The present disclosure relates to a technical idea for minimizing a signal correction process between users using brain activity-based clustering technology. More specifically, the present disclosure relates to technology for minimizing a signal correction process between users by clustering a brain signal of a measurement subject into a specific clustering model and determining an intention of the measurement subject using an intention determination model learned on the specific clustering model. The brain-computer interface apparatus according to one embodiment of the present disclosure may include a feature extractor for extracting a plurality of clustering features using frequency powers for each band of brain signals measured from a plurality of learning subjects; a clustering model generator for generating a plurality of clustering models based on the extracted clustering features; and a brain wave processor for constructing an intention determination model by performing machine learning of brain signals for each of the generated clustering models, determining a newly measured brain signal of a measurement subject as any one of the clustering models, and determining an intention of the measurement subject using the constructed intention determination model.

The present disclosure relates to a technical idea for minimizing a signal correction process between users using brain activity-based clustering technology. More specifically, the present disclosure relates to technology for minimizing a signal correction process between users by clustering a brain signal of a measurement subject into a specific clustering model and determining an intention of the measurement subject using an intention determination model learned on the specific clustering model. The brain-computer interface apparatus according to one embodiment of the present disclosure may include a feature extractor for extracting a plurality of clustering features using frequency powers for each band of brain signals measured from a plurality of learning subjects; a clustering model generator for generating a plurality of clustering models based on the extracted clustering features; and a brain wave processor for constructing an intention determination model by performing machine learning of brain signals for each of the generated clustering models, determining a newly measured brain signal of a measurement subject as any one of the clustering models, and determining an intention of the measurement subject using the constructed intention determination model.

The invention encompasses systems and methods allowing for minimally invasive insertion and functional optimization of implantable electrode arrays designed for placement within the subgaleal space to record brain electrical activity. The implantable arrays comprise a support structure capable of being implanted in the subgaleal space and comprising at least one reference element; at least one ground element; and one or more recording elements; and wherein said array is capable of detecting and/or transmitting a subgaleal electrical signal.

The invention encompasses systems and methods allowing for minimally invasive insertion and functional optimization of implantable electrode arrays designed for placement within the subgaleal space to record brain electrical activity. The implantable arrays comprise a support structure capable of being implanted in the subgaleal space and comprising at least one reference element; at least one ground element; and one or more recording elements; and wherein said array is capable of detecting and/or transmitting a subgaleal electrical signal.

A physiological measurement system comprises a digital module, which receives an analog bio-signal, a digital unit, which converts the at least one analog bio-signal into a digital signal form, and a first 5-pin-USB-connector, which output the bio-signal in the digital signal form. A bio-signal processing unit, which comprises a counter 5-pin-USB-connector, which is connected with the first 5-pin-USB-connector and receives the bio-signal from the digital module, and alternatively receives analog bio-signal in an analog signal form. The bio-signal processing unit distinguishes between the digital and analog signals and performs: an electric connection of the counter 5-pin-USB-connector with an input of an analog-to-digital converter circuit, an output of which is electrically connected with a digital data processing unit of the bio-signal processing unit, in response to detection of the analog signal received from the counter 5-pin-USB-connector, and an electric connection of the counter 5-pin-USB-connector with the digital data processing unit in response to detection of the digital signal received from the counter 5-pin-USB connector.

An intelligent portable medical instrument has an information processing unit and a data storage unit which are connected to a measurement and human body data collection unit. The measurement and human body data collection unit measures electrical, chemical, and acoustic data and sends the data to the information processing unit. The information processing unit compares the measured human physiological index data with the standard ranges of values and makes a preliminary health diagnosis opinion. The preliminary health diagnosis opinion and the measured data are transmitted to an in vitro unit which preferably uploads the information to a cloud server. The in vivo portion of the intelligent portable medical instrument is provided by a single integrated circuit.

In a system for measuring biopotential signals generated by the physiological activity of a subject, it is necessary to monitor the measured biopotentials against a reliable reference. An apparatus and method of deriving a reference suitable for use in referencing and grounding of the subject are described.

In a system for measuring biopotential signals generated by the physiological activity of a subject, it is necessary to monitor the measured biopotentials against a reliable reference. An apparatus and method of deriving a reference suitable for use in referencing and grounding of the subject are described.

Systems and methods are described herein for estimating and filtering electrophysiological signals. In some examples, a noise filtering system can be employed to receive at least one electrophysiological signal. A signal segment extractor of the system can extract a signal segment of interest from the electrophysiological signal. The system employs a signal segment noise calculator to evaluate the extracted signal segment of interest to estimate a noise in the signal segment of interest. The estimated noise can be provided to a signal segment filter of the system to determine a surrogate noise estimate for at least one remaining signal segment of the electrophysiological signal for noise filtering the at least one remaining signal segment. The signal segment noise calculator can be configured to filter the signal segment of interest based on the estimated noise and the filtered signal segments can be combined to provide a filtered electrophysiological signal.

The present disclosure relates to a high density electrical interconnect apparatus for interfacing with remotely located electrical components. The apparatus may have a housing, a substrate element supported within the housing, and a plurality of independent substrate interface connect subsystems arranged in a planar grid on the substrate element. The apparatus further has a plurality of independent electrical interface connector subassemblies, each configured to be coupled to an associated subplurality of the substrate interface connect subsystems, to form a plurality of electrical communication channels with the remotely located electrical components.