Guidelines for Feature Matching Assessment of Brain–Computer Interfaces for Augmentative and Alternative Communication

Pitt, Kevin M.; Brumberg, Jonathan S.

doi:10.1044/2018_ajslp-17-0135

Cited by 25 publications

(11 citation statements)

References 86 publications

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“…Furthermore, positioning factors, such as pressure from a wheelchair headrest, and uncontrolled neck and muscle movements may impede posterior electrode recordings, which decreases SSVEP BCI performance below the aforementioned levels (Daly et al, 2013). Finally, in addition to cognitive factors such as attention, due to the rapidly flickering stimuli, an individual's history of seizures is an important consideration when considering SSVEP-based BCI use (e.g., Pitt & Brumberg, 2018b). In comparison to SSVEP, ASSR devices are an emerging BCI technique.…”

Section: "Why" Are Visual and Auditory Steady-state Evoked Potentialsmentioning

confidence: 99%

Behind the Scenes of Noninvasive Brain–Computer Interfaces: A Review of Electroencephalography Signals, How They Are Recorded, and Why They Matter

Pitt

Brumberg

Burnison³

et al. 2019

Perspect ASHA SIGs

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Purpose Brain–computer interface (BCI) techniques may provide computer access for individuals with severe physical impairments. However, the relatively hidden nature of BCI control obscures how BCI systems work behind the scenes, making it difficult to understand “how” electroencephalography (EEG) records the BCI-related brain signals, “what” brain signals are recorded by EEG, and “why” these signals are targeted for BCI control. Furthermore, in the field of speech-language-hearing, signals targeted for BCI application have been of primary interest to clinicians and researchers in the area of augmentative and alternative communication (AAC). However, signals utilized for BCI control reflect sensory, cognitive, and motor processes, which are of interest to a range of related disciplines, including speech science. Method This tutorial was developed by a multidisciplinary team emphasizing primary and secondary BCI-AAC–related signals of interest to speech-language-hearing. Results An overview of BCI-AAC–related signals are provided discussing (a) “how” BCI signals are recorded via EEG; (b) “what” signals are targeted for noninvasive BCI control, including the P300, sensorimotor rhythms, steady-state evoked potentials, contingent negative variation, and the N400; and (c) “why” these signals are targeted. During tutorial creation, attention was given to help support EEG and BCI understanding for those without an engineering background. Conclusion Tutorials highlighting how BCI-AAC signals are elicited and recorded can help increase interest and familiarity with EEG and BCI techniques and provide a framework for understanding key principles behind BCI-AAC design and implementation.

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Section: "Why" Are Visual and Auditory Steady-state Evoked Potentialsmentioning

confidence: 99%

Behind the Scenes of Noninvasive Brain–Computer Interfaces: A Review of Electroencephalography Signals, How They Are Recorded, and Why They Matter

Pitt

Brumberg

Burnison³

et al. 2019

Perspect ASHA SIGs

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“…Multiple factors affect the prognosis of UL functional recovery. First, there is a large variation in user performance between healthy individuals, and the possible factors include cognitive function, sensory, motor imagery ability (16), psychological factors (17), age (18), gender (19), dominant hand (20), sensorimotor rhythm bias (21), cortical gray matter volume, and medical treatment. Second, studies on the prognosis prediction of UL motor recovery in patients with stroke have incorporated age, gender, dominant hand, lesion of hemisphere and location, initial UL function, and presence of comorbidities into models that can predict UL recovery type up to 6 months after onset (22).…”

Section: Introductionmentioning

confidence: 99%

Analysis of Prognostic Risk Factors Determining Poor Functional Recovery After Comprehensive Rehabilitation Including Motor-Imagery Brain-Computer Interface Training in Stroke Patients: A Prospective Study

et al. 2021

Front. Neurol.

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Objective: Upper limb (UL) motor function recovery, especially distal function, is one of the main goals of stroke rehabilitation as this function is important to perform activities of daily living (ADL). The efficacy of the motor-imagery brain-computer interface (MI-BCI) has been demonstrated in patients with stroke. Most patients with stroke receive comprehensive rehabilitation, including MI-BCI and routine training. However, most aspects of MI-BCI training for patients with subacute stroke are based on routine training. Risk factors for inadequate distal UL functional recovery in these patients remain unclear; therefore, it is more realistic to explore the prognostic factors of this comprehensive treatment based on clinical practice. The present study aims to investigate the independent risk factors that might lead to inadequate distal UL functional recovery in patients with stroke after comprehensive rehabilitation including MI-BCI (CRIMI-BCI).Methods: This prospective study recruited 82 patients with stroke who underwent CRIMI-BCI. Motor-imagery brain-computer interface training was performed for 60 min per day, 5 days per week for 4 weeks. The primary outcome was improvement of the wrist and hand dimensionality of Fugl-Meyer Assessment (δFMA-WH). According to the improvement score, the patients were classified into the efficient group (EG, δFMA-WH > 2) and the inefficient group (IG, δFMA-WH ≤ 2). Binary logistic regression was used to analyze clinical and demographic data, including aphasia, spasticity of the affected hand [assessed by Modified Ashworth Scale (MAS-H)], initial UL function, age, gender, time since stroke (TSS), lesion hemisphere, and lesion location.Results: Seventy-three patients completed the study. After training, all patients showed significant improvement in FMA-UL (Z = 7.381, p = 0.000**), FMA-SE (Z = 7.336, p = 0.000**), and FMA-WH (Z = 6.568, p = 0.000**). There were 35 patients (47.9%) in the IG group and 38 patients (52.1%) in the EG group. Multivariate analysis revealed that presence of aphasia [odds ratio (OR) 4.617, 95% confidence interval (CI) 1.435–14.860; p < 0.05], initial FMA-UL score ≤ 30 (OR 5.158, 95% CI 1.150–23.132; p < 0.05), and MAS-H ≥ level I+ (OR 3.810, 95% CI 1.231–11.790; p < 0.05) were the risk factors for inadequate distal UL functional recovery in patients with stroke after CRIMI-BCI.Conclusion: We concluded that CRIMI-BCI improved UL function in stroke patients with varying effectiveness. Inferior initial UL function, significant hand spasticity, and presence of aphasia were identified as independent risk factors for inadequate distal UL functional recovery in stroke patients after CRIMI-BCI.

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“…BCIs circumvent the demands of voluntary motor control, by enabling control of computers using brain activity alone [41]. A majority of BCI interfaces (indirect BCIs) involve noninvasive recordings of brain activity using electroencephalography.…”

Section: Innovations In Input/access Methodsmentioning

confidence: 99%

Innovations for Supporting Communication: Opportunities and Challenges for People with Complex Communication Needs

Smith¹

2019

Folia Phoniatr Logop

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Individuals with complex communication needs have benefited greatly from technological innovations over the past two decades, as well as from social movements that have shifted focus from disability to functioning and participation in society. Three strands of technological innovation are reviewed in this paper: (1) innovations in the tools that have become available, specifically tablet technologies; (2) innovations in access methods (eye gaze technologies and brain-computer interfaces); and (3) innovations in output, specifically speech technologies. The opportunities these innovations offer are explored, as are some of the challenges that they imply, not only for individuals with complex communication needs, but also for families, professionals, and researchers.

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Guidelines for Feature Matching Assessment of Brain–Computer Interfaces for Augmentative and Alternative Communication

Abstract: This procedure is an initial step toward consideration of feature matching assessment for the full range of BCI devices. Future investigations are needed to fully examine how person-centered factors influence BCI performance across devices.

Cited by 25 publications

References 86 publications

Behind the Scenes of Noninvasive Brain–Computer Interfaces: A Review of Electroencephalography Signals, How They Are Recorded, and Why They Matter

Behind the Scenes of Noninvasive Brain–Computer Interfaces: A Review of Electroencephalography Signals, How They Are Recorded, and Why They Matter

Analysis of Prognostic Risk Factors Determining Poor Functional Recovery After Comprehensive Rehabilitation Including Motor-Imagery Brain-Computer Interface Training in Stroke Patients: A Prospective Study

Innovations for Supporting Communication: Opportunities and Challenges for People with Complex Communication Needs

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