Nayel Al-Zubi

Toward Inexpensive and Practical Brain Computer Interface

Brain Computer Interface (BCI) introduces a new communication system that does not depend on brains normal output pathways. This paper studies the feasibility of using inexpensive Electroencephalogram(EEG) device for BCI with asynchronous BCI mode which leads the control mechanism to become highly available for variety of users and a more natural way to communicate than the current BCI versions which depend on expensive EEG devices, which are less practical where they need special environment to run the BCI system, and use synchronous BCI mode in most cases. Benchmarking the results using Emotiv to another complex EEG device (BrainAmp) shows the reliability of such inexpensive devices.

Block-based SVD image watermarking in spatial and transform domains

The idea of this paper is to implement an efficient block-by-block singular value (SV) decomposition digital image watermarking algorithm, which is implemented in both the spatial and transforms domains. The discrete wavelet transform (DWT), the discrete cosine transform and the discrete Fourier transform are exploited for this purpose. The original image or one of its transforms is segmented into non-overlapping blocks, and consequently the image to be inserted as a watermark is embedded in the SVs of these blocks. Embedding the watermark on a block-by-block manner ensures security and robustness to attacks such like Gaussian noise, cropping and compression. The proposed algorithm can also be used for colour image watermarking. A comparison study between the proposed block-based watermarking algorithm and the method of Liu is performed for watermarking in all domains. Simulation results ensure that the proposed algorithm is more effective than the traditional method of Liu, especially when the watermarking is performed in the DWT domain.

Efficient speaker identification from speech transmitted over Bluetooth networks

This paper studies the process of speaker identification over Bluetooth networks. Bluetooth channel degradations are considered prior to the speaker identification process. The work in this paper employs Mel-frequency cepstral coefficients for feature extraction. Features are extracted from different transforms of the received speech signals such as the discrete cosine transform (DCT), signal plus DCT, discrete sine transform (DST), signal plus DST, discrete wavelet transform (DWT), and signal plus DWT. A neural network classifier is used in the experiments, while the training phase uses clean speech signals and the testing phase uses degraded signals due to communication over the Bluetooth channel. A comparison is carried out between the different methods of feature extraction showing that the DCT achieves the highest recognition rates.

Personalised mechatronic valve time-schedule optimiser for hydrocephalus shunt

Hydrocephalus is an excessive accumulation of the cerebrospinal fluid (CSF) in the ventricles of the brain, without treatment it leads in brain damage. The usual treatment is a shunt procedure implanted into the ventricles of the brain to drain the excess fluid to another part of the body. Current shunts are controlled by a pressure dependent valve, while recent developed shunts are utilising mechatronic valves. Compared to the current differential pressure valves, mechatronic valves are regulated by time-based schedule rather than differential pressure across the valve. Therefore, it is important that this time schedule is chosen properly for each patient so that a normal ICP is preserved. Choosing proper time schedule for each patient is still one of the challenges facing the implementation of such valves. This work presents a new method to propose optimal valve time-schedule using an ICP dynamics model and patients ICP traces, so that shunt valves can be configured accordingly. This method presents a precise and efficient way of how the ICP model can be utilised in evaluating the patients ICP traces and hence proposing a personalised optimal valve time-schedule as a function of mean measured ICP for each individual patient that can keep the ICP within the normal levels.

Intelligent shunt agent for gradual shunt removal

When passive shunts, which divert cerebrospinal fluid (CSF) from the ventricles in the brain to another part of the body, were developed, apparently they change favourably the treatment of hydrocephalus, then it becomes of great importance to overcome the drawbacks of such shunts, and the gradual rising use of various shunts are accompanied by total shunt dependency with several problems and shortcomings has understandably become obvious among physicians as well as surgeons to rehabilitate and upgrade these shunts. There is a little use of carrying out arrested hydrocephalus which is subject to many aspects, ranging from problems of immediate clinical concern to the more unknowable areas of cerebrospinal fluid CSF dynamics, and it is not always as easy to define indications for arrested hydrocephalus or to evaluate the results of such treatment. However, it is important to attempt to define as precisely as possible a technique to measure the ability of arresting hydrocephalus, while current solutions estimations are based on long time procedure, evaluate parameters such as head growth, or ventricle sizes using CT or MRI scan. This paper proposes a new treatment approach and shunting system that helps improving diagnosis and treatment of Hydrocephalus patients. This approach suggests a developing and utilising an intelligent shunt agent (i-Shunt) that can learn from the patients status and initiate a weaning program, and based on the response evaluation, the parameters of the shunt can be modified to accommodate the patients needs. Therefore, a novel shunt could be build to satisfy the patients need instantaneously by keeping the intracranial pressure (ICP) within normal levels, where it is actually directed toward shunt independency.

An expert system for hydrocephalus patient feedback

Diagnosis of hydrocephalus symptoms and shunting system faults currently are based on clinical observation, monitoring of cranial growth, transfontanelle pressure, imaging techniques and, on occasion, studies of cerebrospinal fluid (CSF) dynamics. Up to date, the patient has to visit the hospital or meet consultant to diagnose the symptoms that occur due to rising of intracranial pressure or any shunt complications, which cause suffering for the patient and his family. This work presents the design and implementation of an expert system based on real-time patient feedback that aims to provide a suitable decision for hydrocephalus management and shunt diagnosis. Such decision would help in personalising the management as well as detecting and identifying of any shunt malfunctions without the need to contact or visit the hospital. In this paper, the development of patient feedback expert system is described. The outcome of such system would help satisfy the patients needs regarding his/her shunt.

Multivariate analysis of intracranial pressure (ICP) signal using principal component analysis

The diagnosis and treatment of hydrocephalus and other neurological disorders often involve the acquisition and analysis of large amount of intracranial pressure (ICP) signal. Although the analysis and subsequent interpretation of this data is an essential part of the clinical management of the disorders, it is typically done manually by a trained clinician, and the difficulty in interpreting some of the features of this complex time series can sometimes lead to issues of subjectivity and reliability. This paper presents a method for the quantitative analysis of this data using a multivariate approach based on principal component analysis, with the aim of optimising symptom diagnosis, patient characterisation and treatment simulation and personalisation. In this method, 10 features are extracted from the ICP signal and principal components that represent these features are defined and analysed. Results from ICP traces of 40 patients show that the chosen features have relevant information about the ICP signal and can be represented with a few components of the PCA (approximately 91% of the total variance of the data is represented by the first four components of the PCA) and that these components can be helpful in characterising subgroups in the patient population that would otherwise not have been apparent. The introduction of supplementaty (non-ICP) variables has offered insight into additional groupings and relationships which may prove to be a fruitful avenue for exploration.