An Optimized SWCSP Technique for Feature Extraction in EEG-based BCI System

Brain-computer interface (BCI) is an evolving technology having huge potential for rehabilitation of patients suffering from disorders of the nervous system, besides  many other nonmedical applications. Multichannel electroencephalography (EEG) is widely used to provide input signals to a BCI system. Significant research in methodology employed to implement different stages of BCI system, has led to discovery of new issues and challenges. The raw EEG data includes artifacts from environmental and physiological sources, which is eliminated in preprocessing phase of BCI system. It is then followed by a feature extraction stage to isolate a few relevant features for further classification to a particular motor imagery (MI) activity. A feature extraction approach based on spectrally weighted common spatial pattern (SWCSP) is proposed in this paper to improve overall accuracy of a BCI system. The reported literature uses SWCSP for feature extraction, as it has outperformed other techniques. The proposed approach enhances its performance by optimizing its parameters. The independent component analysis (ICA) method is used for detection and removal of irrelevant data, while linear discriminant analysis (LDA) is used as a classifier. The proposed approach is executed on benchmark data-set 2a of BCI competition IV. It yielded classification accuracy of 70.6% across nine subjects, which is higher than all the reported approaches.&nbsp

Virtuality Supports Reality for e-Health Applications

Strictly speaking the word “virtuality” or the expression “virtual reality” refers to an application for things simulated or created by the computer, which not really exist. More and more often such things are becoming equally referred with the adjective “virtual” or “digital” or mentioned with the prefixes “e-” or “cyber-”. So we know, for instance, of virtual or digital or e- or cyber- community, cash, business, greetings, books .. till even pets. The virtuality offers interesting advantages with respect to the “simple” reality, since it can reproduce, augment and even overcome the reality. The reproduction is not intended as it has been so far that a camera films a scenario from a fixed point of view and a player shows it, but today it is possible to reproduce the scene dynamically moving the point of view in practically any directions, and “real” becomes “realistic”. The virtuality can augment the reality in the sense that graphics are pulled out from a television screen (or computer/laptop/palm display) and integrated with the real world environments. In this way useful, and often in somehow essentials, information are added for the user. As an example new apps are now available even for iphone users who can obtain graphical information overlapped on camera played real scene surroundings, so directly reading the height of mountains, names of streets, lined up of satellites .., directly over the real mountains, the real streets, the real sky. But the virtuality can even overcome reality, since it can produce and make visible the hidden or inaccessible or old reality and even provide an alternative not real world. So we can virtually see deeply into the matter till atomic dimensions, realize a virtual tour in a past century or give visibility to hypothetical lands otherwise difficult or impossible to simple describe. These are the fundamental reasons for a naturally growing interest in “producing” virtuality. So here we will discuss about some of the different available methods to “produce” virtuality, in particular pointing out some steps necessary for “crossing” reality “towards” virtuality. But between these two parallel worlds, as the “real” and the “virtual” ones are, interactions can exist and this can lead to some further advantages. We will treat about the “production” and the “interaction” with the aim to focus the attention on how the virtuality can be applied in biomedical fields, since it has been demonstrated that virtual reality can furnish important and relevant benefits in e-health applications. As an example virtual tomography joins together 3D imaging anatomical features from several CT (Computerized axial Tomography) or MRI (Magnetic Resonance Imaging) images overlapped with a computer-generated kinesthetic interface so to obtain a useful tool in diagnosis and healing. With the new endovascular simulation possibilities, a head mounted display superimposes 3D images on the patient’s skin so to furnish a direction for implantable devices inside blood vessels. Among all, we chose to investigate the fields where we believe the virtual applications can furnish the meaningful advantages, i.e. in surgery simulation, in cognitive and neurological rehabilitation, in postural and motor training, in brain computer interface. We will furnish to the reader a necessary partial but at the same time fundamental view on what the virtual reality can do to improve possible medical treatment and so, at the end, resulting a better quality of our life

Application of Deep Neural Network in Healthcare data

Biomedical data analysis has been playing an important role in healthcare provision services. For decades, medical practitioners and researchers have been extracting and analyse biomedical data to derive different health-related information. Recently, there has been a significant rise in the amount of biomedical data collection. This is due to the availability of biomedical devices for the extraction of biomedical data which are more portable, easy to use and affordable, as an effect technology advancement. As the amount of biomedical data produced every day increases, the risk of human making analytical and diagnostic mistakes also increases. For example, there are approximately 40 million diagnostic errors involving medical imaging annually worldwide, hence rise a need for the development of fast, accurate, reliable and automatic means for analysis of biomedical data. Conventional machine learning has been used to assist in the analysis and interpretation of biomedical data automatically, but always limited with the need for feature extraction process to train the built models. To achieve this, three studies have been conducted. Two studies were conducted by using EEG signals and one study by using microscopic images of cancer cells. In the first study with EEG signals, our method managed to interpret motor imaginary activities from a 64 channels EEG device with 99% classification accuracy when all the 64 channels were used and 91.5% classification when the number of channels was selected to eight (8) channels. In a second study which involved steady-state visual evoked potential form of EEG signals, our method achieved an average of 94% classification accuracy by using two channels, skin like EEG sensor. In the third study for authentication of cancer cell lines by using microscopic images, our method managed to attain an average of 0.91 F1-score in the authentication of eight classes of cancer cell lines. Studies reported in this thesis, significantly shows that CNN can play a major role in the development of a computerised way in the analysis of biomedical data. Towards provision of better healthcare by using CNN in analysis of different formats of biomedical data, this thesis has three major contributions, i) introduction of a new method for EEG channels selection towards development of portable EEG sensors for real-life application, and ii) introduction of a method for cancer cell lines authentication in the laboratory environment towards development of anti-cancer drugs, and iii) Introduction of a method for authentication of isogenic cancer cell lines

SVM Classification of EEG signals for brain computer interface

In this paper, a brain/computer interface is proposed. The aim of this work is the recognition of the will of a human being, without the need of detecting the movement of any muscle. Disabled people could take, of course, most important advantages from this kind of sensor system, but it could also be useful in many other situations where arms and legs could not be used or a brain-computer interface is required to give commands. In order to achieve the above results, a prerequisite has been that of developing a system capable of recognizing and classifying four kind of tasks: thinking to move the right hand, thinking to move the left hand, performing a simple mathematical operation, and thinking to a carol. The data set exploited in the training and test phase of the system has been acquired by means of 61 electrodes and it is formed by time series subsequently transformed to the frequency domain, in order to obtain the power spectrum. For every electrode we have 128 frequency channels. The classification algorithm that we used is the Support Vector Machine (SVM)

SVM Classification of EEG signals for brain computer interface

In this paper, a brain/computer interface is proposed. The aim of this work is the recognition of the will of a human being, without the need of detecting the movement of any muscle. Disabled people could take, of course, most important advantages from this kind of sensor system, but it could also be useful in many other situations where arms and legs could not be used or a brain-computer interface is required to give commands. In order to achieve the above results, a prerequisite has been that of developing a system capable of recognizing and classifying four kind of tasks: thinking to move the right hand, thinking to move the left hand, performing a simple mathematical operation, and thinking to a carol. The data set exploited in the training and test phase of the system has been acquired by means of 61 electrodes and it is formed by time series subsequently transformed to the frequency domain, in order to obtain the power spectrum. For every electrode we have 128 frequency channels. The classification algorithm that we used is the Support Vector Machine (SVM)