All neural information systems (NIS) rely on sensing neural activity to supply commands and control signals for computers, machines and a variety of prosthetic devices. Invasive systems achieve a high signal-to-noise ratio (SNR) by eliminating the volume conduction problems caused by tissue and bone. An implantable brain machine interface (BMI) using intracortical electrodes provides excellent detection of a broad range of frequency oscillatory activities through the placement of a sensor in direct contact with cortex. This paper introduces a compact-sized implantable wireless 32-channel bidirectional brain machine interface (BBMI) to be used with freely-moving primates. The system is designed to monitor brain sensorimotor rhythms and present current stimuli with a configurable duration, frequency and amplitude in real time to the brain based on the brain activity report. The battery is charged via a novel ultrasonic wireless power delivery module developed for efficient delivery of power into a deeply-implanted system. The system was successfully tested through bench tests and in vivo tests on a behaving primate to record the local field potential (LFP) oscillation and stimulate the target area at the same time.

In this study, a low-power spinal motion and muscle activity monitor is developed to monitor 3D kinematics and muscle activities. The low power wireless sensor module offers 8 differential EMG channels and a 9-axis motion processor to enable study full body kinematics and muscle activity. The sensor has a 23 mm circular form factor and can be used in healthy subjects such as athletes and military during relevant field exercises to establish baseline bio-metric data without limiting their mobility. The system offers a standard Bluetooth Low Energy (BLE) wireless interface to communicate with gateway devices and desktop systems. Seven sensors can be placed on a subject and paired with a gateway device such as a cellular phone with BLE interface. A component based open architecture software framework is developed to enable flexibility to use different products under a common platform. The system is validated by measuring lumbar spine posture and muscle activity.

Abstract. With the recent advances in the field of electroencephalography (EEG), researchers have been able to detect and predict cognitive states and patterns based on electromagnetic signals emitted by the brain with greater precision than ever before. EEG has become a viable means of implementing brain-computer-interfaces (BCIs), which translate brain signals into machine commands. We have developed a system with reconfigurable hardware and open architecture component based software to enable multi-sensor physiological signal monitoring. The goal of this research is to study the feasibility of using the system that can facilitate monitoring of visual attention EEG brain signal.

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