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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 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...
Fast and automatic identification and analysis of different bio-medical signals is of growing importance nowadays. This necessitates the application of different computer aided diagnosis methods to interpret, distinguish and analyze various signals and images. In this paper, we have proposed a novel method to identify the Epilepsy from EEG signals. RBF Kernel based Support Vector Machine (SVM) is...
Accurate modeling of Electroencephalography (EEG) signals is an important problem in clinical diagnosis of brain diseases. The method using support vectors machine (SVM) based on the structure risk minimization provides us an effective way of learning machine. But solving the quadratic programming problem for training SVM becomes a bottle-neck of using SVM because of the long time of SVM training...
Our cognitive abilities can help us communicate without any visible action or words. Brain Computer Interfaces (BCI) achieves this communication using the brain waves. But for practical applications, system using BCI must be fast and accurate. In this paper, we present a method that uses SVM classifiers over ensemble of averaged data. Averaging data over number of trails removes the random noise and...
Motivated by the need to deal with critical disorders that involve death of neurons, such as Amyotrophic Lateral Sclerosis (ALS) and brainstem stroke, interpretation of the brain's Motor Imagery (MI) activities is highly needed. Brain signals can be translated into control commands. Electroencephalography (EEG) is considered in this work, EEG is a low-cost non-invasive technique. A big challenge is...
Electroencephalographic (EEG) signals are produced in brain due to firing of the neurons. Any anomaly found in the EEG indicates abnormality associated with brain functioning. The efficacy of automated analysis of EEG depends on features chosen to represent the time series, classifier used and quality of training data. In this work, we present automated analysis of EEG time series acquired from two...
In this paper, a method is proposed to predict the putt outcomes of golfers based on their electroencephalogram (EEG) signals recorded before the impact between the putter and the ball. This method can be used into a brain-computer interface system that encourages golfers for putting when their EEG patterns show that they are ready. In the proposed method, multi-channel EEG trials of a golfer are...
Eye blinks and lateral eye movements are prominent in EEG signals which are obtained by placing electrodes in the frontal region of the brain. This paper presents a machine learning approach to detect eye movements and blinks from EEG data and map them as intents to control external devices like a computer desktop or a wheel chair.
Brain computer interface is one of the most recent and controversial field in Computer Science which emerged in order to help some handicapped people. This paper investigates different classification algorithms dealing with the BCI P300 speller diagram. The system used is composed of an ensemble of Support vector machines. Three different methods are used namely weighted ensemble of SVM, row &...
P300 speller is a traditional brain computer interface paradigm and focused by lots of current BCI researches. In this paper a support vector machine based recursive feature elimination method was adapted to select the optimal channels for character recognition. The margin distance between target and nontarget stimulus in feature space was evaluated by training SVM classifier and then the features...
Clinical electroencephalography (EEG) is routinely used to monitor brain function in critically ill patients, and specific EEG waveforms are recognized by clinicians as signatures of abnormal brain. These pathologic EEG waveforms, once detected, often necessitate accute clinincal interventions, but these events are typically rare, highly variable between patients, and often hard to separate from background,...
Recently, the research on Brain-Computer Interface (BCI) technology has achieved great progress, and the BCI system based on Motor Imagery (MI) has been intensively studied in many labs. The essential part of signal processing in BCI is how to extract the MI features in electroencephalographic (EEG) and recognize the MI task accurately. One challenge lies in that EEG signals are non-stationary, whose...
In BCI research community, support vector machine (SVM) is an effective method for motor imagery (MI)-based electroencephalographic (EEG) classification. However, the computation of decision function during SVM classification stage for a new EEG trial is time-consuming due to the large number of support vectors (SV). This paper proposes a new method to reduce the number of support vectors so that...
Brain Computer Interface Systems (BCIs) allow the identification of volitive brain activity patterns. This allows their use as input channels for alternative communication and computer access systems by patients suffering from severe motor disabilities. This paper presents preliminary results obtained after extracting four different features from EEG signals in order to recognize the activity patterns...
Accurate classification of left and right hand motor imagery of EEG is an important issue in brain-computer interface (BCI). Here, discrete wavelet transform was firstly applied to extract the features of left and right hand motor imagery in EEG. Secondly, Fisher Linear Discriminant Analysis was used with two different threshold calculation methods and obtained good misclassification rate. We also...
A patient-specific seizure prediction algorithm is proposed using a classifier to differentiate preictal from interictal ECoG signals. Spectral power of ECoG processed in four different fashions are used as features: raw, time-differential, space-differential, and time/space-differential ECoG. The features are classified using cost-sensitive support vector machines by the double cross-validation methodology...
In this paper, we designed eight different mental tasks based on logical-mathematical intelligence, spatial intelligence and bodily-kinesthetic intelligence. Eleven students from three professional fields were selected. When they imaged these eight mental tasks, their EEG signal were acquired. First, we extracted the frequency band feature of ??, ??, ??, ?? from the EEG. Then SVM alrothm was used...
Several different algorithms have been proposed for automatic detection of epileptic seizure based on both scalp and intracranial electroencephalography (sEEG and iEEG). Which modality that renders the best result is hard to assess though. From 16 patients with focal epilepsy, at least 24 hours of ictal and non-ictal iEEG were obtained. Characteristics of the seizures are represented by use of wavelet...
The mind speller is a brain-computer interface which enables subjects to spell text on a computer screen by detecting P300 event-related potentials in their electroencephalograms. This BCI application is of particular interest for disabled patients who have lost all means of verbal and motor communication. We report on the implementation of a feature extraction procedure on a new ultra low-power 8-channel...
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