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Motivated by the fact that modeling and representation of multi-class signal patterns plays a critical role in Electroencephalogram (EEG)-based brain computer interface (BCI) systems, the paper proposes the coupling of error correction output coding (ECOC) with the common spatial pattern (CSP) analysis. Referred to as the ECO-CSP framework, the ECOC approach is applied to EEG motor imagery classification...
Nonconvulsive status epilepticus (NCSE) is observed when the patient undergoes a persistent electroencephalographic epileptic episode without physical symptoms. This condition is commonly found in critically ill patients from intensive care units and constitutes a medical emergency. This paper proposes a method to detect nonconvulsive epileptic seizures (NCES). To perform the NCES detection the electroencephalogram...
Joint analysis of neuroimaging data from multiple modalities has the potential to improve our understanding of brain function since each modality provides complementary information. In this paper, we address the problem of jointly analyzing functional magnetic resonance imaging (fMRI), structural MRI (sMRI) and electroencephalography (EEG) data collected during an auditory oddball (AOD) task with...
EEG signals, recording both normal and abnormal activities of neurons in the brain, are widely used in epilepsy detection. In this paper, an EEG signal classification method based on Slantlet Transform and sparse coding is proposed to greatly reduce number of false alarms and improve speed of detection. With Slantlet Transform, salient information of EEG signals is mapped into a sparse space. In order...
Classification of EEG signal involved in a particular cognitive activity has found many application in brain-computer interface (BCI). In specific, use of classification algorithms to highly multivariate non-stationary recordings like EEG is a challenging and promising task. This study investigated two sub-stantial novelty of the topics, (1) Distinction between meditation (Kriya Yoga) and non-meditation...
In this paper, we study an Electroencephalography (EEG) based biometric authentication system with privacy protection. We use motor imagery EEG, recorded using a wearable wireless device, as our biometric modality. To obtain EEG-based authentication keys we employ the fuzzy-commitment like scheme with soft-information at the decoder, see Ignatenko and Willems [2014]. In this work we study the effect...
In this paper we investigate the performance of electroencephalographic (EEG) signals, elicited by means of visual stimuli, for biometric identification. A deep learning method such as convolutional neural network (CNN), is used for automatic discriminative feature extraction and individual identification. Experiments are performed on a longitudinal database comprising of EEG data acquired from 40...
EMOEEG is a multimodal dataset where physiological responses to both visual and audiovisual stimuli were recorded, along with videos of the subjects, with a view to developing affective computing systems, especially automatic emotion recognition systems. The experimental setup involves various physiological sensors, among which electroencephalographic sensors. The experiment is performed with 8 participants,...
Classification of time-series data is a challenging problem with many real-world applications, ranging from identifying medical conditions from electroencephalography (EEG) measurements to forecasting the stock market. The well known Bag-of-Features (BoF) model was recently adapted towards time-series representation. In this work, a neural generalization of the BoF model, composed of an RBF layer...
There are many combined signal processing applications such as the joint processing of EEG (Electroencephalogram) and MEG (Magnetoencephalogram) data that can benefit from coupled CP (Canonical Polyadic) tensor decompositions. The coupled CP decomposition jointly decomposes tensors that have at least one factor matrix in common. The C-SECSI (Coupled — Semi-Algebraic framework for approximate CP decomposition...
In the two process sleep model, the rate of sleep need dissipation is proportional to slow wave activity (SWA; EEG power in the 0.5 to 4 Hz band). The dynamics of sleep need dissipation are characterized by two parameters (the initial sleep need So and the decay rate γ) that can be calculated from SWA values in NREM sleep. The goal in this paper is to use a neural network classifier to automatically...
Hearing prostheses have built-in algorithms to perform acoustic noise reduction and improve speech intelligibility. However, in a multi-speaker scenario the noise reduction algorithm has to determine which speaker the listener is focusing on, in order to enhance it while suppressing the other interfering sources. Recently, it has been demonstrated that it is possible to detect auditory attention using...
Simultaneous recording of electroencephalographic (EEG) signals and functional magnetic resonance images (fMRI) has gained wide interest in brain research, thanks to the highly complementary spatiotemporal nature of both modalities. We propose a novel technique to extract sources of neural activity from the multimodal measurements, which relies on a structured form of coupled matrix-tensor factorization...
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