Jane E. Huggins

Spatiotemporal patterns of beta desynchronization and gamma synchronization in corticographic data during self-paced movement

OBJECTIVE To study the spatiotemporal pattern of event-related desynchronization (ERD) and event-related synchronization (ERS) in electrocorticographic (ECoG) data with closely spaced electrodes. METHODS Four patients with epilepsy performed self-paced hand movements. The ERD/ERS was quantified and displayed in the form of time-frequency maps. RESULTS In all subjects, a significant beta ERD with embedded gamma ERS was found. CONCLUSIONS Self-paced movement is accompanied not only by a relatively widespread mu and beta ERD, but also by a more focused gamma ERS in the 60-90 Hz frequency band.

A direct brain interface based on event-related potentials

Cross-correlation between a trigger-averaged event-related potential (ERP) template and continuous electrocorticogram was used to detect movement-related ERPs. The accuracy of ERP detection for the five best subjects (of 17 studied), had hit percentages >90% and false positive percentages <10%. These cases were considered appropriate for operation of a direct brain interface.

Visualization of significant ERD/ERS patterns in multichannel EEG and ECoG data

OBJECTIVES Analysis of event-related desynchronization (ERD) and event-related synchronization (ERS) often requires the investigation of diverse frequency bands. Such analysis can be difficult, especially when using multichannel data. Therefore, an effective method for the visualization of event-related changes in oscillatory brain activity is required. METHODS A bootstrap-based method is presented which gives time-frequency maps showing only significant changes of ERD or ERS in predetermined frequency bands. RESULTS Examples from an electroencephalographic study and an electrocorticographic study are shown. The results demonstrate how easily reactive channels and their spatio-temporal and frequency-specific characteristics can be identified by means of this method. CONCLUSIONS The proposed method is a simple but effective way to visualize significant ERD/ERS patterns.

Toward a direct brain interface based on human subdural recordings and wavelet-packet analysis

Highly accurate asynchronous detection of movement related patterns in individual electrocorticogram channels has been shown using detection based on either event-related potentials (ERPs) or event-related desynchronization and synchronization (ERD/ERS). A method using wavelet-packet features selected with a genetic algorithm was proposed to simultaneously detect ERP and ERD/ERS and was tested on data from seven subjects and four motor tasks. The proposed wavelet method performed better than previous methods with perfect detection for four subject/task combinations and hit percentages greater than 90% with false positive percentages less than 15% for at least one task for all seven subjects.

What would brain-computer interface users want? Opinions and priorities of potential users with amyotrophic lateral sclerosis

Abstract Universal design principles advocate inclusion of end users in every design stage, including research and development. Brain-computer interfaces (BCIs) have long been described as potential tools to enable people with amyotrophic lateral sclerosis (ALS) to operate technology without moving. Therefore the objective of the current study is to determine the opinions and priorities of people with ALS regarding BCI design. This information will guide BCIs in development to meet end-user needs. A telephone survey was undertaken of 61 people with ALS from the University of Michigans Motor Neuron Disease Clinic. With regard to BCI design, participants prioritized accuracy of command identification of at least 90% (satisfying 84% of respondents), speed of operation comparable to at least 15–19 letters per minute (satisfying 72%), and accidental exits from a standby mode not more than once every 2–4 h (satisfying 84%). While 84% of respondents would accept using an electrode cap, 72% were willing to undergo outpatient surgery and 41% to undergo surgery with a short hospital stay in order to obtain a BCI. In conclusion, people with ALS expressed a strong interest in obtaining BCIs, but current BCIs do not yet provide desired BCI performance.

Identification of electrocorticogram patterns as the basis for a direct brain interface.

This study reports on the first step in the development of a direct brain interface based on the identification of event-related potentials (ERPs) from an electrocorticogram obtained from the surface of the cortex. Ten epilepsy surgery patients, undergoing monitoring with subdural electrode strips and grid arrays, participated in this study. Electrocorticograms were continuously recorded while subjects performed multiple repetitions for each of several motor actions. ERP templates were identified from action-triggered electrocorticogram averages using an amplitude criterion. At least one ERP template was identified for all 10 subjects and in 56% of all electrode-recording sets resulting from a subject performing an action. These results were obtained with electrodes placed solely for clinical purposes and not for research needs. Eighty-two percent of the identified ERPs began before the trigger, indicating the presence of premovement ERP components. The regions yielding the highest probability of valid ERP identification were the sensorimotor cortex (precentral and postcentral gyri) and anterior frontal lobe, although a number were recorded from other areas as well. The recording locations for multiple ERPs arising from the performance of a specific action were usually found on close-by electrodes. ERPs associated with different actions were occasionally identified from the same recording site but often had noticeably different characteristics. The results of this study support the use of ERPs recorded from the cortical surface as a basis for a direct brain interface.

Barriers to and mediators of brain–computer interface user acceptance: focus group findings

Brain–computer interfaces (BCI) are designed to enable individuals with severe motor impairments such as amyotrophic lateral sclerosis (ALS) to communicate and control their environment. A focus group was conducted with individuals with ALS (n=8) and their caregivers (n=9) to determine the barriers to and mediators of BCI acceptance in this population. Two key categories emerged: personal factors and relational factors. Personal factors, which included physical, physiological and psychological concerns, were less important to participants than relational factors, which included corporeal, technological and social relations with the BCI. The importance of these relational factors was analysed with respect to published literature on actor-network theory (ANT) and disability, and concepts of voicelessness and personhood. Future directions for BCI research are recommended based on the emergent focus group themes. Practitioner Summary: This manuscript explores human factor issues involved in designing and evaluating brain–computer interface (BCI) systems for users with severe motor disabilities. Using participatory research paradigms and qualitative methods, this work draws attention to personal and relational factors that act as barriers to, or mediators of, user acceptance of this technology.

Detection of movement-related patterns in ongoing single-channel electrocorticogram

Adaptive autoregressive parameters and a linear classifier were used to detect movement related desynchronization and synchronization patterns in single-channel electrocorticogram (ECoG) obtained from implanted electrode grids. The best classification accuracies found had more than 90% hits and less than 10% false positives. The findings show that the detection of event-related desynchronization and synchronization in ECoG data can be used to reliably provide switch control directly by the brain and is therefore very suitable as the basis of a direct brain interface.

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.

Comparison of Evaluation Metrics in Classification Applications with Imbalanced Datasets

A new framework is proposed for comparing evaluation metrics in classification applications with imbalanced datasets (i.e., the probability of one class vastly exceeds others). For model selection as well as testing the performance of a classifier, this framework finds the most suitable evaluation metric amongst a number of metrics. We apply this framework to compare two metrics: overall accuracy and Kappa coefficient. Simulation results demonstrate that Kappa coefficient is more suitable.