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Brain-computer Interfaces (BCIs) are control and communication systems based on acquisition and processing of brain signals to control a computer or an external device. Usually, BCI is focused in recognizing acquired events by different neuroimage methods, but the most used is the electroencephalography (EEG). Feature extraction over EEG signals for BCI systems is crucial to the classification performance...
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...
Brain-Computer Interfaces (BCIs) provide a way to communicate without movement and can offer significant clinical benefits. Electrical brain activity recorded using electroencephalography (EEG) can be automatically interpreted by supervised learning classifiers according to the descriptive features of the signal. This paper investigates the performance of novel feature extraction based on a signal...
EMG based control becomes the core of the pros-theses, orthoses and rehabilitation devices in the recent research. Though the difficulties of using EMG as a control signal due to the complexity nature of this signal, the researchers employed the pattern recognition technique to overcome this problem. The EMG pattern recognition mainly consists of four stages; signal detection and preprocessing feature...
In this paper, a novel method for detecting steadystate visual evoked potentials (SSVEP) using multiple channel electroencephalogram (EEG) data is presented. Accurate asynchronous detection, high speed and high information transfer rate can be achieved after a short calibration session. Spatial filtering based on the Canonical Corelation Analysis method proposed in [1] is used for identifying optimal...
Brain machine interface (BMI) devices facilitate communication and control of computers using signals measured from within the brain of the operators. These signals are detected using electroencephalography (EEG) devices. Research in this field aims to enable victims of ‘locked-in syndrome’ as a result of amyotrophic lateral sclerosis, spinal injury, cerebral palsy, muscular dystrophies, or multiple...
In this paper, a novel method of human identification using electrocardiogram (ECG) is proposed. In the method, while normalizing RR interval, in addition to normalized signal where time interval of P wave, Q wave, R wave, S wave relatively to R wave is unaligned, normalized signal where time interval of those peaks is aligned is also generated. Wavelet transform is then applied to both normalized...
We investigated whether listener-assisted scanning, an alternative communication method for persons with severe motor and visual impairments but preserved cognitive skills, could be used for spelling with EEG. To that end spoken letters were presented sequentially, and the participants made selections by performing motor execution/imagery or a cognitive task. The motor task was a brisk dorsiflexion...
In recent years, EEG-based technology has become more popular in producing variety of BMI protocols for wheel chair navigation and communication systems. In this research work, as an initial step towards the development of an intelligent navigation system with a communication aid, a simple EEG data capturing procedure has been introduced using visually evoked potentials. A simple, visually evoked...
Understanding cognitive responses of human brain is one of the significant research fields where electroencephalography plays vital role in analyzing brain functionality with respect to brain signals. Electromyography is another modality to understand cognitive responses with respect to muscle activation. In this research work, a data set consists of healthy and myopathy has been considered from physionet...
Electrocardiogram (ECG) reflects the activities of the human heart and reveals hidden information on its structure and behaviour. The information is extracted to gain insights that assist explanation and identification of diverse pathological conditions. This was traditionally done by an expert through visual inspection of ECGs. The complexity and tediousness of this onus hinder long-term monitoring...
In this paper, we proposed a novel supervised feature extractor named as class-augmented independent component analysis (CA-ICA) whose performance can be maintained even after the input variables are varied, only if new input variables are still linear combinations of the same independent sources as old input variables were. This property can be useful in implementing an sEMG decoder robust to the...
Myo-electric signals have been widely used in human-machine interfaces because these biosignal directly reflect human intentions to robots. The major difficulty of applying these biosignal in a pattern recognition system in real time is that they are unstable and vary in time. This instability occurs outside of the steady state of the signal, at the beginning and the ending of the motions. For real-time...
Eye movements play an important role in evaluating the process of reading. By visual inspection of the eye movements, it is possible to differentiate the reading process of different persons. The eye movements can be considered as objective tools for understanding the reading process. However, most of the eye movements are involuntary and out of our conscious control. Hence the reading process is...
This study emphases on the alpha oscillation of Electroencephalography (EEG) signal of normal children ability towards working memory performance and visual responsive. The assessments were conducted on 30 children aged between 7 to 9 years old who have no records of working memory disability. The raw EEG signals were decomposed using discrete wavelet transform with mother wavelet: Daubechies 4 (db4)...
The use of Electroencephalography (EEG) in the domain of Brain Computer Interface is a now common place. EEG for imagined speech reproduction and observation of brain response to audio stimuli are active areas of research. In this paper, we consider the case of imagined and mouthed non-audible speech recorded with EEG electrodes. We analyze different feature extraction techniques such as Mel Frequency...
Classification of postures and movements of distal limbs based on surface electromyography (sEMG) of proximal muscles is necessary in myoelectric hand prostheses. With increasing the number of movements, classification problem becomes a serious challenge. In this paper, we have used NINAPRO database that contains sEMG and kinematic data of upper limbs while performing 52 hand postures and movements...
This paper presents the Brain computer interface (BCI) and its current potential for application in devices and process control. BCI is one of emerging options for Human Computer Interface (HCI) allowing more comfortable interaction with devices and processes in the Information and Communication Technology (ICT) area. Current state on the art is described and various available BCI devices are listed...
An electroencephalography (EEG)-based Motor Imagery Brain-Computer Interface (MI-BCI) requires a long setup time if a large number of channels is used, and EEG from noisy or irrelevant channels may adversely affect the classification performance. To address this issue, this paper proposed 2 approaches to systematically select discriminative channels for EEG-based MI-BCI. The proposed Discriminative...
This paper presents a comparison between two different technologies of acquisition systems (BrainNet36 and Emotiv Epoc) for an Independent-BCI based on Steady-State Visual Evoked Potential (SSVEP). Two stimuli separated by a viewing angle < 1° were used. Multivariate Synchronization Index (MSI) technique was used as feature extractor and five subjects participated in the experiments. The class...
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