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We compare the results given by different methods to reconstruct cortical sources activity in order to classify EEG in real time. Two motor imagery experiments were performed. The aim was to retrieve from 1-second windows of signal which motor imagery task the subjects were performing. The use of cortical activity reconstruction was compared to Laplacian filtering, which is often used in BCI. A recursive...
Connected health represents an increasingly important model for health-care delivery. The concept is heavily reliant on technology and in particular remote physiological monitoring. One of the principal challenges is the maintenance of high quality data streams which must be collected with minimally intrusive, inexpensive sensor systems operating in difficult conditions. Ambulatory monitoring represents...
Fatigue can be defined as a state that involves psychological and physical tiredness with a range of symptoms such as tired eyes, yawning and increased blink rate. It has major implications for work place and road safety as well as a negative symptom of many acute and chronic illnesses. As such there has been considerable research dedicated to systems or algorithms that can be used to detect and monitor...
For individuals with mobility limitations, powered wheelchair systems provide improved functionality, increased access to healthcare, education and social activities. Input devices such as joystick and switches can provide the necessary input required for efficient control of the powered wheelchair. For persons with limited dexterity, or fine control of the fingers, access to mechanical hardware such...
Due to the artifacts in electroencephalography (EEG) data, the performance of brain-computer interface (BCI) is degraded. On the other hand, in the motor imagery based BCI system, EEG signals are usually contaminated by the misleading trials caused by improper imagination of a movement. In this paper, we present a novel algorithm to detect the abnormal EEG data using genetic algorithm (GA). After...
In an overnight driving simulation study three commercially available devices of fatigue monitoring technologies (FMT) were applied to test their accuracy. 16 volunteers performed driving tasks during eight sessions (40 min each) separated by 15 minutes breaks. The main output variable of FMT devices, which is the percentage of eye closure (PERCLOS), the driving performance (standard deviation of...
Brain computer interface aims to provide a communication system with external media via thoughts. For this purpose, brain signals are acquired from the scalp by EEG device and processed for characterization. In this work, the classification of movement imagery EEG data has been studied for left hand, right hand, foot and tongue movement imagination cases. common spatial patterns (CSP) method and temporal...
In this paper, we present the results of single trial EEG classification of observed wrist movements. This study is part of our endeavour to develop brain computer interfaces as an assistive device for people with severe motor disabilities. Our methods rely on a simple but robust algorithm that requires no subject training to modulate brain activity. We adopt a method based on extraction and selection...
Brain-Computer Interfaces (BCI) use electroencephalography (EEG) signals recorded from the scalp to create a new communication channel between the brain and an output device by bypassing conventional motor output pathways of nerves and muscles. One of the most important components of BCI is feature extraction of EEG signals. How to rapidly and reliably extract EEG features for expressing the brain...
A hybrid BSS-SVM method for distinguishing between left and right finger movements from the electroencephalogram (EEG) has been developed. Support vector machines (SVM) is used to effectively classify the extracted features incorporating blind source separation (BSS) and directed transfer functions (DTF). This is the basis for a brain computer interface (BCI). We analyzed 200 trials of 64 electrode...
Our aim is to assess and evaluate signal processing and classification methods for extracting features from EEG signals that are useful in developing brain-computer interfaces. In this paper, we report on results of developing a method to classify wrist movements using EEG signals recorded from a subject whilst controlling a joystick and moving it in different directions. Such method could be potentially...
This study presents a comparison of two methods to extract features for the classification of wrist movements (flexion, extension, pronation, supination). For the first method, a set of 160 features was extracted from the filtered time and frequency domain EEG data and its alpha, beta, and theta bands. For the second method, a set of 40 features per movement type was extracted from the ICA-calculated...
Identification and classification technology plays an important part in study of the BCI system. There are many algorithms to classify the event of different task related. Here, finger movement was used as the basic and typical tasks to be identified in the BCI experiments. The ideas of BP and ERD were introduced and discussed. The CSSD (common spatial subspace decomposition) algorithm was used for...
EEG data were recorded from occipital scalp regions of subjects who attended to an alternating checkerboard stimulus in one visual field while ignoring a similar stimulus of a different frequency in the opposite visual field. Classification of left/right spatial attention is attempted by extracting steady-state visual evoked potentials (SSVEPs) elicited by the stimuli to assess the potential use of...
28 channel EEG data were recorded while a subject performed wrist movements in four directions. Four feature types were extracted for each channel following optimized filtering of the signals. The potential performance of each feature and channel for use in the classification of the EEG signals was analyzed by estimating the relative class overlap using a first order histogram approach. The best feature/channel...
We report our investigation of classification of imagined left and right hand movements by applying source analysis methods. Independent component analysis (ICA) is used as a spatio-temporal filter, then equivalent dipole analysis and cortical current density imaging methods are applied to reconstruct equivalent sources, to aid classification of motor imagery tasks in a human subject. The classification...
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