Reza Fazel-Rezai

P300 brain computer interface: current challenges and emerging trends

A brain-computer interface (BCI) enables communication without movement based on brain signals measured with electroencephalography (EEG). BCIs usually rely on one of three types of signals: the P300 and other components of the event-related potential (ERP), steady state visual evoked potential (SSVEP), or event related desynchronization (ERD). Although P300 BCIs were introduced over twenty years ago, the past few years have seen a strong increase in P300 BCI research. This closed-loop BCI approach relies on the P300 and other components of the ERP, based on an oddball paradigm presented to the subject. In this paper, we overview the current status of P300 BCI technology, and then discuss new directions: paradigms for eliciting P300s; signal processing methods; applications; and hybrid BCIs. We conclude that P300 BCIs are quite promising, as several emerging directions have not yet been fully explored and could lead to improvements in bit rate, reliability, usability, and flexibility.

Human Error in P300 Speller Paradigm for Brain-Computer Interface

A brain-computer interface (BCI) is a system that conveys messages and commands directly from the human brain to a computer. The BCI system described in this work is based on P300 speller BCI paradigm designed by Farwell and Donchin in 1988. It has been the most widely used and a benchmark in P300 BCI. In this paradigm, a 6x6 matrix of letters and numbers is displayed and subject focuses on a character while different rows and columns flash. The work presented in this paper is an attempt to improve the accuracy of P300 BCI systems by understanding a source of error in this paradigm. It is shown that adjacent rows and columns to the target ones play major role in the error. This can be attributed to human error that when the adjacent row or column to the target one flashes, it attracts subjects attention and creates the P300.

A review of hybrid brain-computer interface systems

Increasing number of research activities and different types of studies in brain-computer interface (BCI) systems show potential in this young research area. Research teams have studied features of different data acquisition techniques, brain activity patterns, feature extraction techniques, methods of classifications, and many other aspects of a BCI system. However, conventional BCIs have not become totally applicable, due to the lack of high accuracy, reliability, low information transfer rate, and user acceptability. A new approach to create a more reliable BCI that takes advantage of each system is to combine two or more BCI systems with different brain activity patterns or different input signal sources. This type of BCI, called hybrid BCI, may reduce disadvantages of each conventional BCI system. In addition, hybrid BCIs may create more applications and possibly increase the accuracy and the information transfer rate. However, the type of BCIs and their combinations should be considered carefully. In this paper, after introducing several types of BCIs and their combinations, we review and discuss hybrid BCIs, different possibilities to combine them, and their advantages and disadvantages.

A region-based P300 speller for brain-computer interface

A brain-computer interface (BCI) is a system that conveys messages and commands directly from the human brain to a computer. The BCI system described in this work is based on the P300 wave. The P300 is a positive peak of an event-related potential (ERP) that occurs 300 ms after a stimulus. One of the best-known and most widely used P300 applications is the P300 speller designed by Farwell-Donchin in 1988. The Farwell-Donchin paradigm has been a benchmark for P300 BCIs. In this paradigm, a 6 X 6 matrix of letters and numbers is displayed, and the subject focuses on a target character while rows and columns of characters flash. Through detection of P300 for one row and one column, the target character can be identified. In this paper, it is shown that there is a human perceptual error in the Farwell-Donchin paradigm. To eliminate this error, a new region-based paradigm is presented. Using experimental results, it is shown that the new paradigm has several advantages over the Farwell-Donchin paradigm and achieves better accuracy.

Performance measurement for brain–computer or brain–machine interfaces: a tutorial

OBJECTIVE Brain-computer interfaces (BCIs) have the potential to be valuable clinical tools. However, the varied nature of BCIs, combined with the large number of laboratories participating in BCI research, makes uniform performance reporting difficult. To address this situation, we present a tutorial on performance measurement in BCI research. APPROACH A workshop on this topic was held at the 2013 International BCI Meeting at Asilomar Conference Center in Pacific Grove, California. This paper contains the consensus opinion of the workshop members, refined through discussion in the following months and the input of authors who were unable to attend the workshop. MAIN RESULTS Checklists for methods reporting were developed for both discrete and continuous BCIs. Relevant metrics are reviewed for different types of BCI research, with notes on their use to encourage uniform application between laboratories. SIGNIFICANCE Graduate students and other researchers new to BCI research may find this tutorial a helpful introduction to performance measurement in the field.

A Low Cost Human Computer Interface based on Eye Tracking

This paper describes the implementation of a human computer interface based on eye tracking. Current commercially available systems exist, but have limited use due mainly to their large cost. The system described in this paper was designed to be a low cost and unobtrusive. The technique was video-oculography assisted by corneal reflections. An off-the shelf CCD webcam was used to capture images. The images were analyzed in software to extract key features of the eye. The users gaze point was then calculated based on the relative position of these features. The system is capable of calculating eye-gaze in real-time to provide a responsive interaction. A throughput of eight gaze points per second was achieved. The accuracy of the fixations based on the calculated eye-gazes were within 1 cm of the on-screen gaze location. By developing a low-cost system, this technology is made accessible to a wider range of applications

Analysis of P300 Classifiers in Brain Computer Interface Speller

In this paper, the performance of five classifiers in P300 speller paradigm are compared. Theses classifiers are Linear Support Vector Machine (LSVM), Gaussian Support Vector Machine (GSVM), Neural Network (NN), Fisher Linear Discriminant (FLD), and Kernel Fisher Discriminant (KFD). In classification of P300 waves, there has been a trend to use SVM classifiers. Although they have shown a good performance, in this paper, it is shown that the FLD classifiers outperform the SVM classifiers. FLD classifier uses only ten channels of the recorded electroencephalogram (EEG) signals. This makes them a very good candidate for real-time applications. In addition, FLD approach does not need any optimization similar to other methods. In addition, in this paper, it is shown that the efficiency of using Principal Component Analysis (PCA) for feature reduction results in decreasing the time for the classification and increasing the accuracy

Automatic Annotation of Seismocardiogram With High-Frequency Precordial Accelerations

Seismocardiogram (SCG) is the low-frequency vibrations signal recorded from the chest using accelerometers. Peaks on dorsoventral and sternal SCG correspond to specific cardiac events. Prior research work has shown the potential of extracting such peaks for various types of monitoring and diagnosis applications. However, annotation of these peaks is not a trivial task and complicated in some subjects. In this paper, an automated method is proposed to annotate these peaks. The high-frequency accelerations obtained from the same accelerometer, used to record SCG with, were used to facilitate the annotation of the SCG. Algorithms were developed for detection of isovolumic moment (IM) and aortic valve closure (AC) points of SCG. Four different envelope calculation methods were used: cardiac sound characteristic waveform (CSCW), Shannon, absolute, and Hilbert. The algorithms were evaluated based on a dataset including 18 subjects undergoing lower body negative pressure and were further tested with another dataset, which included 67 subjects. These datasets had been previously manually annotated. The algorithm based on CSCW envelope calculation produced the highest detection accuracy for both IM and AC. The overall CSCW algorithm detection accuracy for the test dataset was 98.7% and 99.1% for the IM and AC points, respectively.

Human performance evaluation based on EEG signal analysis: A prospective review

Electroencephalogram (EEG) signal, the signature of brain activity, can be used to quantify for human performance evaluation. There are ongoing efforts by scientists and researchers in this area. Different traditional and novel signal processing and analysis methods have been applied to evaluate performance, mental workload, and task engagement based on EEG signals. Linear change in the indices with the increase in task difficulty was reported. In addition, EEG index has been used as parameter for performance optimization. In this review article, we will discuss briefly the literature on human performance estimation based on some physiological parameters, EEG in particular. In this paper, the current state of the research field is presented and possible future research options are discussed.

Semi-Automatic Prostate Segmentation of MR Images Based on Flow Orientation

Prostate cancer is one of the leading causes of death in men. Accurate segmentation of prostate magnetic resonance imagery allows for the maximum volume of the prostate to be considered in diagnosis, using magnetic resonance spectroscopy, and in treatment using intensity modulated radiotherapy. In this work a semi-automatic method which segments the prostate on magnetic resonance images is presented. This algorithm evaluates the curve of the prostate in an orientation based frame work. Having computed the edge direction of each individual pixel in the region surrounding the prostate, and considering the location of four points of this region that have been previously selected by the user, a statistical average mask of size 3times3 follows the direction of a channel of low intensity pixels around the prostate using prior knowledge of the shape of the object. The initial results show promising results when comparing this method to the segmentation done by an expert