SSVEP BCI and Eye Tracking Use by Individuals With Late-Stage ALS and Visual Impairments

Peters, Betts; Bedrick, Steven; Dudy, Shiran; Eddy, Brandon; Higger, Matt; Kinsella, Michelle; McLaughlin, Deirdre; Memmott, Tab; Oken, Barry; Quivira, Fernando; Spaulding, Scott; Erdoğmuş, Deni̇z; Fried-Oken, Melanie

doi:10.3389/fnhum.2020.595890

Cited by 24 publications

(13 citation statements)

References 44 publications

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“…Home-based, longitudinal studies such as those conducted by the Wadsworth Center, with internet-based technical support and data collection procedures, may serve as a model for conducting AAC-BCI research with larger sample sizes (Wolpaw et al, 2018). Single-case experimental research designs in which participants serve as their own controls, like the alternating-treatments design used by Peters et al (2020), are a good option for future AAC-BCI studies. Such designs are common in the behavioral sciences, including communication sciences, and allow researchers to draw scientifically valid inferences about the effects of an intervention from the performance of a small number of participants (Horner et al, 2005;Krasny-Pacini and Evans, 2018;Kazdin, 2019).…”

Section: Study Characteristicsmentioning

confidence: 99%

“…Because AAC-BCI systems can be operated without reliable volitional movement, they offer a potential means of communication access for people with locked-in syndrome (LIS) or other forms of severe speech and physical impairment (SSPI) who may have difficulty using other AAC access methods (e.g., eye gaze, switch access, touch access) (Akcakaya et al, 2013;Rezeika et al, 2018;Pitt et al, 2019). Researchers have explored the use of AAC-BCI systems by individuals with a variety of medical conditions that may cause SSPI, including amyotrophic lateral sclerosis (ALS) (Kübler et al, 1999;Wolpaw et al, 2018;Medina-Juliá et al, 2020;Miao et al, 2020;Peters et al, 2020), muscular dystrophy (Halder et al, 2016a), stroke (Kaufmann et al, 2013a;Kleih et al, 2016), traumatic brain injury (Combaz et al, 2013;Lesenfants et al, 2014), cerebral palsy (Käthner et al, 2017), and spinal muscular atrophy (Zickler et al, 2011). Many participants in these studies presented with classic LIS (in which only eye movement and blinking are preserved) or incomplete LIS (in which small movements of other body parts are possible) (Bauer et al, 1979;Smith and Delargy, 2005).…”

Section: Introductionmentioning

confidence: 99%

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A systematic review of research on augmentative and alternative communication brain-computer interface systems for individuals with disabilities

Peters

Eddy²,

Galvin-McLaughlin³

et al. 2022

Front. Hum. Neurosci.

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Augmentative and alternative communication brain-computer interface (AAC-BCI) systems are intended to offer communication access to people with severe speech and physical impairment (SSPI) without requiring volitional movement. As the field moves toward clinical implementation of AAC-BCI systems, research involving participants with SSPI is essential. Research has demonstrated variability in AAC-BCI system performance across users, and mixed results for comparisons of performance for users with and without disabilities. The aims of this systematic review were to (1) describe study, system, and participant characteristics reported in BCI research, (2) summarize the communication task performance of participants with disabilities using AAC-BCI systems, and (3) explore any differences in performance for participants with and without disabilities. Electronic databases were searched in May, 2018, and March, 2021, identifying 6065 records, of which 73 met inclusion criteria. Non-experimental study designs were common and sample sizes were typically small, with approximately half of studies involving five or fewer participants with disabilities. There was considerable variability in participant characteristics, and in how those characteristics were reported. Over 60% of studies reported an average selection accuracy ≤70% for participants with disabilities in at least one tested condition. However, some studies excluded participants who did not reach a specific system performance criterion, and others did not state whether any participants were excluded based on performance. Twenty-nine studies included participants both with and without disabilities, but few reported statistical analyses comparing performance between the two groups. Results suggest that AAC-BCI systems show promise for supporting communication for people with SSPI, but they remain ineffective for some individuals. The lack of standards in reporting outcome measures makes it difficult to synthesize data across studies. Further research is needed to demonstrate efficacy of AAC-BCI systems for people who experience SSPI of varying etiologies and severity levels, and these individuals should be included in system design and testing. Consensus in terminology and consistent participant, protocol, and performance description will facilitate the exploration of user and system characteristics that positively or negatively affect AAC-BCI use, and support innovations that will make this technology more useful to a broader group of people.Clinical trial registrationhttps://www.crd.york.ac.uk/prospero/display_record.php?ID=CRD42018095345, PROSPERO: CRD42018095345.

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Section: Study Characteristicsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

A systematic review of research on augmentative and alternative communication brain-computer interface systems for individuals with disabilities

Peters

Eddy²,

Galvin-McLaughlin³

et al. 2022

Front. Hum. Neurosci.

Self Cite

View full text Add to dashboard Cite

show abstract

“…The proposed system supports patients with remaining abilities such as emitting vocal commands or sounds and controlling the head or eye movements. Nonetheless, if there are still remaining abilities in the patient for consciously executing basic tasks, there is an opportunity to adapt a sensing input for satisfying basic needs, like drinking water (Hochberg et al, 2012) or typing messages (Peters et al, 2020). As there are progressive diseases, it is a challenge to adapt systems to the patient's evolving needs.…”

Section: Further Opportunitiesmentioning

confidence: 99%

Development of a Sensing Platform Based on Hands-Free Interfaces for Controlling Electronic Devices

Rojas

Ponce

Molina

2022

Front. Hum. Neurosci.

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Hands-free interfaces are essential to people with limited mobility for interacting with biomedical or electronic devices. However, there are not enough sensing platforms that quickly tailor the interface to these users with disabilities. Thus, this article proposes to create a sensing platform that could be used by patients with mobility impairments to manipulate electronic devices, thereby their independence will be increased. Hence, a new sensing scheme is developed by using three hands-free signals as inputs: voice commands, head movements, and eye gestures. These signals are obtained by using non-invasive sensors: a microphone for the speech commands, an accelerometer to detect inertial head movements, and an infrared oculography to register eye gestures. These signals are processed and received as the user's commands by an output unit, which provides several communication ports for sending control signals to other devices. The interaction methods are intuitive and could extend boundaries for people with disabilities to manipulate local or remote digital systems. As a study case, two volunteers with severe disabilities used the sensing platform to steer a power wheelchair. Participants performed 15 common skills for wheelchair users and their capacities were evaluated according to a standard test. By using the head control they obtained 93.3 and 86.6%, respectively for volunteers A and B; meanwhile, by using the voice control they obtained 63.3 and 66.6%, respectively. These results show that the end-users achieved high performance by developing most of the skills by using the head movements interface. On the contrary, the users were not able to develop most of the skills by using voice control. These results showed valuable information for tailoring the sensing platform according to the end-user needs.

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“…Then, a user can communicate with others or devices without using muscles. Therefore, SSVEP-based BCI is one of the most successful assistive communication interfaces for subjects with ALS [ 14 , 15 , 16 , 17 ]. However, the quality of the SSVEP signal is degraded since the SSVEP signals always mix with normal brain activity and background noise.…”

Section: Introductionmentioning

confidence: 99%

Multi-Task Learning-Based Deep Neural Network for Steady-State Visual Evoked Potential-Based Brain–Computer Interfaces

Chuang

Lee

et al. 2022

Sensors

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Amyotrophic lateral sclerosis (ALS) causes people to have difficulty communicating with others or devices. In this paper, multi-task learning with denoising and classification tasks is used to develop a robust steady-state visual evoked potential-based brain–computer interface (SSVEP-based BCI), which can help people communicate with others. To ease the operation of the input interface, a single channel-based SSVEP-based BCI is selected. To increase the practicality of SSVEP-based BCI, multi-task learning is adopted to develop the neural network-based intelligent system, which can suppress the noise components and obtain a high level of accuracy of classification. Thus, denoising and classification tasks are selected in multi-task learning. The experimental results show that the proposed multi-task learning can effectively integrate the advantages of denoising and discriminative characteristics and outperform other approaches. Therefore, multi-task learning with denoising and classification tasks is very suitable for developing an SSVEP-based BCI for practical applications. In the future, an augmentative and alternative communication interface can be implemented and examined for helping people with ALS communicate with others in their daily lives.

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SSVEP BCI and Eye Tracking Use by Individuals With Late-Stage ALS and Visual Impairments

Cited by 24 publications

References 44 publications

A systematic review of research on augmentative and alternative communication brain-computer interface systems for individuals with disabilities

A systematic review of research on augmentative and alternative communication brain-computer interface systems for individuals with disabilities

Development of a Sensing Platform Based on Hands-Free Interfaces for Controlling Electronic Devices

Multi-Task Learning-Based Deep Neural Network for Steady-State Visual Evoked Potential-Based Brain–Computer Interfaces

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