Online removal of eye movement and blink EEG artifacts using a high-speed eye tracker.

A novel approach is presented for using an eye tracker-based reference instead of EOG for methods that require an EOG reference to remove ocular artifacts (OA) from EEG. It uses a high-speed eye tracker and a new online algorithm for extracting the time course of a blink from eye tracker images to remove both eye movement and blink artifacts. It eliminates the need for EOG electrodes attached to the face, which is critical for practical daily applications. The ability of two adaptive filters (RLS and H^ ) to remove OA is measured using: 1) EOG; 2) frontal EEG only (fEEG); and 3) the eye tracker with frontal EEG (ET + fEEG) as reference inputs. The results are compared for different eye movements and blinks of varying amplitudes at electrodes across the scalp. Both the RLS and H^ methods were shown to benefit from using the proposed eye tracker-based reference (ET + fEEG) instead of either an EOG reference or a reference based on frontal EEG alone.

A self-paced brain-computer interface system with a low false positive rate.

The performance of current EEG-based self-paced brain-computer interface (SBCI) systems is not suitable for most practical applications. In this paper, an improved SBCI that uses features extracted from three neurological phenomena (movement-related potentials, changes in the power of Mu rhythms and changes in the power of Beta rhythms) to detect an intentional control command in noisy EEG signals is proposed. The proposed system achieves a high true positive (TP) to false positive (FP) ratio. To extract features for each neurological phenomenon in every EEG signal, a method that consists of a stationary wavelet transform followed by matched filtering is developed. For each neurological phenomenon in every EEG channel, features are classified using a support vector machine classifier (SVM). For each neurological phenomenon, a multiple classifier system (MCS) then combines the outputs of the SVMs. Another MCS combines the outputs of MCSs designed for the three neurological phenomena. Various configurations for combining the outputs of these MCSs are considered. A hybrid genetic algorithm (HGA) is proposed to simultaneously select the features, the values of the classifiers' parameters and the configuration for combining MCSs that yield the near optimal performance. Analysis of the data recorded from four able-bodied subjects shows a significant performance improvement over previous SBCIs.

A comprehensive survey of brain interface technology designs.

In this work we present the first comprehensive survey of Brain Interface (BI) technology designs published prior to January 2006. Detailed results from this survey, which was based on the Brain Interface Design Framework proposed by Mason and Birch, are presented and discussed to address the following research questions: (1) which BI technologies are directly comparable, (2) what technology designs exist, (3) which application areas (users, activities and environments) have been targeted in these designs, (4) which design approaches have received little or no research and are possible opportunities for new technology, and (5) how well are designs reported. The results of this work demonstrate that meta-analysis of high-level BI design attributes is possible and informative. The survey also produced a valuable, historical cross-reference where BI technology designers can identify what types of technology have been proposed and by whom.

Analyzing trends in brain interface technology: a method to compare studies.

Continued progress in the field of Brain Interface (BI) research has encouraged the rapid expansion of the BI community over the last two decades. As the number of BI researchers and organizations steadily increases, newer and more advanced technologies are constantly produced, evaluated, and reported. Though the BI community is committed to accurate and objective evaluation of methods, systems, and technology, the diversity of the field has hindered the development of objective methods of comparison. This paper introduces a new method for directly comparing studies of BI technology based on the theoretical models and taxonomy proposed by Mason, Moore, and Birch. The effectiveness of the proposed method was demonstrated by interpreting and comparing a representative set of 21 BI studies. The method allowed us to 1) identify the salient aspects of a specific BI study, 2) identify what has been reported and what has been omitted, 3) facilitate a complete and objective comparison with other studies, and 4) characterize overall trends, areas of inactivity, and reporting practices.

A general framework for characterizing studies of brain interface technology.

The development of brain interface (BI) technology continues to attract researchers with a wide range of backgrounds and expertise. Though the BI community is committed to accurate and objective evaluation of methods, systems, and technology, the very diversity of the methods and terminology used in the field hinders understanding and impairs technology cross-fertilization and cross-group validation of findings. Underlying this dilemma is a lack of common perspective and language. As seen in our previous works in this area, our approach to remedy this problem is to propose language in the form of taxonomy and functional models. Our intent is to document and validate our best thinking in this area and publish a perspective that will stimulate discussion. We encourage others to do the same with the belief that focused discussion on language issues will accelerate the inherently slow natural evolution of language selection and thus alleviate related problems. In this work, we propose a theoretical framework for describing BI-technology-related studies. The proposed framework is based on the theoretical concepts and terminology from classical science, assistive technology development, human-computer interaction, and previous BI-related works. Using a representative set of studies from the literature, the proposed BI study framework was shown to be complete and appropriate perspective for thoroughly characterizing a BI study. We have also demonstrated that this BI study framework is useful for (1) objectively reviewing existing BI study designs and results, (2) comparing designs and results of multiple BI studies, (3) designing new studies or objectively reporting BI study results, and (4) facilitating intra- and inter-group communication and the education of new researchers. As such, it forms a sound and appropriate basis for community discussion.

An automatic method to generate ensemble averages of movement-related potentials for individuals with spinal cord injuries.

Ensemble averaging of the electroencephalogram is known to be a good tool for characterizing various event related potentials. An important part of ensemble averaging is to know the time reference that the signals should be averaged. In able-bodied individuals the muscle activity or switch activation is used to time-lock the averages. In people with spinal cord injuries who lack the ability to produce muscle activity, the expected time of the attempted movement based on an external cue can be used. This time is not accurate and can result in poor ensemble averages. A method that automatically detects the onset of the movement related potentials and use this knowledge to time-lock the averages is introduced. This method is based on the estimation of the probability density distribution of the feature vectors related to spontaneous EEG. To estimate the probability density function Parzen's method is used which is known to be as the most accurate method when large population of data is available. Preliminary experiments demonstrate the feasibility of the proposed method and show that the proposed method could generate ensemble averages closer to the averages with muscle activity knowledge than the method based on an external cue.

A brain-controlled switch for asynchronous control applications.

Asynchronous control applications are an important class of application that has not received much attention from the brain-computer interface (BCI) community. This work provides a design for an asynchronous BCI switch and performs the first extensive evaluation of an asynchronous device in attentive, spontaneous electroencephalographic (EEG). The switch design [named the low-frequency asynchronous switch design (LF-ASD)] is based on a new feature set related to imaginary movements in the 1-4 Hz frequency range. This new feature set was identified from a unique analysis of EEG using a bi-scale wavelet. Offline evaluations of a prototype switch demonstrated hit (true positive) rates in the range of 38%-81% with corresponding false positive rates in the range of 0.3%-11.6%. The performance of the LF-ASD was contrasted with two other ASDs: one based on mu-power features and another based on the outlier processing method (OPM) algorithm. The minimum mean error rates for the LF-ASD were shown to be significantly lower than either of these other two switch designs.

Brain-computer interface research at the Neil Squire Foundation.

The ultimate goal of our research is to utilize voluntary motor-related potentials recorded from the scalp in a direct Brain Computer Interface for asynchronous control applications. This type of interface will allow an individual with a high-level impairment to have effective and sophisticated control of devices such as wheelchairs, robotic assistive appliances, computers, and neural prostheses.

Single-trial processing of event-related potentials using outlier information.

A new approach to extracting single-trial event-related information is described in this paper. This approach, called the outlier processing method (OPM), is based on the concept that event-related information is contained in EEG time-series outliers. In particular, the OPM has been effective in extracting motor-related information from single-trial EEG. An investigation into the viability of the OPM was carried out on single-trial EEG data from four subjects. The EEG was collected under two conditions: an active task in which the subject performed a skilled thumb movement and an idle task in which the subject remained alert but did not carry out any motor activity. The results of this investigation demonstrated that consistent single-trial motor related information can be successfully extracted using the OPM.