Ramses Eduardo Alcaide-Aguirre

University of Michigan–Ann Arbor

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Jane E. Huggins

Interface (United States)

Seth Warschausky

University of Michigan–Ann Arbor

Abdulrahman W. Aref

University of Michigan–Ann Arbor

Jay Jantz

Queen's University

Kate Ching‐Ju Lin

National Yang Ming Chiao Tung University

D. Brown

University of Michigan–Ann Arbor

Arnaldo E Pereira

David C. Morgenroth

VA Puget Sound Health Care System

Adel Aref

University of Michigan–Ann Arbor

Dereck Padden

Cooperative Institutions

University of Michigan–Ann Arbor

Michigan Medicine

Interface (United States)

Queen's University

National Yang Ming Chiao Tung University

Taiwan Semiconductor Manufacturing Company (Taiwan)

China Medical University

Wellesley College

Research Center for Information Technology Innovation, Academia Sinica

Academia Sinica

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Preliminary psychometric properties of a standard vocabulary test administered using a non-invasive brain-computer interface

Frontiers in Human Neuroscience (2022)

Seth Warschausky Jane E. Huggins Ramses Eduardo Alcaide-Aguirre Abdulrahman W. Aref

To examine measurement agreement between a vocabulary test that is administered in the standardized manner and a version that is administered with a brain-computer interface (BCI).The sample was comprised of 21 participants, ages 9-27, mean age 16.7 (5.4) years, 61.9% male, including 10 with congenital spastic cerebral palsy (CP), and 11 comparison peers. Participants completed both standard and BCI-facilitated alternate versions of the Peabody Picture Vocabulary Test - 4 (PPVT™-4). The BCI-facilitated PPVT-4 uses items identical to the unmodified PPVT-4, but each quadrant forced-choice item is presented on a computer screen for use with the BCI.Measurement agreement between instruments was excellent, including an intra-class correlation coefficient of 0.98, and Bland-Altman plots and tests indicating adequate limits of agreement and no systematic test version bias. The mean standard score difference between test versions was 2.0 points (SD 6.3).These results demonstrate that BCI-facilitated quadrant forced-choice vocabulary testing has the potential to measure aspects of language without requiring any overt physical or communicative response. Thus, it may be possible to identify the language capabilities and needs of many individuals who have not had access to standardized clinical and research instruments.

Two-alternative forced choice

10.3389/fnhum.2022.930433

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

Journal of Neural Engineering (2017)

Ramses Eduardo Alcaide-Aguirre Seth Warschausky D. Brown Adel Aref Jane E. Huggins

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.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.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.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.

Interface (matter)

10.1088/1741-2552/aa7fc4

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Cross-Subject EEG Event-Related Potential Classification for Brain-Computer Interfaces Using Residual Networks

HAL (Le Centre pour la Communication Scientifique Directe) (2018)

Arnaldo E Pereira Dereck Padden Jay Jantz Kate Ching‐Ju Lin Ramses Eduardo Alcaide-Aguirre

EEG event-related potentials, and the P300 signal in particular, are promising modalities for brain-computer interfaces (BCI). But the nonstationarity of EEG signals and their differences across individuals have made it difficult to implement classifiers that can determine user intent without having to be retrained or calibrated for each new user and sometimes even each session. This is a major impediment to the development of consumer BCI. Recently, the EEG BCI literature has begun to apply convolutional neural networks (CNNs) for classification, but experiments have largely been limited to training and testing on single subjects. In this paper, we report a study in which EEG data were recorded from 66 subjects in a visual oddball task in virtual reality. Using wide residual networks (WideResNets), we obtain state-of-the-art performance on a test set composed of data from all 66 subjects together. Additionally , a minimal preprocessing stream to convert EEG data into square images for CNN input while adding regularization is presented and shown to be viable. This study also provides some guidance on network architecture parameters based on experiments with different models. Our results show that it may possible with enough data to train a classifier for EEG-based BCIs that can generalize across individuals without the need for individual training or calibration.

10.13140/rg.2.2.16257.10086

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Novel hold–release functionality in a P300 brain–computer interface

Journal of Neural Engineering (2014)

Ramses Eduardo Alcaide-Aguirre Jane E. Huggins

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.

Interface (matter)

10.1088/1741-2560/11/6/066010

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Effects of text generation on P300 brain-computer interface performance

Brain-Computer Interfaces (2016)

Jane E. Huggins Ramses Eduardo Alcaide-Aguirre Katya Hill

Brain-computer interfaces (BCIs) are intended to provide independent communication for those with the most severe physical impairments. However, development and testing of BCIs is typically conducted with copy-spelling of provided text, which models only a small portion of a functional communication task. This study was designed to determine how BCI performance is affected by novel text generation. We used a within-subject single-session study design in which subjects used a BCI to perform copy-spelling of provided text and to generate self-composed text to describe a picture. Additional off-line analysis was performed to identify changes in the event-related potentials that the BCI detects and to examine the effects of training the BCI classifier on task-specific data. Accuracy was reduced during the picture description task; (t(8)=2.59 p=0.0321). Creating the classifier using self-generated text data significantly improved accuracy on these data; (t(7)=-2.68, p=0.0317), but did not bring performance up to the level achieved during copy-spelling. Thus, this study shows that the task for which the BCI is used makes a difference in BCI accuracy. Task-specific BCI classifiers are a first step to counteract this effect, but additional study is needed.

Spelling

Interface (matter)

10.1080/2326263x.2016.1203629

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Motor control and learning with lower-limb myoelectric control in amputees

The Journal of Rehabilitation Research and Development (2013)

Ramses Eduardo Alcaide-Aguirre David C. Morgenroth Daniel P. Ferris

Advances in robotic technology have recently enabled the development of powered lower-limb prosthetic limbs. A major hurdle in developing commercially successful powered prostheses is the control interface. Myoelectric signals are one way for prosthetic users to provide feedforward volitional control of prosthesis mechanics. The goal of this study was to assess motor learning in people with lower-limb amputation using proportional myoelectric control from residual-limb muscles. We examined individuals with transtibial amputation and nondisabled controls performing tracking tasks of a virtual object. We assessed how quickly the individuals with amputation improved their performance and whether years since amputation correlated with performance. At the beginning of training, subjects with amputation performed much worse than control subjects. By the end of a short training period, tracking error did not significantly differ between subjects with amputation and nondisabled subjects. Initial but not final performance correlated significantly with time since amputation. This study demonstrates that although subjects with amputation may initially have poor volitional control of their residual lower-limb muscles, training can substantially improve their volitional control. These findings are encouraging for the future use of proportional myoelectric control of powered lower-limb prostheses.

Motor Control

10.1682/jrrd.2012.06.0115

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Brain-computer interface administration of the Peabody Picture Vocabulary Test-IV

Jane E. Huggins Ramses Eduardo Alcaide-Aguirre Abdulrahman W. Aref David J. Brown Seth Warschausky

A non-invasive brain-computer interface (BCI)-adapted version of the Peabody Picture Vocabulary Test - 4 ^th Edition (PPVT-IV) was created with the overall goal of enabling test participation by people unable to make the speech or movement responses required for the unmodified test. The BCI-adapted PPVT-IV used custom algorithms to wait for the participant to decide on the response to each test item and to allow verification that the correct answer had been selected. The BCI-adapted PPVT-IV was evaluated with 13 participants who were also able to take the unmodified PPVT-IV. Standardized PPVT-IV scores for the unmodified and BCI-adapted PPVT-IV test versions had average differences of 2.3 ± 6.6 points. BCI administration of the PPVT-IV test thus shows agreement between test administration versions that is within the expected variation of repeated test administration.

Interface (matter)

10.1109/ner.2015.7146552

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Citations (4)