The Recovery of Vanadium Pentoxide (V2O5) from Spent Catalyst Utilized in a Sulfuric Acid Production Plant in Jordan.

Vanadium is a significant metal, and its derivatives are widely employed in industry. One of the essential vanadium compounds is vanadium pentoxide (V2O5), which is mostly recovered from titanomagnetite, uranium-vanadium deposits, phosphate rocks, and spent catalysts. A smart method for the characterization and recovery of vanadium pentoxide (V2O5) was investigated and implemented as a small-scale benchtop model. Several nondestructive analytical techniques, such as particle size analysis, X-ray fluorescence (XRF), inductively coupled plasma (ICP), and X-ray diffraction (XRD) were used to determine the physical and chemical properties, such as the particle size and composition, of the samples before and after the recovery process of vanadium pentoxide (V2O5). After sample preparation, several acid and alkali leaching techniques were investigated. A noncorrosive, environmentally friendly extraction method based on the use of less harmful acids was applied in batch and column experiments for the extraction of V2O5 as vanadium ions from a spent vanadium catalyst. In batching experiments, different acids and bases were examined as leaching solution agents; oxalic acid showed the best percent recovery for vanadium ions compared with the other acids used. The effects of the contact time, acid concentration, solid-to-liquid ratio, stirring rate, and temperature were studied to optimize the leaching conditions. Oxalic acid with a 6% (w/w) to a 1/10 solid-to-liquid ratio at 300 rpm and 50 °C was the optimal condition for extraction (67.43% recovery). On the other hand, the column experiment with a 150 cm long and 5 cm i.d. and 144 h contact time using the same leaching reagent, 6% oxalic acid, showed a 94.42% recovery. The results of the present work indicate the possibility of the recovery of vanadium pentoxide from the spent vanadium catalyst used in the sulfuric acid industry in Jordan.

An adversarial collaboration protocol for testing contrasting predictions of global neuronal workspace and integrated information theory.

The relationship between conscious experience and brain activity has intrigued scientists and philosophers for centuries. In the last decades, several theories have suggested different accounts for these relationships. These theories have developed in parallel, with little to no cross-talk among them. To advance research on consciousness, we established an adversarial collaboration between proponents of two of the major theories in the field, Global Neuronal Workspace and Integrated Information Theory. Together, we devised and preregistered two experiments that test contrasting predictions of these theories concerning the location and timing of correlates of visual consciousness, which have been endorsed by the theories' proponents. Predicted outcomes should either support, refute, or challenge these theories. Six theory-impartial laboratories will follow the study protocol specified here, using three complementary methods: Functional Magnetic Resonance Imaging (fMRI), Magneto-Electroencephalography (M-EEG), and intracranial electroencephalography (iEEG). The study protocol will include built-in replications, both between labs and within datasets. Through this ambitious undertaking, we hope to provide decisive evidence in favor or against the two theories and clarify the footprints of conscious visual perception in the human brain, while also providing an innovative model of large-scale, collaborative, and open science practice.

Decreased but diverse activity of cortical and thalamic neurons in consciousness-impairing rodent absence seizures.

Absence seizures are brief episodes of impaired consciousness, behavioral arrest, and unresponsiveness, with yet-unknown neuronal mechanisms. Here we report that an awake female rat model recapitulates the behavioral, electroencephalographic, and cortical functional magnetic resonance imaging characteristics of human absence seizures. Neuronally, seizures feature overall decreased but rhythmic firing of neurons in cortex and thalamus. Individual cortical and thalamic neurons express one of four distinct patterns of seizure-associated activity, one of which causes a transient initial peak in overall firing at seizure onset, and another which drives sustained decreases in overall firing. 40-60 s before seizure onset there begins a decline in low frequency electroencephalographic activity, neuronal firing, and behavior, but an increase in higher frequency electroencephalography and rhythmicity of neuronal firing. Our findings demonstrate that prolonged brain state changes precede consciousness-impairing seizures, and that during seizures distinct functional groups of cortical and thalamic neurons produce an overall transient firing increase followed by a sustained firing decrease, and increased rhythmicity.

Early neural activity changes associated with stimulus detection during visual conscious perception.

The earliest cortical neural signals following consciously perceived visual stimuli in humans are poorly understood. Using intracranial electroencephalography, we investigated neural activity changes associated with the earliest stages of stimulus detection during visual conscious perception. Participants (N = 10; 1,693 electrode contacts) completed a continuous performance task where subjects were asked to press a button when they saw a target letter among a series of nontargets. Broadband gamma power (40-115 Hz) was analyzed as marker of cortical population neural activity. Regardless of target or nontarget letter type, we observed early gamma power changes within 30-180 ms from stimulus onset in a network including increases in bilateral occipital, fusiform, frontal (including frontal eye fields), and medial temporal cortex; increases in left lateral parietal-temporal cortex; and decreases in the right anterior medial occipital cortex. No significant differences were observed between target and nontarget stimuli until >180 ms post-stimulus, when we saw greater gamma power increases in left motor and premotor areas, suggesting a possible role in perceptual decision-making and/or motor responses with the right hand. The early gamma power findings support a broadly distributed cortical visual detection network that is engaged at early times tens of milliseconds after signal transduction from the retina.

Human visual consciousness involves large scale cortical and subcortical networks independent of task report and eye movement activity.

The full neural circuits of conscious perception remain unknown. Using a visual perception task, we directly recorded a subcortical thalamic awareness potential (TAP). We also developed a unique paradigm to classify perceived versus not perceived stimuli using eye measurements to remove confounding signals related to reporting on conscious experiences. Using fMRI, we discovered three major brain networks driving conscious visual perception independent of report: first, increases in signal detection regions in visual, fusiform cortex, and frontal eye fields; and in arousal/salience networks involving midbrain, thalamus, nucleus accumbens, anterior cingulate, and anterior insula; second, increases in frontoparietal attention and executive control networks and in the cerebellum; finally, decreases in the default mode network. These results were largely maintained after excluding eye movement-based fMRI changes. Our findings provide evidence that the neurophysiology of consciousness is complex even without overt report, involving multiple cortical and subcortical networks overlapping in space and time.

A machine-learning approach for predicting impaired consciousness in absence epilepsy.

Behavior during 3-4 Hz spike-wave discharges (SWDs) in absence epilepsy can vary from obvious behavioral arrest to no detectible deficits. Knowing if behavior is impaired is crucial for clinical care but may be difficult to determine without specialized behavioral testing, often inaccessible in practice. We aimed to develop a pure electroencephalography (EEG)-based machine-learning method to predict SWD-related behavioral impairment. Our classification goals were 100% predictive value, with no behaviorally impaired SWDs misclassified as spared; and maximal sensitivity. First, using labeled data with known behavior (130 SWDs in 34 patients), we extracted EEG time, frequency domain, and common spatial pattern features and applied support vector machines and linear discriminant analysis to classify SWDs as spared or impaired. We evaluated 32 classification models, optimized with 10-fold cross-validation. We then generalized these models to unlabeled data (220 SWDs in 41 patients), where behavior during individual SWDs was not known, but observers reported the presence of clinical seizures. For labeled data, the best classifier achieved 100% spared predictive value and 93% sensitivity. The best classifier on the unlabeled data achieved 100% spared predictive value, but with a lower sensitivity of 35%, corresponding to a conservative classification of 8 patients out of 23 as free of clinical seizures. Our findings demonstrate the feasibility of machine learning to predict impaired behavior during SWDs based on EEG features. With additional validation and optimization in a larger data sample, applications may include EEG-based prediction of driving safety, treatment adjustment, and insight into mechanisms of impaired consciousness in absence seizures.

Early cortical signals in visual stimulus detection.

During visual conscious perception, the earliest responses linked to signal detection are little known. The current study aims to reveal the cortical neural activity changes in the earliest stages of conscious perception using recordings from intracranial electrodes. Epilepsy patients (N=158) were recruited from a multi-center collaboration and completed a visual word recall task. Broadband gamma activity (40-115Hz) was extracted with a band-pass filter and gamma power was calculated across subjects on a common brain surface. Our results show early gamma power increases within 0-50ms after stimulus onset in bilateral visual processing cortex, right frontal cortex (frontal eye fields, ventral medial/frontopolar, orbital frontal) and bilateral medial temporal cortex regardless of whether the word was later recalled. At the same early times, decreases were seen in the left rostral middle frontal gyrus. At later times after stimulus onset, gamma power changes developed in multiple cortical regions. These included sustained changes in visual and other association cortical networks, and transient decreases in the default mode network most prominently at 300-650ms. In agreement with prior work in this verbal memory task, we also saw greater increases in visual and medial temporal regions as well as prominent later (> 300ms) increases in left hemisphere language areas for recalled versus not recalled stimuli. These results suggest an early signal detection network in the frontal, medial temporal, and visual cortex is engaged at the earliest stages of conscious visual perception.

Evaluation of Natural Bioactive-Derived Punicalagin Niosomes in Skin-Aging Processes Accelerated by Oxidant and Ultraviolet Radiation.

INTRODUCTION: Skin aging is a normal process that might be accelerated or delayed by altering the balance between antioxidants and free radicals due to increase in the exposure to reactive oxygen species (ROS) into skin cells via UV radiation. Antioxidants can neutralize the harmful effects of ROS, and secondary plant metabolites might help protect against UV radiation. METHODS: In this study, punicalagin was extracted from pomegranate, and concentrations of total polyphenolics and flavonoids were determined, and antioxidant activities were measured. Punicalagin was loaded onto niosomes, and its morphology and release were studied. An in vitro study was performed on human fibroblast cell line HFB4 cells with aging induced by H2O2 and UV radiation. Cell cycle arrest was studied, and different genes (MMP3, Col1A1, Timp3, and TERT) involved in the skin aging process were selected to measure punicalagin's effect. RESULTS: Punicalagin succeeded in reducing the growth arrest of HFB4 cells, activated production of the Col1A1 and Timp3 genes, maintained collagen level, and lowered MMP3. Punicalagin increased human TERT concentration in skin cells. DISCUSSION: Punicalagin is promising as a natural antioxidant to protect human skin from aging.

Induced bioresistance via BNP detection for machine learning-based risk assessment.

Machine Learning (ML) is a powerful tool for big data analysis that shows substantial potential in the field of healthcare. Individual patient data can be inundative, but its value can be extracted by ML's predictive power and ability to find trends. A great area of interest is early diagnosis and disease management strategies for cardiovascular disease (CVD), the leading cause of death in the world. Treatment is often inhibited by analysis delays, but rapid testing and determination can help improve frequency for real time monitoring. In this research, an ML algorithm was developed in conjunction with a flexible BNP sensor to create a quick diagnostic tool. The sensor was fabricated as an ion-selective field effect transistor (ISFET) in order to be able to quickly gather large amounts of electrical data from a sample. Artifical samples were tested to characterize the sensors using linear sweep voltammetry, and the resulting data was utilized as the initial training set for the ML algorithm, an implementation of quadratic discriminant analysis (QDA) written in MATLAB. Human blood serum samples from 30 University of Pittsburgh Medical Center (UPMC) patients were tested to evaluate the effective sorting power of the algorithm, yielding 95% power in addition to ultra fast data collection and determination.

EEG-based Neglect Detection for Stroke Patients.

Spatial neglect (SN) is a neurological syndrome in stroke patients, commonly due to unilateral brain injury. It results in inattention to stimuli in the contralesional visual field. The current gold standard for SN assessment is the behavioral inattention test (BIT). BIT includes a series of penand-paper tests. These tests can be unreliable due to high variablility in subtest performances; they are limited in their ability to measure the extent of neglect, and they do not assess the patients in a realistic and dynamic environment. In this paper, we present an electroencephalography (EEG)-based brain-computer interface (BCI) that utilizes the Starry Night Test to overcome the limitations of the traditional SN assessment tests. Our overall goal with the implementation of this EEG-based Starry Night neglect detection system is to provide a more detailed assessment of SN. Specifically, to detect the presence of SN and its severity. To achieve this goal, as an initial step, we utilize a convolutional neural network (CNN) based model to analyze EEG data and accordingly propose a neglect detection method to distinguish between stroke patients without neglect and stroke patients with neglect.Clinical relevance-The proposed EEG-based BCI can be used to detect neglect in stroke patients with high accuracy, specificity and sensitivity. Further research will additionally allow for an estimation of a patient's field of view (FOV) for more detailed assessment of neglect.

A probabilistic approach for calibration time reduction in hybrid EEG-fTCD brain-computer interfaces.

BACKGROUND: Generally, brain-computer interfaces (BCIs) require calibration before usage to ensure efficient performance. Therefore, each BCI user has to attend a certain number of calibration sessions to be able to use the system. However, such calibration requirements may be difficult to fulfill especially for patients with disabilities. In this paper, we introduce a probabilistic transfer learning approach to reduce the calibration requirements of our EEG-fTCD hybrid BCI designed using motor imagery (MI) and flickering mental rotation (MR)/word generation (WG) paradigms. The proposed approach identifies the top similar datasets from previous BCI users to a small training dataset collected from a current BCI user and uses these datasets to augment the training data of the current BCI user. To achieve such an aim, EEG and fTCD feature vectors of each trial were projected into scalar scores using support vector machines. EEG and fTCD class conditional distributions were learnt separately using the scores of each class. Bhattacharyya distance was used to identify similarities between class conditional distributions obtained using training trials of the current BCI user and those obtained using trials of previous users. RESULTS: Experimental results showed that the performance obtained using the proposed transfer learning approach outperforms the performance obtained without transfer learning for both MI and flickering MR/WG paradigms. In particular, it was found that the calibration requirements can be reduced by at least 60.43% for the MI paradigm, while at most a reduction of 17.31% can be achieved for the MR/WG paradigm. CONCLUSIONS: Data collected using the MI paradigm show better generalization across subjects.

Photocatalytic degradation of methylene blue dye by fluorite type Fe2Zr2-xWxO7 system under visible light irradiation.

Nano-sized Fe2Zr2-xWxO7 system was prepared using the Pacini method where x = 0, 0.05, 0.1 and 0.15. All the samples were characterized using chemical analysis, X-ray diffraction (XRD), Fourier-transform infrared (FT-IR), transmission electron microscopy (TEM), UV-vis diffuse reflectance measurements (DRS) and surface area measurements. The undoped Fe2Zr2O7 was crystallised in the cubic fluorite phase as a major phase in addition to rhombohedral phase of Fe2O3 and monoclinic phase of ZrO2 as the minor phases. Meanwhile, single cubic fluorite phase was defined for Fe2Zr0.85W0.15O7 sample. The last has the lowest band gap (1.69 eV) and the highest surface area (106 m2/g). From TEM, the average particle size of the prepared samples was in the range of (3-7 nm). The photocatalytic efficiency of the prepared Fe2Zr2-xWxO7 system was manifested by the degradation of methylene blue and real textile wastewater of blue colour. Ascending degradation efficiency was exhibited with increasing tungsten concentration which is in accordance with their band gap as well as their surface area values. The degradation rate using Fe2Zr0.85W0.15O7 sample obeys the pseudo-first order kinetic at the optimum degradation conditions (1.5 g/L catalyst and pH11). Fe2Zr0.85W0.15O7 showed promising photocatalytic activity for real textile wastewater where the 69% COD removal was obtained under the same conditions used for methylene blue degradation.

Common spatial pattern and wavelet decomposition for motor imagery EEG- fTCD brain-computer interface.

BACKGROUND: Recently, hybrid brain-computer interfaces (BCIs) combining more than one modality have been investigated with the aim of boosting the performance of the existing single-modal BCIs in terms of accuracy and information transfer rate (ITR). Previously, we introduced a novel hybrid BCI in which EEG and fTCD modalities are used simultaneously to measure electrical brain activity and cerebral blood velocity during motor imagery (MI) tasks. NEW METHOD: In this paper, we used multi-scale analysis and common spatial pattern algorithm to extract EEG and fTCD features. Moreover, we proposed probabilistic fusion of EEG and fTCD evidences instead of concatenating EEG and fTCD feature vectors corresponding to each trial. A Bayesian approach was proposed to fuse EEG and fTCD evidences under 3 different assumptions. RESULTS: Experimental results showed that 93.85%, 93.71%, and 100% average accuracies and 19.89, 26.55, and 40.83 bits/min average ITRs were achieved for right MI vs baseline, left MI versus baseline, and right MI versus left MI respectively. COMPARISON WITH EXISTING METHODS: These performance measures outperformed the results we obtained before in our preliminary study in which average accuracies of 88.33%, 89.48%, and 82.38% and average ITRs of 4.17, 5.45, and 10.57 bits/min were achieved for right MI versus baseline, left MI versus baseline, and right MI versus left MI respectively. Moreover, in terms of both accuracy and speed, the EEG- fTCD BCI with the proposed analysis techniques outperformed all EEG- fNIRS studies in comparison. CONCLUSIONS: The proposed system is a more accurate and faster alternative to EEG-fNIRS systems.

EEG-fTCD hybrid brain-computer interface using template matching and wavelet decomposition.

OBJECTIVE: We aim at developing a hybrid brain-computer interface that utilizes electroencephalography (EEG) and functional transcranial Doppler (fTCD). In this hybrid BCI, EEG and fTCD are used simultaneously to measure electrical brain activity and cerebral blood velocity respectively in response to flickering mental rotation (MR) and word generation (WG) tasks. In this paper, we improve both the accuracy and information transfer rate (ITR) of this novel hybrid brain computer interface (BCI) we designed in our previous work. APPROACH: To achieve such aim, we extended our feature extraction approach through using template matching and multi-scale analysis to extract EEG and fTCD features, respectively. In particular, template matching was used to analyze EEG data whereas 5-level wavelet decomposition was applied to fTCD data. Significant EEG and fTCD features were selected using Wilcoxon signed rank test. Support vector machines classifier (SVM) was used to project EEG and fTCD selected features of each trial into scalar SVM scores. Moreover, instead of concatenating EEG and fTCD feature vectors corresponding to each trial, we proposed a Bayesian fusion approach of EEG and fTCD evidences. MAIN RESULTS: Average accuracy and average ITR of 98.11% and 21.29 bits min-1 were achieved for WG versus MR classification while MR versus baseline yielded 86.27% average accuracy and 8.95 bit min-1 average ITR. In addition, average accuracy of 85.29% and average ITR of 8.34 bits min-1 were obtained for WG versus baseline. SIGNIFICANCE: The proposed analysis techniques significantly improved the hybrid BCI performance. Specifically, for MR/WG versus baseline problems, we achieved twice of the ITRs obtained in our previous study. Moreover, the ITR of WG versus MR problem is 4-times the ITR we obtained before for the same problem. The current analysis methods boosted the performance of our EEG-fTCD BCI such that it outperformed the existing EEG-fNIRS BCIs in comparison.

A novel motor imagery hybrid brain computer interface using EEG and functional transcranial Doppler ultrasound.

BACKGROUND: Hybrid brain computer interfaces (BCIs) combining multiple brain imaging modalities have been proposed recently to boost the performance of single modality BCIs. NEW METHOD: In this paper, we propose a novel motor imagery (MI) hybrid BCI that uses electrical brain activity recorded using Electroencephalography (EEG) as well as cerebral blood flow velocity measured using functional transcranial Doppler ultrasound (fTCD). Features derived from the power spectrum for both EEG and fTCD signals were calculated. Mutual information and linear support vector machines (SVM) were employed for feature selection and classification. RESULTS: Using the EEG-fTCD combination, average accuracies of 88.33%, 89.48%, and 82.38% were achieved for right arm MI versus baseline, left arm MI versus baseline, and right arm MI versus left arm MI respectively. Compared to performance measures obtained using EEG only, the hybrid system provided significant improvement in terms of accuracy by 4.48%, 5.36%, and 4.76% respectively. In addition, average transmission rates of 4.17, 5.45, and 10.57 bits/min were achieved for right arm MI versus baseline, left arm MI versus baseline, and right arm MI versus left arm MI respectively. COMPARISON WITH EXISTING METHODS: Compared to EEG-fNIRS hybrid BCIs in literature, we achieved similar or higher accuracies with shorter task duration. CONCLUSIONS: The proposed hybrid system is a promising candidate for real-time BCI applications.

Real-Time Cardiac Arrhythmia Classification Using Memristor Neuromorphic Computing System.

Cardiac arrhythmia is known to be one of the most common causes of death worldwide. Therefore, development of efficient arrhythmia detection techniques is essential to save patients' lives. In this paper, we introduce a new real-time cardiac arrhythmia classification using memristor neuromorphic computing system for classification of 5 different beat types. Neuromorphic computing systems utilize new emerging devices, such as memristors, as a basic building block. Hence, these systems provide excellent trade-off between real-time processing, power consumption, and overall accuracy. Experimental results showed that the proposed system outperforms most of the methods in comparison in terms of accuracy and testing time, since it achieved 96.17% average accuracy and 34 ms average testing time per beat.

Towards optimal visual presentation design for hybrid EEG-fTCD brain-computer interfaces.

OBJECTIVE: In this paper, we introduce a novel hybrid brain-computer interface (BCI) system that measures electrical brain activity as well as cerebral blood velocity using electroencephalography (EEG) and functional transcranial Doppler ultrasound (fTCD) respectively in response to flickering mental rotation (MR) and flickering word generation (WG) cognitive tasks as well as a fixation cross that represents the baseline. This work extends our previous approach, in which we showed that motor imagery induces simultaneous changes in EEG and fTCD to enable task discrimination; and hence, provides a design approach for a hybrid BCI. Here, we show that instead of using motor imagery, the proposed visual stimulation technique enables the design of an EEG-fTCD based BCI with higher accuracy. APPROACH: Features based on the power spectrum of EEG and fTCD signals were calculated. Mutual information and support vector machines were used for feature selection and classification purposes. MAIN RESULTS: EEG-fTCD combination outperformed EEG by 4.05% accuracy for MR versus baseline problem and by 5.81% accuracy for WG versus baseline problem. An average accuracy of 92.38% was achieved for MR versus WG problem using the hybrid combination. Average transmission rates of 4.39, 3.92, and 5.60 bits min-1 were obtained for MR versus baseline, WG versus baseline, and MR versus WG problems respectively. SIGNIFICANCE: In terms of accuracy, the current visual presentation outperforms the motor imagery visual presentation we designed before for the EEG-fTCD system by 10% accuracy for task versus task problem. Moreover, the proposed system outperforms the state of the art hybrid EEG-fNIRS BCIs in terms of accuracy and/or information transfer rate. Even though there are still limitations of the proposed system, such promising results show that the proposed hybrid system is a feasible candidate for real-time BCIs.

EEG-based neglect assessment: A feasibility study.

BACKGROUND: Spatial neglect (SN) is a neuropsychological syndrome that impairs automatic attention orienting to stimuli in the contralesional visual space of stroke patients. SN is commonly assessed using paper and pencil tests. Recently, computerized tests have been proposed to provide a dynamic assessment of SN. However, both paper- and computer-based methods have limitations. NEW METHOD: Electroencephalography (EEG) shows promise for overcoming the limitations of current assessment methods. The aim of this work is to introduce an objective passive BCI system that records EEG signals in response to visual stimuli appearing in random locations on a screen with a dynamically changing background. Our preliminary experimental studies focused on validating the system using healthy participants with intact brains rather than employing it initially in more complex environments with patients having cortical lesions. Therefore, we designed a version of the test in which we simulated SN by hiding target stimuli appearing on the left side of the screen so that the subject's attention is shifted to the right side. RESULTS: Results showed that there are statistically significant differences between EEG responses due to right and left side stimuli reflecting different processing and attention levels towards both sides of the screen. The system achieved average accuracy, sensitivity and specificity of 74.24%, 75.17% and 71.36% respectively. COMPARISON WITH EXISTING METHODS: The proposed test can examine both presence and severity of SN, unlike traditional paper and pencil tests and computer-based methods. CONCLUSIONS: The proposed test is a promising objective SN evaluation method.

A brain-computer interface based on functional transcranial doppler ultrasound using wavelet transform and support vector machines.

BACKGROUND: Functional transcranial Doppler (fTCD) is an ultrasound based neuroimaging technique used to assess neural activation that occurs during a cognitive task through measuring velocity of cerebral blood flow. NEW METHOD: The objective of this paper is to investigate the feasibility of a 2-class and 3-class real-time BCI based on blood flow velocity in left and right middle cerebral arteries in response to mental rotation and word generation tasks. Statistical features based on a five-level wavelet decomposition were extracted from the fTCD signals. The Wilcoxon test and support vector machines (SVM), with a linear kernel, were employed for feature reduction and classification. RESULTS: The experimental results showed that within approximately 3s of the onset of the cognitive task average accuracies of 80.29%, and 82.35% were obtained for the mental rotation versus resting state and the word generation versus resting state respectively. The mental rotation task versus word generation task achieved an average accuracy of 79.72% within 2.24s from the onset of the cognitive task. Furthermore, an average accuracy of 65.27% was obtained for the 3-class problem within 4.68s. COMPARISON WITH EXISTING METHODS: The results presented here provide significant improvement compared to the relevant fTCD-based systems presented in literature in terms of accuracy and speed. Specifically, the reported speed in this manuscript is at least 12 and 2.5 times faster than any existing binary and 3-class fTCD-based BCIs, respectively. CONCLUSIONS: These results show fTCD as a promising and viable candidate to be used towards developing a real-time BCI.

Arrhythmia classification based on novel distance series transform of phase space trajectories.

Cardiac arrhythmia is a serious disorder in heart electrical activity that may have fatal consequences especially if not detected early. This motivated the development of automated arrhythmia detection systems that can early detect and accurately recognize arrhythmias thus significantly improving the chances of patient survival. In this paper, we propose an improved arrhythmia detection system particularly designed to identify five different types based on nonlinear dynamical modeling of electrocardiogram signals. The new approach introduces a novel distance series domain derived from the reconstructed phase space as a transform space for the signals that is explored using classical features. The performance measures showed that the proposed system outperforms state of the art methods as it achieved 98.7% accuracy, 99.54% sensitivity, 99.42% specificity, 98.19% positive predictive value, and 99.85% negative predictive value.