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Gary E Birch

Publications and source records attributed to Gary E Birch.

14 recordsLinked to original sources
PubMed2007

A self-paced brain interface system that uses movement related potentials and changes in the power of brain rhythms.

Movement execution results in the simultaneous generation of movement-related potentials (MRP) as well as changes in the power of Mu and Beta rhythms. This paper proposes a new self-paced multi-channel BI that combines features extracted from MRPs and from changes in the power of Mu and Beta rhythms. We developed a new algorithm to classify the high-dimensional feature space. It uses a two-stage multiple-classifier system (MCS). First, an MCS classifies each neurological phenomenon separately using the information extracted from specific EEG channels (EEG channels are selected by a genetic algorithm). In the second stage, another MCS combines the outputs of MCSs developed in the first stage. Analysis of the data of four able-bodied subjects shows the superior performance of the proposed algorithm compared with a scheme where the features were all combined in a single feature vector and then classified.

Brain↗
PubMed2006

EMG and EOG artifacts in brain computer interface systems: A survey.

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.

Algorithms↗
PubMed2006

Automatic user customization for improving the performance of a self-paced brain interface system.

Customizing the parameter values of brain interface (BI) systems by a human expert has the advantage of being fast and computationally efficient. However, as the number of users and EEG channels grows, this process becomes increasingly time consuming and exhausting. Manual customization also introduces inaccuracies in the estimation of the parameter values. In this paper, the performance of a self-paced BI system whose design parameter values were automatically user customized using a genetic algorithm (GA) is studied. The GA automatically estimates the shapes of movement-related potentials (MRPs), whose features are then extracted to drive the BI. Offline analysis of the data of eight subjects revealed that automatic user customization improved the true positive (TP) rate of the system by an average of 6.68% over that whose customization was carried out by a human expert, i.e., by visually inspecting the MRP templates. On average, the best improvement in the TP rate (an average of 9.82%) was achieved for four individuals with spinal cord injury. In this case, the visual estimation of the parameter values of the MRP templates was very difficult because of the highly noisy nature of the EEG signals. For four able-bodied subjects, for which the MRP templates were less noisy, the automatic user customization led to an average improvement of 3.58% in the TP rate. The results also show that the inter-subject variability of the TP rate is also reduced compared to the case when user customization is carried out by a human expert. These findings provide some primary evidence that automatic user customization leads to beneficial results in the design of a self-paced BI for individuals with spinal cord injury.

Algorithms↗
PubMed2006

User customization of the feature generator of an asynchronous brain interface.

A study that customizes the feature generator parameters of an asynchronous Brain Interface (BI) is discussed. The goal is to detect the presence of a certain pattern, in the ongoing EEG, associated with a specific movement and to improve the system's performance. Results of this study show that customization mostly benefits able-bodied subjects with performance improvements of up to 6.8%. We evaluate the performance of our BI using stratified cross-validation scheme. This scheme repeats the analysis on the different cross-validation sets. It is shown that the performances of the system across the different sets are very similar. Thus, we conclude that a robust performance measure of the system can be obtained by using only one of these performance results.

Artificial Intelligence↗
PubMed2006

An improved asynchronous brain interface: making use of the temporal history of the LF-ASD feature vectors.

The low-frequency asynchronous switch design (LF-ASD) has been introduced as a direct brain interface (BI) for asynchronous control applications. Asynchronous interfaces, as opposed to synchronous interfaces, have the advantage of being operational at all times and not only at specific system-defined periods. This paper modifies the LF-ASD design by incorporating into the system more knowledge about the attempted movements. Specifically, the history of feature values extracted from the EEG signal is used to detect a right index finger movement attempt. Using data collected from individuals with high-level spinal cord injuries and able-bodied subjects, it is shown that the error characteristics of the modified design are significantly better than the previous LF-ASD design. The true positive rate percentage increased by up to 15 which corresponds to 50% improvement when the system is operating with false positive rates in the 1-2% range.

Adult↗
PubMed2006

Detection and classification of sensory information from acute spinal cord recordings.

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 electrical 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.

Action Potentials↗
PubMed2006

Brain interface research for asynchronous control applications.

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.

Algorithms↗
PubMed2004

Brain-computer interface design for asynchronous control applications: improvements to the LF-ASD asynchronous brain switch.

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.

Adult↗
PubMed2003

A general framework for brain-computer interface design.

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.

Algorithms↗
PubMed2003

Current trends in brain-computer interface research at the Neil Squire Foundation.

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.

Adult↗
PubMed2003

Linear and nonlinear methods for brain-computer interfaces.

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.

Algorithms↗
PubMed2002

Initial on-line evaluations of the LF-ASD brain-computer interface with able-bodied and spinal-cord subjects using imagined voluntary motor potentials.

Previous research has focused on developing a brain-controlled switch named the low frequency asynchronous switch design (LF-ASD) that is suitable for intermittent control of devices such as environmental control systems, computers, and neural prostheses. On-line implementations of the LF-ASD have shown promising results in response to actual index finger flexions with able-bodied subjects. This paper reports the results of initial on-line evaluations of the LF-ASD brain-controlled switch with both able-bodied subjects and subjects with high-level spinal-cord injuries. This paper has demonstrated that users can activate the LF-ASD switch by imaging movement. In this paper, two able-bodied subjects were able to control the LF-ASD with imagined voluntary movements with hit (true positive) rates above 70% and false positive rates below 3% while two subjects with high-level spinal-cord injuries demonstrated hit rates ranging from 45-48% and false positive rates below 1%.

Action Potentials↗
PubMedDate not supplied

Real-time control of a video game with a direct brain--computer interface.

Mason and Birch have developed a direct brain-computer interface for intermittent control of devices such as environmental control systems and neuroprotheses. This EEG-based brain switch, named the LF-ASD, has been used in several off-line studies, but little is known about its usability with real-world devices and computer applications. In this study, able-bodied individuals and people with high-level spinal injury used the LF-ASD brain switch to control a video game in real time. Both subject groups demonstrated switch activations varying from 30% to 78% and false-positive rates in the range of 0.5% to 2.2% over three 1-hour test sessions. These levels correspond to switch classification accuracies greater than 94% for all subjects. The results suggest that subjects with spinal cord injuries can operate the brain switch to the same ability as able-bodied subjects in a real-time control environment. These results support the findings of previous studies.

Adult↗