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Feature selection represents a key stage in electroencephalogram (EEG) classifications, because these applications involve numerous, high-dimensional samples. In recent literature, a multitude of supervised embedded feature selection procedures has been proposed. Regardless if they are configured as Single Objective (SOO) or Multi-Objective Optimizations (MOO), the embedded methods assess the quality...
Recognition of epileptic seizures is an important issue and in certain circumstances it is desirable to have portable equipment implementing the algorithm in order to better monitor the patients. This work considers a widely used EEG database from University of Bonn as reference for comparing our recognition method with other previously reported. In order to perform epileptic seizures we combine a...
A Brain-Computer Interface (BCI) speller system based on the Steady-State Visually Evoked Potentials (SSVEP) paradigm is presented. The potentials are elicited through the gaze fixation at one out of the four checkerboards shown on screen, which are flickering at 5, 12, 15 and 20 Hz. After the feature extraction, two dimensionality reduction algorithms, Principal Components Analysis (PCA) and Linear...
The process through which children learn about the world and develop perceptual, cognitive and motor skills relies heavily on object exploration in their physical world. New types of assistive technology that enable children with impairments to interact with their environment have emerged in recent years, and they could be beneficial for children's cognitive and perceptual skills development. Many...
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...
The electroencephalography (EEG) data records vast amounts of human cerebral activity yet is still reviewed primarily by human readers. Most of the times, the data is contaminated with non-cerebral originated signals, called artifacts, which could be very difficult to visually detect and, undiscovered, could damage the neural information analysis. The purpose of our work is to detect the artifacts...
High dimensionality of feature space is a problem in supervised machine learning. Redundant or superfluous features either slow down the training process or dilute the quality of classification. Many methods are available in literature for dimensionality reduction. Earlier studies explored a discernibility matrix (DM) based reduct calculation for dimensionality reduction. Discernibility matrix works...
In this paper, the single-channel EEG based classification systems using simple extracted features are investigated. Each classification system contains the following stages: data acquisition, signal decomposition, feature extraction, and classification. In addition to using the filter bank and empirical mode decomposition (EMD) methods for signal decomposition, a sparse discrete wavelet packet transform...
P300-based brain-computer interface (BCI) is one of the most common BCIs. Due to the characteristics of P300 responses vary from person to person, it leads to the necessity of collecting much labeled data from each user and the problem of time-consuming in many applications. In this work, a transfer learning method which dynamically adjusts the weights of instances is applied to improve the P300-based...
For a practical intracranial brain computer interface (BCI), minimizing the invasiveness of the electrode implantation is crucial. In this study, we used only one intracranial electrode to implement an online BCI for fast typing. When the subject attended the virtual button containing visual motion stimuli, prominent responses were elicited at the stereo-EEG (SEEG) electrodes within the fMRI defined...
The traditional Brain-computer Interface (BCI) obtains parameters from the offline analysis and applies them to online experiments. However, due to non-stationary characte-ristic of electroencephalography (EEG), static classification of algorithms are hard to be used in practical BCI. In this paper, we propose a new algorithm that combines the adaptation of preferable new incoming data with the incremental...
This paper presents an automated method for seizure detection in EEGs using an increment entropy (IncrEn) and support vector machines (SVMs). The IncrEn is a measure of the complexity of time series, which characterizes both the permutation of values and the temporal order of values. The IncrEn is used to extract features of epileptic EEGs and normal EEGs. The SVMs are employed to classify seizure...
Emotion classification and recognition from electroencephalogram (EEG) signals have been studied extensively due to its potential benefits such as entertainment and health care. Concerning classification, various techniques have been developed and applied. Support Vector Machines (SVMs) has been reported as the most used because of its accuracy. Nevertheless, although SVMs has satisfactory performance,...
In brain-computer interface (BCI) research, there must be a trade-off between accuracy and speed of the BCI system, especially those based on event-related potentials (ERPs). This paper proposes a novel method which can significantly increase the spelling bit rate while also maintaining the desired accuracy. We provide an adaptive real-time stopping method based on the scores of ensemble support vector...
In recent days, the classification of abnormal brain Electroencephalogram (EEG) signals is a demanding and challenging task. For this purpose, some of the classification techniques which include Discrete Wavelet Transform (DWT), Discrete Cosine Transform (DCT) and Fast Fourier Transform (FFT) are frequently used in the existing works. But, it has some drawbacks such as, the above mentioned techniques...
Brain-Computer Interface (BCI) research hopes to improve the quality of life for people with severe motor disabilities by providing a capability to control external devices using their thoughts. To control a device through BCI, neural signals of a user must be translated to meaningful control commands using various machine learning components, e.g. feature extraction, dimensionality reduction and...
Over the last few decades, the use of electroencephalography (EEG) signals for motor imagery based brain-computer interface (MI-BCI) has gained widespread attention. Deep learning have also gained widespread attention and used in various application such as natural language processing, computer vision and speech processing. However, deep learning has been rarely used for MI EEG signal classification...
The P300 Speller is a Brain Computer Interface that enables communication using the EEG signal. The P300 wave is an Event Related Potential that occurs as a response to a familiar stimulus. This system can be used to aid persons who are unable to communicate via conventional methods. In this paper, the P300 Speller has been modified to allow communication in three languages: English, Sinhala and Tamil...
The intrinsic interactions among a video's emotion tag, its content, and a user's spontaneous response while consuming the video can be leveraged to improve video emotion tagging, but this capability has not been thoroughly exploited yet. In this paper, we propose an implicit hybrid video emotion tagging approach by integrating video content and users' multiple physiological responses, which are only...
The task of classifying EEG signals for self-paced Brain Computer Interface (BCI) applications is extremely challenging. This difficulty in classification of self-paced data stems from the fact that the system has no clue about the start time of a control task and the data contains a large number of periods during which the user has no intention to control the BCI. Therefore, to improve the performance...
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