Meta Platforms, Inc.

Facebook, Inc.

Patrick Mineault

Mark Allan Chevillet

Emily Mittag Mugler

Soo Yeon Kim Jennings

Michael Andrew Choma

Tobias Gerard Tiecke

Henry B Wettersten

Regina Elvira Dugan

A brain computer interface (BCI) system predicts text based on input and output signals obtained in relation to an individual that are informative for determining an individual's neurobiological activity. The BCI system applies a first predictive model to the input signal and a second predictive model to the output signal. The first predictive model predicts the forward propagation of the input signal through the individual's head whereas the second predictive model predicts the backward propagation of the output signal through the individual's head. Each of the first predictive model and second predictive model predicts characteristics of their respective signal at a common plane such as the cortical surface of the individual's brain. The BCI system predicts text by applying a third predictive model to the predicted signal characteristics at the common plane outputted by the first predictive model and the second predictive model.

Predicting text by reconstructing neuroimaging signals

Embodiments relate to decoding architecture that rapidly decodes light-derived signals to extract predicted user actions or intents (e.g., commands) in relation to interactions with objects (e.g., virtual objects, physical objects), such that the user can manipulate the objects or otherwise receive assistance without manually interacting with an input device (e.g., touch input device, audio input device, etc.). The decoding architecture thus enables a neural decoding process with a neural signal stream as an input, and provides feedback to the user, where the feedback is used to train the neural decoding algorithm and user behavior. The neural signals can be blood oxygenation level dependent (BOLD) signals associated with activation of different articulators of the motor cortex, and signals can characterize both actual and imagined motor cortex-related behaviors. With training of the decoding algorithm, rapid calibration of the BCI for new users can be achieved.

Neural decoding with co-learning for brain computer interfaces

Embodiments relate to a brain computer interface (BCI) including a light source subsystem, an interface transmitting light from the light source subsystem to a body region of a user, and a detector subsystem coupled to the interface and configured to receive light signals from the body region of the user. The light source subsystem, the interface, and the detector subsystem are coupled to other electronics providing power, computing functionality, and/or other functionality. The BCI is designed to be worn at a head region of a user and to generate optical signals that can be used to characterize brain activity of the user, where decoded brain activity can be used as inputs to control other systems and/or electronic content provided to the user.

Wearable brain computer interface

A brain computer interface system interprets an individual's neural signals to predict specific phonemes, words, or sentences, thereby enabling the individual to communicate with others through unspoken methods. Specifically, a brain computer interface system captures neural signals from an individual at mesoscopic resolutions using optical neuroimaging techniques. The system applies the captured neural signals to multiple predictive models that have been trained on neural signals captured from previous individuals. The predictive models output predictions as to a phoneme or word that corresponds to the captured neural signals. Therefore, the individual can communicate through the brain computer interface system by only providing neural signals without using verbal, expressive, or physical means.

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San Francisco, CA, United States of America

Patrick Mineault