Abstract: A method for analyzing electroencephalogram (EEG) signals is disclosed. EEG signals are received from a sensor coupled to a user. Contextual information from one or both of the user and the user's environment is also received. The EEG signals are processed in real time using a machine learning model to predict an action of the user, which is associated with the contextual information. Output associated with the predicted action is then generated.

Abstract: A sensor device includes a sensor housing defining a channel extending along a channel axis through the housing from a first side of the sensor housing to a second side of the sensor housing opposite the first side, at least one contact electrode extending from the first side of the housing, an electrically-conducting lead attached to the housing in electrical communication with the at least one contact electrode, and a locking mechanism located in the channel permitting one-way axial motion of a thread threaded through the channel from the first side to the second side.

Abstract: A method for obtaining an electroencephalogram (EEG) of a user is disclosed. A reference sensor is attached to the user by connecting a first component of the reference sensor to a second component of the reference sensor, at least a portion of the first component being sub-dermally implanted on or adjacent to a mastoid process of the user. At least one active sensor is attached to the user. A first signal is detected from the reference sensor simultaneously as a second signal is detected from the at least one active sensor. The EEG is obtained based on the first signal and the second signal.

Abstract: A method for analyzing electroencephalogram (EEG) signals is disclosed. Information associated with two or more options is presented to a user. EEG signals from a sensor coupled to the user are received contemporaneously to the user receiving information associated with the two or more options. The EEG signals are processed in real time to determine which one of the options was selected by the user. In response to determining which one of the options was selected by the user, an action from one or more possible actions associated with the information presented to the user is selected. An output associated with the selected action is then generated.

Abstract: A method for generating an EEG signal is disclosed. Multiple signals from multiple electrodes applied to a user's scalp are measured. The signals correspond to electrical activity generated by the user's brain. Each of the signals is evaluated using a machine learning algorithm in real-time to determine a quality of each of the signals. One or more of the signals is selected based on quality in real-time to provide one or more selected signals. An EEG signal is outputted corresponding to the electrical activity based on the selected signals.

Abstract: A bioamplifier for analyzing electroencephalogram (EEG) signals is disclosed. The bioamplifier includes an input terminal for receiving an EEG signal from a plurality of sensors coupled to a user. The bioamplifier also includes an analogue-to-digital converter arranged to receive the EEG signal from the input terminal and convert the EEG signal to a digital EEG signal. A data processing apparatus within the bioamplifier is arranged to receive the digital EEG signal from the analogue-to-digital converter and programmed to process, in real time the digital EEG signal using a first machine learning model to generate a cleaned EEG signal having a higher signal-to-noise ratio than the digital EEG signal. The bioamplifier further includes a power source to provide electrical power to the analogue-to-digital converter and the data processing apparatus. The bioamplifier includes a housing that contains the analogue-to-digital converter, the data processing apparatus, the power source, and the sensor input.

Abstract: A method for generating an EEG signal is disclosed. Multiple signals from multiple electrodes applied to a user's scalp are measured. The signals correspond to electrical activity generated by the user's brain. Each of the signals is evaluated using a machine learning algorithm in real-time to determine a quality of each of the signals. One or more switches is activated between the electrodes to electrically-connect a plurality of the electrodes. An EEG signal is measured using the parallel-connected electrodes.

Abstract: Methods, systems, and apparatus, including computer programs encoded on a computer storage medium, for receiving brain activity data of a user from a brain wave sensor. Identifying Alpha wave activity from the brain activity data. Determining a synchronization timing for presenting content to the user such that the content appears on a display device during a predetermined phase of the Alpha wave activity based on the Alpha wave activity. Causing the content to be displayed on the display device according to the synchronization timing, where the content includes a first content item and a second content item that is associated with the first content item.