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The electroencephalogram (EEG) signals are a sturdy tool for tracing brain variations during different periods of life, also it plays a prominent and considerable role in the diagnosis of various diseases. In our previously published papers [1-8] we have worked on diverse problems that can be analyzed by neural networks. In this paper, we have chosen EEG signals due to its increase its application...
In this study, pattern recognition based brain computer interface is designed using EEG p300 component elicited by visual stimuli. A novel EEG database obtained from 19 subjects is constructed with EMOTIV EPOC+ amplifier and OPENVIBE software. Extreme Learning Machine, a type of single layer neural network, Λ-nearest neighbour, Bayesian network and Multi-Layer Perceptron classifiers are compared for...
In the video, experiments demonstrating the control of robot using a brain computer interface are shown. This is done using “actemes”. Actemes are fundamental units of action. The actemes can be combined to perform complex tasks. The user who performs the tasks imagines the complex actions as an ordered combination of actemes. The EEG of the user during the process is collected and classified in real-time...
Adopting the "Imitating-natural-reading(INR)" elicited N2_P3 as carriers between brain and computer. The feature selection and classification of the evoked potentials with wavelet transform and Back Propagation(BP) neural network was used to obtain brain-computer interface(BCI) control signal. Followed by using 20 single-channel electroen-cephalogram(EEG) to single-trial estimation, the...
Patterns in electroencephalogram (EEG) signals are analyzed for a brain computer interface (BCI). An important aspect of this analysis is the work on transformations of high dimensional EEG data to low dimensional spaces in which we can classify the data according to mental tasks being performed. In this research we investigate how a neural network (NN) in an auto-encoder with bottleneck configuration...
Brain Computer Interface one of hopeful interface technologies between humans and machines. Electroencephalogram-based Brain Computer Interfaces have become a hot spot in the research of neural engineering, rehabilitation, and brain science. The artifacts are disturbance that can occur during the signal acquisition and that can alter the analysis of the signals themselves. Detecting artifacts produced...
A new wavelet neural network (WNN) is constructed combining wavelet transform and neural network theory to classify electroencephalogram (EEG) signals. The new WNN takes nonlinear mother wavelet as neuron instead of traditional nonlinear sigmoid function. It owns the merits of good generalization ability and high converging speed. In addition, multi-resolution and self-adaptation are also its advantages...
Multilayer neural networks (MLNN) and the FFT amplitude of brain waves have been applied to dasiaBrain Computer Interfacepsila (BCI). In this paper, a magnetoencephalograph (MEG) system, dasiaMEGvisionpsila developed by Yokogawa Corporation, is used to measure brain activities. MEGvision is a 160-channel whole-head MEG system. Channels are selected from 8 main regions, a frontal lobe, a temporal lobe,...
Human thinking tasks evoke Electroencephalogram (EEG) signal changes, so EEG analysis can help to design communication systems of Brain computer interface (BCI). Second-order blind identification (SOBI), a blind source separation (BSS) algorithm was applied to preprocess EEG data. Subsequently, Fisher distance was used to select the features. Finally, classification of Motor Imagery EEG evoked by...
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