Search

The partnered research method, used routinely in other fields, offers great potential to improve the quality and practical use of applied psychology research. Partnered research integrates the perspectives of researchers, knowledge users, people who have lived experience with the attributes being studied, and other stakeholders in all elements of the research process, from the creation and generation of research questions to the methods used, the data analyzed, and the dissemination, application, and implementation of research results. We explain the concept of partnered research and provide a step‐by‐step roadmap for applied psychology scholars interested in conducting partnered research. In doing so, we also address common challenges with this method and provide advice on how to overcome them. We embed our description of the partnered research approach primarily in the context of research on disabilities and work but also offer examples drawn from other areas of applied psychology. [ABSTRACT FROM AUTHOR]

One avenue of research for partial restoration of function following spinal cord injury is the use of neural prostheses, an example of which is functional electrical stimulation (FES) devices for motor functions. Neural prostheses may also be useful for the extraction of sensory information directly from the nervous system. We suggest the spinal cord as a possible site for the detection of peripheral sensory information from neural activity alone. Acute multichannel extracellular recordings were used to extract neural spike activity elicited from peripheral sensations from the spinal cords of rats. To test the recording method and classification potential, eight classes of sensory events were recorded consisting of electricai stimulation of seven locations on rat forepaws, and another class of data during which no stimulus was present. A dual-stage classification scheme using principal component analysis and k-Means clustering was devised to classify the sensory events during single trials. The eight tasks were correctly identified at a mean accuracy of 96%. Thus, we have shown the methodology to detect and classify peripheral sensory information from multichannel recordings of the spinal cord. These methods may be useful, for example, in a closed-loop FES for restoration of hand grasp. Index Terms--Brain interface, functional electrical stimulation, neural prosthesis, spinal cord injury.

A power spectral analysis study was conducted to investigate the effects of using an electromagnetic motion tracking sensor on an electroencephalogram (EEG) recording system. The results showed that the sensors do not generate any consistent frequency component(s) in the power spectrum of the EEG in the frequencies of interest (0.1-55 Hz). Index Terms--EEG, electroencephalography, motion tracking.

The low-frequency asynchronous switch design (LF-ASD) was introduced as a direct brain-computer interface (BCI) technology for asynchronous control applications. The LF-ASD operates as an asynchronous brain switch (ABS) which is activated only when a user intends control and maintains an inactive state output when the user is not meaning to control the device (i.e., they may be idle, thinking about a problem, or performing some other action). Results from LF-ASD evaluations have shown promise, although the reported error rates are too high for most practical applications. This paper presents the evaluation of four new LF-ASD designs with data collected from individuals with high-level spinal cord injuries and able-bodied subjects. These new designs incorporated electroencephalographic energy normalization and feature space dimensionality reduction. The error characteristics of the new ABS designs were significantly better than the LF-ASD design with true positive rate increases of approximately 33% for false positive rates in the range of 1%-2%. The results demonstrate that the dimensionality of the LF-ASD feature space can be reduced without performance degradation. The results also confirm previous findings that spinal cord-injured subjects can operate ABS designs to the same ability as able-bodied subjects. Index Terms--Assistive technology, asynchronous control, brain-computer interface (BCI), intermittent control, neuroprosthesis, pattern recognition, statistical signal processing.

Abstract Background A novel artefact removal algorithm is proposed for a self-paced hybrid brain-computer interface (BCI) system. This hybrid system combines a self-paced BCI with an eye-tracker to operate a virtual keyboard. To select a letter, the user must gaze at the target for at least a specific period of time (dwell time) and then activate the BCI by performing a mental task. Unfortunately, electroencephalogram (EEG) signals are often contaminated with artefacts. Artefacts change the quality of EEG signals and subsequently degrade the BCI’s performance. Methods To remove artefacts in EEG signals, the proposed algorithm uses the stationary wavelet transform combined with a new adaptive thresholding mechanism. To evaluate the performance of the proposed algorithm and other artefact handling/removal methods, semi-simulated EEG signals (i.e., real EEG signals mixed with simulated artefacts) and real EEG signals obtained from seven participants are used. For real EEG signals, the hybrid BCI system’s performance is evaluated in an online-like manner, i.e., using the continuous data from the last session as in a real-time environment. Results With semi-simulated EEG signals, we show that the proposed algorithm achieves lower signal distortion in both time and frequency domains. With real EEG signals, we demonstrate that for dwell time of 0.0s, the number of false-positives/minute is 2 and the true positive rate (TPR) achieved by the proposed algorithm is 44.7%, which is more than 15.0% higher compared to other state-of-the-art artefact handling methods. As dwell time increases to 1.0s, the TPR increases to 73.1%. Conclusions The proposed artefact removal algorithm greatly improves the BCI’s performance. It also has the following advantages: a) it does not require additional electrooculogram/electromyogram channels, long data segments or a large number of EEG channels, b) it allows real-time processing, and c) it reduces signal distortion.

Abstract Background Recently, successful applications of the discrete wavelet transform have been reported in brain interface (BI) systems with one or two EEG channels. For a multi-channel BI system, however, the high dimensionality of the generated wavelet features space poses a challenging problem. Methods In this paper, a feature selection method that effectively reduces the dimensionality of the feature space of a multi-channel, self-paced BI system is proposed. The proposed method uses a two-stage feature selection scheme to select the most suitable movement-related potential features from the feature space. The first stage employs mutual information to filter out the least discriminant features, resulting in a reduced feature space. Then a genetic algorithm is applied to the reduced feature space to further reduce its dimensionality and select the best set of features. Results An offline analysis of the EEG signals (18 bipolar EEG channels) of four able-bodied subjects showed that the proposed method acquires low false positive rates at a reasonably high true positive rate. The results also show that features selected from different channels varied considerably from one subject to another. Conclusion The proposed hybrid method effectively reduces the high dimensionality of the feature space. The variability in features among subjects indicates that a user-customized BI system needs to be developed for individual users.

A problem that impedes the progress in Brain-Computer Interface (BCI) research is the difficulty in reproducing the results of different papers. Comparing different algorithms at present is very difficult. Some improvements have been made by the use of standard datasets to evaluate different algorithms. However, the lack of a comparison framework still exists. In this paper, we construct a new general comparison framework to compare different algorithms on several standard datasets. All these datasets correspond to sensory motor BCIs, and are obtained from 21 subjects during their operation of synchronous BCIs and 8 subjects using self-paced BCIs. Other researchers can use our framework to compare their own algorithms on their own datasets. We have compared the performance of different popular classification algorithms over these 29 subjects and performed statistical tests to validate our results. Our findings suggest that, for a given subject, the choice of the classifier for a BCI system depends on the feature extraction method used in that BCI system. This is in contrary to most of publications in the field that have used Linear Discriminant Analysis (LDA) as the classifier of choice for BCI systems. [ABSTRACT FROM AUTHOR]

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^\infty) 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^\infty 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. [ABSTRACT FROM PUBLISHER]

Background: A novel artefact removal algorithm is proposed for a self-paced hybrid brain-computer interface (BCI) system. This hybrid system combines a self-paced BCI with an eye-tracker to operate a virtual keyboard. To select a letter, the user must gaze at the target for at least a specific period of time (dwell time) and then activate the BCI by performing a mental task. Unfortunately, electroencephalogram (EEG) signals are often contaminated with artefacts. Artefacts change the quality of EEG signals and subsequently degrade the BCI's performance. Methods: To remove artefacts in EEG signals, the proposed algorithm uses the stationary wavelet transform combined with a new adaptive thresholding mechanism. To evaluate the performance of the proposed algorithm and other artefact handling/removal methods, semi-simulated EEG signals (i.e., real EEG signals mixed with simulate artefacts) and real EEG signals obtained from seven participants are used. For real EEG signals, the hybrid BCI system's performance is evaluated in an online-like manner, i.e., using the continuous data from the last session as in a real-time environment. Results: With semi-simulated EEG signals, we show that the proposed algorithm achieves lower signal distortion in both time and frequency domains. With real EEG signals, we demonstrate that for dwell time of 0.0s, the number of false-positives/minute is 2 and the true positive rate (TPR) achieved by the proposed algorithm is 44.7%, which is more than 15.0% higher compared to other state-of-the-art artefact handling methods. As dwell time increases to 1.0s, the TPR increases to 73.1%. Conclusions: The proposed artefact removal algorithm greatly improves the BCI's performance. It also has the following advantages: a) it does not require additional electrooculogram/electromyogram channels, long data segments or a large number of EEG channels, b) it allows real-time processing, and c) it reduces signal distortion. [ABSTRACT FROM AUTHOR]

The performance of a specific self-paced BCI (SBCI) is investigated using two different datasets to determine its suitability for using online: (1) data contaminated with large-amplitude eye movements, and (2) data recorded in a session subsequent to the original sessions used to design the system. No part of the data was rejected in the subsequent session. Therefore, this dataset can be regarded as a "pseudo-online" test set. The SBCI under investigation uses features extracted from three specific neurological phenomena. Each of these neurological phenomena belongs to a different frequency band. Since many prominent artifacts are either of mostly low-frequency (e.g., eye movements) or mostly high-frequency nature (e.g., muscle movements), it is expected that the system shows a fairly robust performance over artifact-contaminated data. Analysis of the data of four participants using epochs contaminated with large-amplitude eye-movement artifacts shows that the system's performance deteriorates only slightly. Furthermore, the system's performance during the session subsequent to the original sessions remained largely the same as in the original sessions for three out of the four participants. This moderate drop in performance can be considered tolerable, since allowing artifact-contaminated data to be used as inputs makes the system available for users at ALL times. [ABSTRACT FROM AUTHOR]

Direct brain interface (BI) systems provide an alternative communication and control solution for individuals with severe motor disabilities, bypassing impaired interface pathways. Most BI systems are aimed to be operated by individuals with severe disabilities. With these individuals, there is no observable indicator of their intent to control or communicate with the BI system. In contrast, able-bodied subjects can perform the desired physical movements such as finger flexion and one can observe the movement as the indicator of intent. Since no external knowledge of intention is available for individuals with severe motor disabilities, generating the data for system training is problematic. This paper introduces three methods for generating training-data for self-paced BI systems and compares their performances with four alternative methods of training-data generation. Results of the offline analysis on the electroencephalogram data of eight subjects during self-paced BI experiments show that two of the proposed methods increase true positive rates (at fixed false positive rate of 2%) over that of the four alternative methods from 50.8 %-58.4% to about 62% which corresponds to 3.6%-11.2% improvement. [ABSTRACT FROM AUTHOR]

Most existing brain-computer interfaces (BCIs) detect specific mental activity in a so-called synchronous paradigm. Unlike synchronous systems which are operational at specific system-defined periods, self-paced (asynchronous) interfaces have the advantage of being operational at all times. The low-frequency asynchronous switch design (LF-ASD) is a 2-state self-paced BCI that detects the presence of a specific finger movement in the ongoing EEG. Recent evaluations of the 2-state LF-ASD show an average true positive rate of 41% at the fixed false positive rate of 1%. This paper proposes two designs for a 3-state self-paced BCI that is capable of handling idle brain state. The two proposed designs aim at detecting right- and left-hand extensions from the ongoing EEG. They are formed of two consecutive detectors. The first detects the presence of a right- or a left-hand movement and the second classifies the detected movement as a right or a left one. In an offline analysis of the EEG data collected from four able-bodied individuals, the 3-state brain-computer interface shows a comparable performance with a 2-state system and significant performance improvement if used as a 2-state BCI, that is, in detecting the presence of a right- or a left-hand movement (regardless of the type of movement). It has an average true positive rate of 37.5% and 42.8% (at false positives rate of 1%) in detecting right- and left-hand extensions, respectively, in the context of a 3-state self-paced BCI and average detection rate of 58.1% (at false positive rate of 1%) in the context of a 2-state self-paced BCI. [ABSTRACT FROM AUTHOR]

The Neil Squire Society has developed asynchronous, direct brain switches for self-paced control applications with mean activation rates of 73% and false positive error rates of 2%. This report summarizes our results to date, lessons learned, and current directions, including research into implanted brain interface designs. [ABSTRACT FROM AUTHOR]

At the recent Second International Meeting on Brain-Computer Interfaces (BCIs) held in June 2002 in Rensselaerville, NY, a formal debate was held on the pros and cons of linear and nonlinear methods in BCI research. Specific examples applying EEG data sets to linear and nonlinear methods are given and an overview of the various pros and cons of each approach is summarized. Overall, it was agreed that simplicity is generally best and, therefore, the use of linear methods is recommended wherever possible. It was also agreed that nonlinear methods in some applications can provide better results, particularly with complex and/or other very large data sets. [ABSTRACT FROM AUTHOR]

The Neil Squire Foundation (NSF) is a Canadian nonprofit organization whose purpose is to create opportunities for independence for individuals who have significant physical disabilities. Over the last ten years, our team in partnership with researchers at the Electrical and Computer Engineering Department, the University of British Columbia, has been working to develop a direct brain-controlled switch for individuals with significant physical disabilities. The NSF Brain Interface Project primarily focuses on the development of brain-computer interface switch technologies for intermittent (or asynchronous) control in natural environments. That is, technologies that will work when the User intends control but also remains in a stable off state when there is no intent to control. A prototype of such a switch has successfully been developed. This switch has demonstrated classification accuracies greater than 94%. The initial results are promising, but further research is required to improve switch accuracies and reliability and to test these switch technologies over a larger population of users and operating conditions. This paper provides an overview of the NSF brain-switch technologies and details our approach to future work in this area. [ABSTRACT FROM AUTHOR]

The Brain-Computer Interface (BCI) research community has acknowledged that researchers are experiencing difficulties when they try to compare the BCI techniques described in the literature. In response to this situation, the community has stressed the need for objective methods to compare BCI technologies. Suggested improvements have included the development and use of benchmark applications and standard data sets. However, as a young, multidisciplinary research field, the BCI community lacks a common vocabulary. As a result, this deficiency leads to poor intergroup communication, which hinders the development of the desired methods of comparison. One of the principle reasons for the lack of common vocabulary is the absence of a common functional model of a BCI System. This paper proposes a new functional model for BCI System design. The model supports many features that facilitate the comparison of BCI technologies with other BCI and non-BCI user interface technologies. From this model, taxonomy for BCI System design is developed. Together the model and taxonomy are considered a general framework for BCI System design. The representational power of the proposed framework was evaluated by applying it to a set of existing BCI technologies. The framework could effectively describe all of the BCI System designs tested. Index Terms—Assistive technology; brain-computer interface (BCI); direct-brain interface (DBI); EEG; framework; functional model; taxonomy. [ABSTRACT FROM AUTHOR]

Presents the results of initial on-line evaluations of low frequency asynchronous switch design (LF-ASD) brain-controlled switch with able-bodied subjects and subjects with high-level spinal-cord injuries. Capability of the users to activate LF-ASD by imagining movement; Rates of imagined voluntary movements; Output of the LF-ASD.

Abstract: The feasibility of having a self-paced brain–computer interface (BCI) based on mental tasks is investigated. The EEG signals of four subjects performing five mental tasks each are used in the design of a 2-state self-paced BCI. The output of the BCI should only be activated when the subject performs a specific mental task and should remain inactive otherwise. For each subject and each task, the feature coefficient and the classifier that yield the best performance are selected, using the autoregressive coefficients as the features. The classifier with a zero false positive rate and the highest true positive rate is selected as the best classifier. The classifiers tested include: linear discriminant analysis, quadratic discriminant analysis, Mahalanobis discriminant analysis, support vector machine, and radial basis function neural network. The results show that: (1) some classifiers obtained the desired zero false positive rate; (2) the linear discriminant analysis classifier does not yield acceptable performance; (3) the quadratic discriminant analysis classifier outperforms the Mahalanobis discriminant analysis classifier and performs almost as well as the radial basis function neural network; and (4) the support vector machine classifier has the highest true positive rates but unfortunately has nonzero false positive rates in most cases. [Copyright &y& Elsevier]

Abstract: It is widely accepted in the brain computer interface (BCI) research community that neurological phenomena are the only source of control in any BCI system. Artifacts are undesirable signals that can interfere with neurological phenomena. They may change the characteristics of neurological phenomena or even be mistakenly used as the source of control in BCI systems. Electrooculography (EOG) and electromyography (EMG) artifacts are considered among the most important sources of physiological artifacts in BCI systems. Currently, however, there is no comprehensive review of EMG and EOG artifacts in BCI literature. This paper reviews EOG and EMG artifacts associated with BCI systems and the current methods for dealing with them. More than 250 refereed journal and conference papers are reviewed and categorized based on the type of neurological phenomenon used and the methods employed for handling EOG and EMG artifacts. This study reveals weaknesses in BCI studies related to reporting the methods of handling EMG and EOG artifacts. Most BCI papers do not report whether or not they have considered the presence of EMG and EOG artifacts in the brain signals. Only a small percentage of BCI papers report automated methods for rejection or removal of artifacts in their systems. As the lack of dealing with artifacts may result in the deterioration of the performance of a particular BCI system during practical applications, it is necessary to develop automatic methods to handle artifacts or to design BCI systems whose performance is robust to the presence of artifacts. [Copyright &y& Elsevier]

As the characteristics of EEG signals change over time, updating the classifier of a brain computer interface, BCI, (over time) would improve the performance of the system. Developing an adaptive classifier for a self-paced BCI however is not easy because the user's intention (and therefore the true labels of the EEG signals) are not known during the operation of the system. For certain applications, it may be possible to predict the labels of some of the EEG segments using some information about the user's state (e.g., the error potentials or gaze information). This study proposes a method that adaptively updates the classifier of a self-paced BCI in a supervised or semi-supervised manner, using those EEG segments whose labels can be predicted. We employ the eye position information obtained from an eye-tracker to predict the EEG labels. This eye-tracker is also used along with a self-paced BCI to form a hybrid BCI system. The results obtained from seven individuals show that the proposed algorithm outperforms the non-adaptive and other unsupervised adaptive classifiers. It achieves a true positive rate of 49.7% and lowers the number of false positives significantly to only 2.2 FPs/minute.

The stationary wavelet packet analysis is exploited for the first time in the design of a self-paced BCI based on mental tasks. The BCI system is custom designed to achieve a zero false positive rate, as false activations highly restricts the applications of BCIs in real life. The EEG signals of four subjects performing five different mental tasks are used as the dataset. The stationary wavelet packets decompose the signal into eight components. The features used are the autoregressive coefficients obtained by applying autoregressive modeling on the resultant wavelet components. Classification is a two-stage process. The first stage is based on quadratic discriminant analysis which is extremely fast. The second stage is a simple majority voting classifier. During model selection, which is performed via 5-folded cross-validation, the combination of decomposed components and the autoregressive model order that yield the best performance are selected. Results show enhancements in the overall performance for three subjects comparing to our previously designed BCI.

We propose a novel metric for quantitatively evaluating ocular artifact (OA) removal methods on real electroencephalogram (EEG) data. For real EEG, existing metrics measure the amount of artifact removed. Our metric measures how much a given method is likely to distort the underlying EEG. The new metric was used to evaluate two existing OA removal algorithms that use the electro-oculogram (EOG) as a reference signal. The combination of a previous metric and our new metric showed there is a trade-off between how well an algorithm removes OAs and how likely it is to distort the underlying EEG. These algorithms require a reference EOG signal, yet for certain applications (e.g., a brain computer interface or BCI) it is preferable or necessary to avoid attaching electrodes around the eyes. We thus also used various combinations of up to 55 channels of EEG to estimate the EOG reference. The metric was again used to compare the use of estimated vs. measured EOG. Our initial results showed that using EOG estimated from as few as 4 EEG electrodes increased the likelihood of distorting the EEG from 14% to 19% and from 21% to 23% for the two algorithms. For some applications (e.g., BCI), the slight reduction in performance may be acceptable in order to avoid using EOG electrodes.

The Common Spatial Patterns (CSP) algorithm finds spatial filters that are useful in discriminating different classes of electroencephalogram (EEG) signals such as those corresponding to different types of motor activities. This algorithm is however, sensitive to outliers because it involves the estimation of covariance matrices. Classical sample covariance estimates are easily affected even if a single outlier exists. To improve the CSP algorithm's robustness against outliers, this paper first investigates how multivariate outliers affect the performance of the CSP algorithm. We then propose a modified version of the algorithm whereby the classical covariance estimates are replaced by the robust covariance estimates obtained using Minimum Covariance Determinant (MCD) estimator. Median Absolute Deviation (MAD) is also used to robustly estimate the variance of the projected EEG signals. The results show that the proposed algorithm is able to reduce the influence of the outliers. When an average of 2.5% outliers is introduced, the average drop in the accuracy is 9.21% for the CSP algorithm and 0.72% for the proposed algorithm.

In previous studies, we proposed a self-paced brain computer interface (SBCI) system that employed three neurological phenomena to identify intentional control (IC) commands from the no control (NC) states of EEG signals. We showed that this SBCI system achieved a good performance that was better than those of other EEG-based SBCI systems. In this paper, we carry out a new study to show that this system can be generalized. Specifically, we show that it can also achieve good performance when 1) a new type of movement is used (hand extension vs. the finger flexion this system was designed for), and 2) NC data are recorded in an engaging environment. A more reliable artifact monitoring system is also added to the system to rule out not only the effects of eye blinks but also the frontalis muscles when controlling the system. Using the data from five participants it is shown that the system obtains good performance compared to other EEG-based SBCI systems.

The presence of false activations inhibits the use of existing self-paced BCIs in real life applications. We present a new design method for a self-paced BCI that yielded 0% false activations using the data of two subjects. This system obtains templates/shapes of the movement related finger flexion patterns. To obtain the templates, the intentional control data are decomposed into 5 levels using the stationary wavelet transform. Then, ensemble averaging is done. These templates are used to train 5 radial basis function neural networks. This is followed by a majority voting classifier.

Background: Recently, successful applications of the discrete wavelet transform have been reported in brain interface (BI) systems with one or two EEG channels. For a multi-channel BI system, however, the high dimensionality of the generated wavelet features space poses a challenging problem., Methods: In this paper, a feature selection method that effectively reduces the dimensionality of the feature space of a multi-channel, self-paced BI system is proposed. The proposed method uses a two-stage feature selection scheme to select the most suitable movement-related potential features from the feature space. The first stage employs mutual information to filter out the least discriminant features, resulting in a reduced feature space. Then a genetic algorithm is applied to the reduced feature space to further reduce its dimensionality and select the best set of features., Results: An offline analysis of the EEG signals (18 bipolar EEG channels) of four able-bodied subjects showed that the proposed method acquires low false positive rates at a reasonably high true positive rate. The results also show that features selected from different channels varied considerably from one subject to another., Conclusion: The proposed hybrid method effectively reduces the high dimensionality of the feature space. The variability in features among subjects indicates that a user-customized BI system needs to be developed for individual users.

The findings of two recent studies that aim at developing a self-paced brain interface (BI) system with low false positive rates are discussed. The first study examines the use of information extracted from different neurological phenomena and the second study examines the wavelet coefficients extracted from a single neurological phenomenon. The analysis of the data of two subjects shows that both are successful at yielding low false positive rates. These studies also show that for each subject, a unique set of features and EEG channels lead to superior performance.