A cognitive signal generation unit of a cognitive ability detection device includes a brain signal acquisition unit, a database, an MRCP correction data selection unit, and a calculation unit. The brain signal acquisition unit acquires a brain signal including an event-related potential. The database stores motor readiness potential correction data corresponding to an operation of a subject. The MRCP correction data selection unit selects the motor readiness potential correction data, on the basis of prior information including the type of an action, and outputs the motor readiness potential correction data to the calculation unit. The calculation unit generates the cognitive signal by correcting the brain signal with the motor readiness potential correction data.

Disclosed is a method for scoring in real time neural signals of a subject with respect to a reference state characterized by k=1 . . . K reference covariance matrices, the method including the following steps: (i) obtaining neural signals from the subject; (ii) computing a covariance matrix of the neural signals; (iii) computing the Riemannian distances between the covariance matrix and k=1 . . . K reference covariance matrices; (iv) computing a continuous score s in real time based on at least one of the distances computed in step (iii). Also disclosed is a system and method for self-paced modulation or external modulation of neural activity of a subject.

Described is a system for the therapeutic modification of human behavior and, more specifically, a system for the transcranial control of procedural memory reconsolidation for the purposes of enhanced skill acquisition. During operation the system records a practice pattern. The practice pattern is a recording of a sensed brain activity from a sensor array when a subject is performing a skill. The practice pattern is converted to brain activity voxels and stored as both an uncompressed practice pattern and a compressed practice pattern.

This disclosure discloses a method and apparatus for processing a motor MI-EEG signal and a storage medium. The method includes: inputting a source MI-EEG signal belonging to a source domain and a target MI-EEG signal belonging to a target domain to an initial feature extraction model to obtain first source MI features and first target MI features; inputting the first source MI features to an initial classification model to obtain a first classification result outputted by the initial classification model, the first classification result representing an action predicted to be performed in the source MI-EEG signal; and adjusting a model parameter of the initial feature extraction model and/or a model parameter of the initial classification model when a certain condition (set) is met.

This disclosure discloses a method and apparatus for processing a motor MI-EEG signal and a storage medium. The method includes: inputting a source MI-EEG signal belonging to a source domain and a target MI-EEG signal belonging to a target domain to an initial feature extraction model to obtain first source MI features and first target MI features; inputting the first source MI features to an initial classification model to obtain a first classification result outputted by the initial classification model, the first classification result representing an action predicted to be performed in the source MI-EEG signal; and adjusting a model parameter of the initial feature extraction model and/or a model parameter of the initial classification model when a certain condition (set) is met.

Certain examples provide systems and methods to enhance slow wave sleep. An example method includes identifying a sleep stage for slow wave sleep in a subject being monitored. The example method also includes generating, following identification of slow wave sleep and using a processor including a phase locked loop, an output signal based on a phase of a reference input signal, the output signal phase locked according to the reference input signal. The example method includes delivering, during slow wave sleep for the subject, a stimulus to the subject based on the phase locked output signal. The delivering includes providing the stimulus in a series of signal pulses for a first period of time; and providing a refractory period without pulses in a second period of time. The method further includes measuring feedback from the stimulus.

An information processing device includes a first acquisition unit that acquires biological inform information on a user, a first judgment execution unit that executes a first judgment on whether a first discomfort condition is satisfied or not based on first discomfort condition information specifying the first discomfort condition and the biological information, a second acquisition unit that acquires a sound signal, an acoustic feature detection unit that detects an acoustic feature based on the sound signal, a second judgment execution unit that executes a second judgment on whether a second discomfort condition is satisfied or not based on second discomfort condition information specifying the second discomfort condition and the acoustic feature, and an output judgment unit that judges whether first masking sound should be outputted or not based on a result of the first judgment and a result of the second judgment.

The present disclosure provides methods for identifying non-penetrating brain injury in a subject, as well as methods for classifying a subject that received a hit to the body that transmitted an impulsive force to the brain as either having a non-penetrating brain injury or not, by analyzing one or more components of frequency-following response (FFR) following administration of an acoustic stimulus to the subject. In addition, the present disclosure provides methods for assessing a subject's recovery from a non-penetrating brain injury. Also disclosed herein are processes and systems for automatically generating acoustic stimuli and processing brain response data to identify non-penetrating brain injuries in subjects.

An in-ear stimulation system including a first device configured to be worn at least partially in a first ear canal of a subject and a second device configured to be worn at least partially in a second ear canal of the subject. Each of the first device and the second device includes: at least one in-ear active electrode configured to receive a bio-signal and at least one in-ear reference electrode configured to receive a bio-signal; at least one stimulation device configured for emitting at least one electrical or sensory stimulus; and an electronic system configured to detect at least one bio-signal pattern from the bio-signals measured from the electrodes.

A brain training system and method are provided that incorporates a first phase and a second phase of brain training. The first phase is designed to increase attention by requiring the completion of an attention-based activity or task. This may require the identification of match or mismatched images. The second phase is designed to increase working memory by requiring the completion of a working memory-based task that requires the repetition of an illumination sequence. Each phase may be implemented in a self-contained handheld device, similar to the shape of a tablet, or may be implemented in a computer application, such as software. The answers of each phase are recorded in a central database and the system may provide recommendations based on the answers.