Abstract: A method and system for neuro-feedback training are disclosed. According to certain embodiments, the method may include receiving, by a processor via a communication network, a brainwave signal measured by at least one sensor attached to a user. The method may also include determining, by the processor, a frequency distribution of the brainwave signal. The method may also include determining, by the processor, a reward in a video game when at least one first value indicative of an amount of the brainwave signal within a first frequency band meets a first criterion. The method may further include providing, to the user, a first feedback signal indicative of the reward.

Abstract: The present disclosure provides a system and a method for manipulating a robotic device. The system includes a human interface device for obtaining information associated with a position and/or movement of a user's finger using the human interface device; and a robotic device for simulating the position and/or movement of the user's finger in real time. The robotic device is in communication with the human interface device. The operating system of the robotic device of the present disclosure can accurately simulate the functions of human hand.

Abstract: Disclosed is a method for attention training, which includes: acquiring a first electroencephalogram (EEG) data that is currently detected through an EEG headband worn by a user; determining an attention level of the user, based on the first EEG data; and in response that the attention level is lower than a preset attention level, controlling to execute a first program based on the attention level, wherein the first program corresponds to a training plan. A device for attention training and a computer readable storage medium are further disclosed.

Abstract: The present disclosure provides a system and a method for manipulating a robotic device. The system includes a human interface device for obtaining information associated with a position and/or movement of a user's finger using the human interface device; and a robotic device for simulating the position and/or movement of the user's finger in real time. The robotic device is in communication with the human interface device. The operating system of the robotic device of the present disclosure can accurately simulate the functions of human hand.

Abstract: A method and system for neuro-feedback training are disclosed. According to certain embodiments, the method may include receiving, by a processor via a communication network, a brainwave signal measured by at least one sensor attached to a user. The method may also include determining, by the processor, a frequency distribution of the brainwave signal. The method may also include determining, by the processor, a first value indicative of an amount of the brainwave signal within a first frequency band. The method may also include generating, by the processor based on the first value, a first control signal to actuate a target device wirelessly connected to the processor. The method may further include transmitting, by the processor via the communication network, the first control signal to the target device.

Abstract: A method and system for neuro-feedback training are disclosed. According to certain embodiments, the method may include receiving, by a processor via a communication network, a brainwave signal measured by at least one sensor attached to a user. The method may also include determining, by the processor, a frequency distribution of the brainwave signal. The method may also include determining, by the processor, a reward in a video game when at least one first value indicative of an amount of the brainwave signal within a first frequency band meets a first criterion. The method may further include providing, to the user, a first feedback signal indicative of the reward.