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Epileptic seizure detection has gained increasing attention in clinical therapy. Scalp electroencephalogram (EEG) analysis is a common way to capture brain abnormality for seizure onset detection. This paper presents a novel context-learning based approach using multi-feature fusion to compensate for incomplete description of single feature in epileptic EEG signals. First, EEG scalogram sequence is...
This study presents a novel end-to-end architecture that learns hierarchical representations from raw EEG data using fully convolutional deep neural networks for the task of neonatal seizure detection. The deep neural network acts as both feature extractor and classifier, allowing for end-to-end optimization of the seizure detector. The designed system is evaluated on a large dataset of continuous...
Distinctive EEG signals from the motor and somatosensory cortex are generated during mental tasks of motor imagery (MI) and somatosensory attentional orientation (SAO). In this paper, we hypothesize that a combination of these two signal modalities provides improvements in a brain–computer interface (BCI) performance with respect to using the two methods separately, and generate novel types of multi-class...
Recently, SSVEP detection from EEG signals has attracted the interest of the research community, leading to a number of well-tailored methods, such as Canonical Correlation Analysis (CCA) and a number of variants. Despite their effectiveness, due to their strong dependence on the correct calculation of correlations, these methods may prove to be inadequate in front of potential deficiency in the number...
Brain-Computer Interface (BCI) is a direct communication pathway between brain and external devices bypassing the natural pathway of nerves and muscles. BCI enables an individual to send commands to a peripheral device using his brain activity. Electroencephalogram (EEG) is the most commonly used brain signal acquisition method as it is simple, economical and portable. Feasibility of detecting familiar...
In the context of brain-computer interfacing based on motor imagery, we propose a method which allows an expert to select manually time-frequency features. This selection is performed specifically for each subject, by analysing a set of curves that emphasize differences of brain activity recorded from electroencephalographic signals during the execution of various motor imagery tasks. We will show...
In the context of brain-computer interfacing based on motor imagery, we propose a method allowing a human expert to supervise the selection of user-specific time-frequency features computed from EEG signals. Indeed, in the current state of BCI research, there is always at least one expert involved in the first stages of any experimentation. On one hand, such experts really appreciate keeping a certain...
Lack of proper restorative sleep can induce sleepiness at odd hours making a person drowsy. This onset of drowsiness can be detrimental for the individual in a number of ways if it happens at an unwanted time. For example, drowsiness while driving a vehicle or operating heavy machinery poses a threat to the safety and wellbeing of individuals as well as those around them. Timely detection of drowsiness...
This paper presents a motor imagery based Brain Computer Interface (BCI) that uses single channel EEG signal from the C3 or C4 electrode placed in the motor area of the head. Time frequency analysis using Short Time Fourier Transform (STFT) is used to compute spectrogram from the EEG data. The STFT is scaled to have gray level values on which Grey Co-occurrence Matrix (GLCM) is computed. Texture descriptors...
A brain-computer interface (BCI) permits cerebral activity alone to control the external devices for assisting people with neuro muscular impairments. Electroencephalogram (EEG) signals are used for brain computer interaction which is highly non-stationary therefore major challenge is to extract features and classify the signals accurately. In this paper we focused on the extraction of features of...
Utilizing brain activity to interact with the external environment is no longer impossible thanks to recent advances in developing Brain-Computer Interfaces (BCIs). This paper proposes a novel recognition method for Steady-State Visual Evoked Potentials (SSVEPs) from electroencephalography (EEG). In this approach, EEG signals are pre-processed using spectral and time domain filters in order to enhance...
Brain signals arise as a mixture of various neural processes that occur in different spatial, frequency and temporal locations. In detection paradigms, algorithms are developed that target specific processes. In this work, we apply tensor factorisation to a set of intracranial electroencephalography data from a group of epileptic patients and factorise the data into three modes; space, time and frequency...
In recent years, movement related cortical potentials (MRCP), a type of slow cortical potentials, have been used for motor intention detection for triggering external devices in close loop rehabilitation paradigm. One of the main issues with these slow frequency MRCP signals is to separate them from the background brain activity or their poor signal to noise ratio. For this reason, different filter...
In this paper, we investigated Deep Learning (DL) for characterizing and detecting target images in an image rapid serial visual presentation (RSVP) task based on EEG data. We exploited DL technique with input feature clusters to handle high dimensional features related to time - frequency events. The method was applied to EEG recordings of a RSVP experiment with multiple sessions and subjects. For...
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