Asynchronous brain-computer interface for cognitive assessment in people with cerebral palsy.

OBJECTIVE: Typically, clinical measures of cognition require motor or speech responses. Thus, a significant percentage of people with disabilities are not able to complete standardized assessments. This situation could be resolved by employing a more accessible test administration method, such as a brain-computer interface (BCI). A BCI can circumvent motor and speech requirements by translating brain activity to identify a subject's response. By eliminating the need for motor or speech input, one could use a BCI to assess an individual who previously did not have access to clinical tests. APPROACH: We developed an asynchronous, event-related potential BCI-facilitated administration procedure for the peabody picture vocabulary test (PPVT-IV). We then tested our system in typically developing individuals (N = 11), as well as people with cerebral palsy (N = 19) to compare results to the standardized PPVT-IV format and administration. MAIN RESULTS: Standard scores on the BCI-facilitated PPVT-IV, and the standard PPVT-IV were highly correlated (r = 0.95, p < 0.001), with a mean difference of 2.0 ± 6.4 points, which is within the standard error of the PPVT-IV. SIGNIFICANCE: Thus, our BCI-facilitated PPVT-IV provided comparable results to the standard PPVT-IV, suggesting that populations for whom standardized cognitive tests are not accessible could benefit from our BCI-facilitated approach.

Novel hold-release functionality in a P300 brain-computer interface.

Assistive technology control interface theory describes interface activation and interface deactivation as distinct properties of any control interface. Separating control of activation and deactivation allows precise timing of the duration of the activation. Objective. We propose a novel P300 brain-computer interface (BCI) functionality with separate control of the initial activation and the deactivation (hold-release) of a selection. Approach. Using two different layouts and off-line analysis, we tested the accuracy with which subjects could (1) hold their selection and (2) quickly change between selections. Main results. Mean accuracy across all subjects for the hold-release algorithm was 85% with one hold-release classification and 100% with two hold-release classifications. Using a layout designed to lower perceptual errors, accuracy increased to a mean of 90% and the time subjects could hold a selection was 40% longer than with the standard layout. Hold-release functionality provides improved response time (6-16 times faster) over the initial P300 BCI selection by allowing the BCI to make hold-release decisions from very few flashes instead of after multiple sequences of flashes. Significance. For the BCI user, hold-release functionality allows for faster, more continuous control with a P300 BCI, creating new options for BCI applications.

Brain-computer communication based on the dynamics of brain oscillations.

This chapter presents a review of brain-computer communication based on motor imagery and the dynamics of brain oscillations. The concept of motor imagery as experimental strategy and the two different modes of operation a brain-computer interface can have are explained. An EEG based brain switch that can control a FES-induced hand grasp of a tetraplegic and an approach towards an ECoG based brain switch are presented.

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.

Frequency component selection for an ECoG-based brain-computer interface.

The aim of the present study was to investigate the most significant frequency components in electrocorticogram (ECoG) recordings in order to operate a brain computer interface (BCI). For this purpose the time-frequency ERD/ERS map and the distinction sensitive learning vector quantization (DSLVQ) are applied to ECoG from three subjects, recorded during a self-paced finger movement. The results show that the ERD/ERS pattern found in ECoG generally matches the ERD/ERS pattern found in EEG recordings, but has an increased prevalence of frequency components in the beta range.

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.

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 ERP's. 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.

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.

Detection of event-related potentials for development of a direct brain interface.

The study presented here is part of an ongoing effort to develop a direct brain interface based on detection of event-related potentials (ERPs). In a study presented in a companion article, averaged ERP templates were identified from electrocorticograms recorded during repetition of voluntary motor actions. Here the authors report on the detection of individual motor ERPs within the electrocorticogram using cross-correlation. An averaged ERP template was created from the first half of each electrocorticogram and then cross-correlated with the continuous electrocorticogram from the second half. Points where the cross-correlation value exceeded an experimentally determined detection threshold were considered to be detection points. A detection point was considered to be a valid "hit" if it occurred between 1 second before and 0.25 second after the recorded time of a voluntary action. The difference between the hit and false-positive percentages (HF-difference) was used as a metric of detection accuracy. HF-differences greater than 90 were found for 5 of 15 subjects, HF-differences greater than 75 were found for 8 of 15 subjects, and HF-differences greater than 50 were found for 12 of 15 subjects. The three other subjects with HF-differences less than 50 had electrode locations not well suited for recording movement-related ERPs. Recordings from sensorimotor and supplementary motor areas produced the highest yield of channels with HF-difference greater than 50; however, a number of channels with good performance were found in other areas as well. The results demonstrate the likely prospect of using ERP detection as the basis of a single-switch direct brain interface and that furthermore, there is a good possibility of obtaining multiple control channels using this approach.