Blinding efficacy and adverse events following repeated transcranial alternating current, direct current, and random noise stimulation.

As transcranial electrical stimulation (tES) protocols advance, assumptions underlying the technique need to be retested to ensure they still hold. Whilst the safety of stimulation has been demonstrated mainly for a small number of sessions, and small sample size, adverse events (AEs) following multiple sessions remain largely untested. Similarly, whilst blinding procedures are typically assumed to be effective, the effect of multiple stimulation sessions on the efficacy of blinding procedures also remains under question. This is especially relevant in multisite projects where small unintentional variations in protocol could lead to inter-site difference. We report AE and blinding data from 1,019 participants who received up to 11 semi-consecutive sessions of active or sham transcranial alternating current stimulation (tACS), direct current stimulation (tDCS), and random noise stimulation (tRNS), at 4 sites in the UK and US. We found that AEs were often best predicted by factors other than tES, such as testing site or session number. Results from the blinding analysis suggested that blinding was less effective for tDCS and tACS than tRNS. The occurrence of AEs did not appear to be linked to tES despite the use of smaller electrodes or repeated delivery. However, blinding efficacy was impacted in tES conditions with higher cutaneous sensation, highlighting a need for alternative stimulation blinding protocols. This may be increasingly necessary in studies wishing to deliver stimulation with higher intensities.

Modulating fluid intelligence performance through combined cognitive training and brain stimulation.

It is debated whether cognitive training of specific executive functions leads to far transfer effects, such as improvements in fluid intelligence (Gf). Within this context, transcranial direct current stimulation and recently also novel protocols such as transcranial random noise and alternating current stimulation are being investigated with regards to their ability to enhance cognitive training outcomes. We compared the effects of four different transcranial electrical brain stimulation protocols in combination with nine daily computerized training sessions on Gf. 82 participants were randomly assigned to receive transcranial direct current stimulation (tDCS), random noise stimulation (tRNS), multifocal alternating current stimulation at 40 Hz (mftACS), or multifocal tDCS (mftDCS) in combination with an adaptive and synergistic executive function (EF) training, or to a no-contact control group. EF training consisted of gamified tasks drawing on isolated as well as integrated executive functions (working memory, inhibition, cognitive flexibility). Transfer was assessed with a combined measure of Gf including three established tests (Bochumer Matrizentest - BOMAT, Raven's Advanced Progressive Matrices - RAPM, and Sandia Matrices). We found significant improvements in Gf for the tDCS, mftDCS, and tRNS groups when compared with the no-contact group. In contrast, the mftACS group did not improve significantly and showed a similar pattern as the no-contact group. Mediation analyses indicated that the improvement in Gf was mediated through game progression in the mftDCS and tRNS group. Electrical brain stimulation in combination with sustained EF training can lead to transfer effects in Gf, which are mediated by training progression.

Probabilistic Simulation Framework for EEG-Based BCI Design.

A simulation framework could decrease the burden of attending long and tiring experimental sessions on the potential users of brain computer interface (BCI) systems. Specifically during the initial design of a BCI, a simulation framework that could replicate the operational performance of the system would be a useful tool for designers to make design choices. In this manuscript, we develop a Monte Carlo based probabilistic simulation framework for electroencephalography (EEG) based BCI design. We employ one event related potential (ERP) based typing and one steady state evoked potential (SSVEP) based control interface as testbeds. We compare the results of simulations with real time experiments. Even though over and under estimation of the performance is possible, the statistical results over the Monte Carlo simulations show that the developed framework generally provides a good approximation of the real time system performance.

Language-Model Assisted Brain Computer Interface for Typing: A Comparison of Matrix and Rapid Serial Visual Presentation.

Noninvasive electroencephalography (EEG)-based brain-computer interfaces (BCIs) popularly utilize event-related potential (ERP) for intent detection. Specifically, for EEG-based BCI typing systems, different symbol presentation paradigms have been utilized to induce ERPs. In this manuscript, through an experimental study, we assess the speed, recorded signal quality, and system accuracy of a language-model-assisted BCI typing system using three different presentation paradigms: a 4 × 7 matrix paradigm of a 28-character alphabet with row-column presentation (RCP) and single-character presentation (SCP), and rapid serial visual presentation (RSVP) of the same. Our analyses show that signal quality and classification accuracy are comparable between the two visual stimulus presentation paradigms. In addition, we observe that while the matrix-based paradigm can be generally employed with lower inter-trial-interval (ITI) values, the best presentation paradigm and ITI value configuration is user dependent. This potentially warrants offering both presentation paradigms and variable ITI options to users of BCI typing systems.

Manifold Learning by Preserving Distance Orders.

Nonlinear dimensionality reduction is essential for the analysis and the interpretation of high dimensional data sets. In this manuscript, we propose a distance order preserving manifold learning algorithm that extends the basic mean-squared error cost function used mainly in multidimensional scaling (MDS)-based methods. We develop a constrained optimization problem by assuming explicit constraints on the order of distances in the low-dimensional space. In this optimization problem, as a generalization of MDS, instead of forcing a linear relationship between the distances in the high-dimensional original and low-dimensional projection space, we learn a non-decreasing relation approximated by radial basis functions. We compare the proposed method with existing manifold learning algorithms using synthetic datasets based on the commonly used residual variance and proposed percentage of violated distance orders metrics. We also perform experiments on a retinal image dataset used in Retinopathy of Prematurity (ROP) diagnosis.

Noninvasive brain-computer interfaces for augmentative and alternative communication.

Brain-computer interfaces (BCIs) promise to provide a novel access channel for assistive technologies, including augmentative and alternative communication (AAC) systems, to people with severe speech and physical impairments (SSPI). Research on the subject has been accelerating significantly in the last decade and the research community took great strides toward making BCI-AAC a practical reality to individuals with SSPI. Nevertheless, the end goal has still not been reached and there is much work to be done to produce real-world-worthy systems that can be comfortably, conveniently, and reliably used by individuals with SSPI with help from their families and care givers who will need to maintain, setup, and debug the systems at home. This paper reviews reports in the BCI field that aim at AAC as the application domain with a consideration on both technical and clinical aspects.

Brain-computer interface with language model-electroencephalography fusion for locked-in syndrome.

BACKGROUND: Some noninvasive brain-computer interface (BCI) systems are currently available for locked-in syndrome (LIS) but none have incorporated a statistical language model during text generation. OBJECTIVE: To begin to address the communication needs of individuals with LIS using a noninvasive BCI that involves rapid serial visual presentation (RSVP) of symbols and a unique classifier with electroencephalography (EEG) and language model fusion. METHODS: The RSVP Keyboard was developed with several unique features. Individual letters are presented at 2.5 per second. Computer classification of letters as targets or nontargets based on EEG is performed using machine learning that incorporates a language model for letter prediction via Bayesian fusion enabling targets to be presented only 1 to 4 times. Nine participants with LIS and 9 healthy controls were enrolled. After screening, subjects first calibrated the system, and then completed a series of balanced word generation mastery tasks that were designed with 5 incremental levels of difficulty, which increased by selecting phrases for which the utility of the language model decreased naturally. RESULTS: Six participants with LIS and 9 controls completed the experiment. All LIS participants successfully mastered spelling at level 1 and one subject achieved level 5. Six of 9 control participants achieved level 5. CONCLUSIONS: Individuals who have incomplete LIS may benefit from an EEG-based BCI system, which relies on EEG classification and a statistical language model. Steps to further improve the system are discussed.

Offline analysis of context contribution to ERP-based typing BCI performance.

OBJECTIVE: We aim to increase the symbol rate of electroencephalography (EEG) based brain-computer interface (BCI) typing systems by utilizing context information. APPROACH: Event related potentials (ERP) corresponding to a stimulus in EEG can be used to detect the intended target of a person for BCI. This paradigm is widely utilized to build letter-by-letter BCI typing systems. Nevertheless currently available BCI typing systems still require improvement due to low typing speeds. This is mainly due to the reliance on multiple repetitions before making a decision to achieve higher typing accuracy. Another possible approach to increase the speed of typing while not significantly reducing the accuracy of typing is to use additional context information. In this paper, we study the effect of using a language model (LM) as additional evidence for intent detection. Bayesian fusion of an n-gram symbol model with EEG features is proposed, and a specifically regularized discriminant analysis ERP discriminant is used to obtain EEG-based features. The target detection accuracies are rigorously evaluated for varying LM orders, as well as the number of ERP-inducing repetitions. MAIN RESULTS: The results demonstrate that the LMs contribute significantly to letter classification accuracy. For instance, we find that a single-trial ERP detection supported by a 4-gram LM may achieve the same performance as using 3-trial ERP classification for the non-initial letters of words. SIGNIFICANCE: Overall, the fusion of evidence from EEG and LMs yields a significant opportunity to increase the symbol rate of a BCI typing system.

Real-time CHF detection from ECG signals using a novel discretization method.

This study proposes a new method, equal frequency in amplitude and equal width in time (EFiA-EWiT) discretization, to discriminate between congestive heart failure (CHF) and normal sinus rhythm (NSR) patterns in ECG signals. The ECG unit pattern concept was introduced to represent the standard RR interval, and our method extracted certain features from the unit patterns to classify by a primitive classifier. The proposed method was tested on two classification experiments by using ECG records in Physiobank databases and the results were compared to those from several previous studies. In the first experiment, an off-line classification was performed with unit patterns selected from long ECG segments. The method was also used to detect CHF by real-time ECG waveform analysis. In addition to demonstrating the success of the proposed method, the results showed that some unit patterns in a long ECG segment from a heart patient were more suggestive of disease than the others. These results indicate that the proposed approach merits additional research.

Epileptic seizure detection using probability distribution based on equal frequency discretization.

In this study, we offered a new feature extraction approach called probability distribution based on equal frequency discretization (EFD) to be used in the detection of epileptic seizure from electroencephalogram (EEG) signals. Here, after EEG signals were discretized by using EFD method, the probability densities of the signals were computed according to the number of data points in each interval. Two different probability density functions were defined by means of the polynomial curve fitting for the subjects without epileptic seizure and the subjects with epileptic seizure, and then when using the mean square error criterion for these two functions, the success of epileptic seizure detection was 96.72%. In addition, when the probability densities of EEG segments were used as the inputs of a multilayer perceptron neural network (MLPNN) model, the success of epileptic seizure detection was 99.23%. This results show that non-linear classifiers can easily detect the epileptic seizures from EEG signals by means of probability distribution based on EFD.

RSVP Keyboard: An EEG Based Typing Interface.

Humans need communication. The desire to communicate remains one of the primary issues for people with locked-in syndrome (LIS). While many assistive and augmentative communication systems that use various physiological signals are available commercially, the need is not satisfactorily met. Brain interfaces, in particular, those that utilize event related potentials (ERP) in electroencephalography (EEG) to detect the intent of a person noninvasively, are emerging as a promising communication interface to meet this need where existing options are insufficient. Existing brain interfaces for typing use many repetitions of the visual stimuli in order to increase accuracy at the cost of speed. However, speed is also crucial and is an integral portion of peer-to-peer communication; a message that is not delivered timely often looses its importance. Consequently, we utilize rapid serial visual presentation (RSVP) in conjunction with language models in order to assist letter selection during the brain-typing process with the final goal of developing a system that achieves high accuracy and speed simultaneously. This paper presents initial results from the RSVP Keyboard system that is under development. These initial results on healthy and locked-in subjects show that single-trial or few-trial accurate letter selection may be possible with the RSVP Keyboard paradigm.

Selective transitional zone sampling approach versus random biopsy in cases with malignant liver masses: is there any superiority?

BACKGROUND/AIMS: Currently, the diagnostic sensitivity of malignant liver mass biopsies is an important problem in the definitive diagnosis. In this study, we aimed to investigate the role of selective peripheral approach to lesion biopsies for diagnostic sensitivity of liver masses. MATERIALS AND METHODS: Between June 2007 and March 2011, totally 88 patients (50 male, 38 female), referred to our Interventional Radiology Department for sonographically guided Tru-cut biopsies for liver lesions, were examined.All biopsies were performed by an experienced radiologist with an 18-gauge Tru-cut biopsy needle with a spring-loaded biopsy gun under sonographic guidance. We describe two locations (peripheral and central) for liver lesions, with the inner 2/3 part of the mass as central and the outer 1/3 part as peripheral. We obtained biopsy from both of these locations, and samples were transferred to the Pathology Department separately. RESULTS: According to pathological and immunohistochemistry studies, there were 42 hepatocellular carcinomas and 46 metastases. All of the metastatic tumors were stained by cytokeratin (10 lung adenocarcinoma, 15 breast adenocarcinoma, 16 gastrointestinal tract, 4 prostate, and 1 malignant melanoma of these 46 metastases were reported as primary). According to histopathological results, diagnostic sensitivity was 97.7% in peripherally located biopsies and 86.3% in biopsies taken from the center of the masses (p=0.0063). CONCLUSIONS: Selective peripheral biopsy approach in Tru-cut biopsies of liver lesions has better sensitivity rates for histopathologic diagnosis compared to the centrally located and random biopsies.

Sonographic appearance of the normal appendix in children.

PURPOSE: To determine the visualization rate of the appendix in children without appendicitis and investigate factors affecting it. METHODS: Between January 2010 and April 2010, 205 consecutive children (103 boys and 102 girls; mean age: 9 years) without clinical signs of appendicitis were examined by ultrasound (US). The location of appendix was determined. The outer appendiceal diameter with and without compression was measured and the content of the lumen and mural vascularity on color Doppler was determined. The appendix diameter was correlated with age, weight, and height using Pearson correlation. For age, weight, and height, children with a visualized appendix were compared with those in whom the appendix was not visualized using Student's t test. RESULTS: The appendix was visualized in 142 of 205 (69.3%) children. The mean diameters of the appendices without and with compression were 4.2 ± 0.9 mm and 3.5 ± 0.8 mm, respectively, with 53.5% of the appendices in the mid-pelvic location. Appendiceal lumen was empty in 35.2% of children. Only one appendix showed mural vascularity on color Doppler. There was no correlation between the diameter (compressed or noncompressed) of the appendix and age, weight, or height. Mean ± SD age, weight, and height of the children with a visualized appendix (8.6 ± 0.3 years, 29.9 ± 0.9 kg, 127.7 ± 1.7 cm, respectively) were significantly lower than those in children with a nonvisualized appendix (9.8 ± 0.4 years, 36.0 ± 1.8 kg, 134.7 ± 2.5 cm, respectively) (p < 0.05 for all three parameters). CONCLUSION: In the majority of the children, the appendix can be visualized with US. Age, weight, and height affect the visualization rate of the normal appendix.

Context Information Significantly Improves Brain Computer Interface Performance - a Case Study on Text Entry Using a Language Model Assisted BCI.

We present recent results on the design of the RSVP Keyboard - a brain computer interface (BCI) for expressive language generation for functionally locked-in individuals using rapid serial visual presentation of letters or other symbols such as icons. The proposed BCI design tightly incorporates probabilistic contextual information obtained from a language model into the single or multi-trial event related potential (ERP) decision mechanism. This tight fusion of contextual information with instantaneous and independent brain activity is demonstrated to potentially improve accuracy in a dramatic manner. Specifically, a simple regularized discriminant single-trial ERP classifier's performance can be increased from a naive baseline of 75% to 98% in a 28-symbol alphabet operating at 5% false ERP detection rate. We also demonstrate results which show that trained healthy subjects can achieve real-time typing accuracies over 90% mostly relying on single-trial ERP evidence when supplemented with a rudimentary n-gram language model. Further discussion and preliminary results include our initial efforts involving a locked-in individual and our efforts to train him to improve his skill in performing the task.

Fusion with language models improves spelling accuracy for ERP-based brain computer interface spellers.

Event related potentials (ERP) corresponding to a stimulus in electroencephalography (EEG) can be used to detect the intent of a person for brain computer interfaces (BCI). This paradigm is widely utilized to build letter-by-letter text input systems using BCI. Nevertheless using a BCI-typewriter depending only on EEG responses will not be sufficiently accurate for single-trial operation in general, and existing systems utilize many-trial schemes to achieve accuracy at the cost of speed. Hence incorporation of a language model based prior or additional evidence is vital to improve accuracy and speed. In this paper, we study the effects of Bayesian fusion of an n-gram language model with a regularized discriminant analysis ERP detector for EEG-based BCIs. The letter classification accuracies are rigorously evaluated for varying language model orders as well as number of ERP-inducing trials. The results demonstrate that the language models contribute significantly to letter classification accuracy. Specifically, we find that a BCI-speller supported by a 4-gram language model may achieve the same performance using 3-trial ERP classification for the initial letters of the words and using single trial ERP classification for the subsequent ones. Overall, fusion of evidence from EEG and language models yields a significant opportunity to increase the word rate of a BCI based typing system.