compared on computed tomographic (CT) scans, performed in the weeks after the stroke and 1 week after single or repeated seizures. Lesion size was determined by superimposing the CT slices on digital cerebral vascular maps, on which the contours of the infarct area were delineated. The extent of the infarcts was expressed as the percentage fraction of the total surface area of the cerebral hemisphere. Groups with a single seizure and with epilepsy were mutually compared. Infarcts predominated in the parieto-temporal cortical regions. In the overall group the median Rankin score worsened significantly after seizures. The average size of the X-ray hypoattenuation zone was also significantly increased on the CT scans after the seizures, compared with those after stroke, without clear evidence of recent infarction. Mutual comparison of patients with a single seizure episode and of those with epilepsy showed only a trend of more severe disability and of increase in lesion size in the post-stroke epilepsy group. Delayed seizures and epilepsy after ischaemic stroke are accompanied by an increase in lesion size on CT and by worsening of the disability of the patients. This study does not allow to determine whether this is due to stroke recurrence or due to additional damage as a result of the seizures themselves.