Independent home use of a brain–computer interface by people with amyotrophic lateral sclerosis. P300-based brain–computer interface (BCI) event-related potentials (ERPs): people with amyotrophic lateral sclerosis (ALS) vs. A novel P300-based brain–computer interface stimulus presentation paradigm: moving beyond rows and columns. A P300-based brain–computer interface for people with amyotrophic lateral sclerosis. Fully implanted brain–computer interface in a locked-in patient with ALS. Estimated prevalence of the target population for brain–computer interface neurotechnology in the Netherlands.
Toward optimal target placement for neural prosthetic devices. A high-performance brain–computer interface. Cognitive control signals for neural prosthetics.
A theory of multineuronal dimensionality, dynamics and measurement. Signal-independent noise in intracortical brain–computer interfaces causes movement time properties inconsistent with Fitts’ law. Intracortical recording stability in human brain–computer interface users. Tracking single units in chronic, large scale, neural recordings for brain machine interface applications. Single-unit stability using chronically implanted multielectrode arrays. High-speed spelling with a noninvasive brain–computer interface. Machine translation of cortical activity to text with an encoder-decoder framework. Speech synthesis from neural decoding of spoken sentences. In 2019 IEEE International Conference on Acoustics, Speech and Signal Processing 6381–6385 (2019).Īnumanchipalli, G. Streaming end-to-end speech recognition for mobile devices. The Microsoft 2017 Conversational Speech Recognition System. In 2013 IEEE International Conference on Acoustics, Speech and Signal Processing 6645–6649 (2013). Speech recognition with deep recurrent neural networks. Deep neural networks for acoustic modeling in speech recognition: the shared views of four research groups. Discovering precise temporal patterns in large-scale neural recordings through robust and interpretable time warping. Hand knob area of premotor cortex represents the whole body in a compositional way. Localization of the motor hand area to a knob on the precentral gyrus. 21st International Conference on Human–Computer Interaction with Mobile Devices and Services 1–12 (Association for Computing Machinery, 2019). How do people type on mobile devices? Observations from a study with 37,000 volunteers.
High performance communication by people with paralysis using an intracortical brain–computer interface. Virtual typing by people with tetraplegia using a self-calibrating intracortical brain–computer interface. Restoration of reaching and grasping movements through brain-controlled muscle stimulation in a person with tetraplegia: a proof-of-concept demonstration.
Restoring cortical control of functional movement in a human with quadriplegia. Decoding motor imagery from the posterior parietal cortex of a tetraplegic human. High-performance neuroprosthetic control by an individual with tetraplegia. Reach and grasp by people with tetraplegia using a neurally controlled robotic arm. Our results open a new approach for BCIs and demonstrate the feasibility of accurately decoding rapid, dexterous movements years after paralysis. Finally, theoretical considerations explain why temporally complex movements, such as handwriting, may be fundamentally easier to decode than point-to-point movements. To our knowledge, these typing speeds exceed those reported for any other BCI, and are comparable to typical smartphone typing speeds of individuals in the age group of our participant (115 characters per minute) 8.
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With this BCI, our study participant, whose hand was paralysed from spinal cord injury, achieved typing speeds of 90 characters per minute with 94.1% raw accuracy online, and greater than 99% accuracy offline with a general-purpose autocorrect. Here we developed an intracortical BCI that decodes attempted handwriting movements from neural activity in the motor cortex and translates it to text in real time, using a recurrent neural network decoding approach. However, rapid sequences of highly dexterous behaviours, such as handwriting or touch typing, might enable faster rates of communication. So far, a major focus of BCI research has been on restoring gross motor skills, such as reaching and grasping 1, 2, 3, 4, 5 or point-and-click typing with a computer cursor 6, 7. Brain–computer interfaces (BCIs) can restore communication to people who have lost the ability to move or speak.