Independent home use of a brain-computer interface by people with amyotrophic lateral sclerosis

Wolpaw, Jonathan R.; Bedlack, Richard; Reda, Domenic J.; Ringer, Robert J.; Banks, Patricia G.; Vaughan, Theresa M.; Heckman, Susan M.; McCane, Lynn M.; Carmack, Charles S.; Winden, Stefan; McFarland, Dennis J.; Sellers, Eric W.; Shi, Hang; Paine, Tamara; Higgins, Donald S.; Lo, Albert; Patwa, Huned; Hill, Katherine J.; Huang, Grant D.; Ruff, Robert L.

doi:10.1212/wnl.0000000000005812

Cited by 114 publications

(87 citation statements)

References 41 publications

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“…Commonly used BCIs for restoring communication to people who can't move or speak are either flashing EEG spellers [21][22][23][24][25][26] or 2D point-and-click computer cursor-based BCIs for selecting letters on a virtual keyboard 27,6,7 . EEG spellers that use visually evoked potentials have achieved speeds of characters per minute 24 , but have important usability limitations, as they tie up the eyes, are not typically self-paced, and require panels of flashing lights on a screen that take up space and may be fatiguing.…”

Section: Discussionmentioning

confidence: 99%

High-performance brain-to-text communication via imagined handwriting

Willett

Avansino

Hochberg

et al. 2020

Preprint

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SummaryBrain-computer interfaces (BCIs) can restore communication to people who have lost the ability to move or speak. To date, a major focus of BCI research has been on restoring gross motor skills, such as reaching and grasping1–5 or point-and-click typing with a 2D computer cursor6,7. However, rapid sequences of highly dexterous behaviors, such as handwriting or touch typing, might enable faster communication rates. Here, we demonstrate an intracortical BCI that can decode imagined handwriting movements from neural activity in motor cortex and translate it to text in real-time, using a novel recurrent neural network decoding approach. With this BCI, our study participant (whose hand was paralyzed) achieved typing speeds that exceed those of any other BCI yet reported: 90 characters per minute at >99% accuracy with a general-purpose autocorrect. These speeds are comparable to able-bodied smartphone typing speeds in our participant’s age group (115 characters per minute)8 and significantly close the gap between BCI-enabled typing and able-bodied typing rates. Finally, new theoretical considerations explain why temporally complex movements, such as handwriting, may be fundamentally easier to decode than point-to-point movements. Our results open a new approach for BCIs and demonstrate the feasibility of accurately decoding rapid, dexterous movements years after paralysis.

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Section: Discussionmentioning

confidence: 99%

High-performance brain-to-text communication via imagined handwriting

Willett

Avansino

Hochberg

et al. 2020

Preprint

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“…For example, if an end user spells the first letter of a word, the P300 BCI might present options to complete that word, which could greatly reduce the time needed to convey that word. This approach has been validated with target patients in their homes (Oken et al, 2014; Speier, Chandravadia, Roberts, Pendekanti, & Pouratian, 2017; Wolpaw et al, 2018). This is a promising direction, and additional incorporation of language models could make BCIs much more efficient.…”

Section: Theoretical Implicationsmentioning

confidence: 99%

30+ years of P300 brain–computer interfaces

2020

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Brain–computer interfaces (BCIs) directly measure brain activity with no physical movement and translate the neural signals into messages. BCIs that employ the P300 event‐related brain potential often have used the visual modality. The end user is presented with flashing stimuli that indicate selections for communication, control, or both. Counting each flash that corresponds to a specific target selection while ignoring other flashes will elicit P300s to only the target selection. P300 BCIs also have been implemented using auditory or tactile stimuli. P300 BCIs have been used with a variety of applications for severely disabled end users in their homes without frequent expert support. P300 BCI research and development has made substantial progress, but challenges remain before these tools can become practical devices for impaired patients and perhaps healthy people.

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“…Wolpaw et al described a study involving five Veterans Administration medical centers, each managing several patients with ALS. After a screening of 37 patients to determine usability, P300‐based spellers were placed in the homes of 27 patients for periods of 12 to 18 months.…”

Section: Real‐time Applications Of Bcis With Disabled Usersmentioning

confidence: 99%

Brain‐computer interfaces for amyotrophic lateral sclerosis

McFarland

2020

Muscle and Nerve

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A brain-computer interface (BCI) is a device that detects signals from the brain and transforms them into useful commands. Researchers have developed BCIs that utilize different kinds of brain signals. These different BCI systems have differing characteristics, such as the amount of training required and the degree to which they are or are not invasive. Much of the research on BCIs to date has involved healthy individuals and evaluation of classification algorithms. Some BCIs have been shown to have potential benefit for users with minimal muscular function as a result of amyotrophic lateral sclerosis. However, there are still several challenges that need to be successfully addressed before BCIs can be clinically useful.amyotrophic lateral sclerosis, brain-computer interface, neuroprosthetics

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Independent home use of a brain-computer interface by people with amyotrophic lateral sclerosis

Cited by 114 publications

References 41 publications

High-performance brain-to-text communication via imagined handwriting

High-performance brain-to-text communication via imagined handwriting

30+ years of P300 brain–computer interfaces

Brain‐computer interfaces for amyotrophic lateral sclerosis

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