The measurement of bioelectrical signals has long tradition and is extensively used in clinical setting. There are various areas of application, including, among others, intensive-care medicine, typically for continuous monitoring of heart --- electrocardiography (ECG) --- and brain activity --- electroencephalography (EEG) --- providing important information about health condition and physiological processes. Another widespread application of bioelectrical signals is electromyography (EMG), which is used in physical medicine and rehabilitation, especially for diagnostic tests such as nerve conduction studies and movement analysis, or for the control of prosthesis. Due to the vast amount of information carried in bioelectrical signals, they are also widely used in many research areas which require the conduct of various novel tests that might not be feasible with conventional integrated measurement systems. For this reason, there is a continuous need of custom-developed solutions, tailored according to specific requirements. Consequently, tools that facilitate the flexible and efficient development of such measurement applications are highly demanded. The goal of this thesis was the implementation of a driver library for a custom acquisition device, developed at the Medical University of Vienna, suitable to implement measurement applications for various types of bioelectrical signals and sensors, based on the National Instruments LabVIEW platform. Using that driver library, a recording system for EMG signals was developed that allows the simultaneous acquisition of data from two measurement devices with a total of 16 channels. Due to the architecture of the driver library, consisting of a set of highly cohesive modules, a certain degree of scalability is provided which, in theory, allows parallel operation of an arbitrary number of devices, however, that number is restricted by the available resources of the executing computer system. As a proof-of-functionality, the measurement system was used in a small preliminary spinal cord stimulation study with a sample of two healthy participants. The aim was to investigate the reciprocal interaction of monosynaptic and polysynaptic activity underlying posterior root-muscle reflexes. For this purpose, single pulses of transcutaneous electrical spinal cord stimulation were applied at T12-L3 vertebral level, while monitoring the EMG activity from quadriceps, hamstrings, tibialis anterior and triceps surae in both legs. To examine the interaction between monoand polysynaptic responses, a second pulse was introduced to elicit a conditioning monosynaptic response, which was shifted from 10 to 300 ms. It was found that moving the conditioning monosynaptic response towards the polysynaptic activity, effectively reduces its RMS voltage. On the other hand, the PTP voltage of the conditioning monosynaptic response was significantly increased when entering the time window of polysynaptic activity. The developed driver brings a new level of flexibility, allowing other researchers to rapidly develop new applications not only for monitoring but also for active control of actuators like arm prosthesis or functional electrical stimulation.
The measurement of bioelectrical signals has long tradition and is extensively used in clinical setting. There are various areas of application, including, among others, intensive-care medicine, typically for continuous monitoring of heart --- electrocardiography (ECG) --- and brain activity --- electroencephalography (EEG) --- providing important information about health condition and physiological processes. Another widespread application of bioelectrical signals is electromyography (EMG), which is used in physical medicine and rehabilitation, especially for diagnostic tests such as nerve conduction studies and movement analysis, or for the control of prosthesis. Due to the vast amount of information carried in bioelectrical signals, they are also widely used in many research areas which require the conduct of various novel tests that might not be feasible with conventional integrated measurement systems. For this reason, there is a continuous need of custom-developed solutions, tailored according to specific requirements. Consequently, tools that facilitate the flexible and efficient development of such measurement applications are highly demanded. The goal of this thesis was the implementation of a driver library for a custom acquisition device, developed at the Medical University of Vienna, suitable to implement measurement applications for various types of bioelectrical signals and sensors, based on the National Instruments LabVIEW platform. Using that driver library, a recording system for EMG signals was developed that allows the simultaneous acquisition of data from two measurement devices with a total of 16 channels. Due to the architecture of the driver library, consisting of a set of highly cohesive modules, a certain degree of scalability is provided which, in theory, allows parallel operation of an arbitrary number of devices, however, that number is restricted by the available resources of the executing computer system. As a proof-of-functionality, the measurement system was used in a small preliminary spinal cord stimulation study with a sample of two healthy participants. The aim was to investigate the reciprocal interaction of monosynaptic and polysynaptic activity underlying posterior root-muscle reflexes. For this purpose, single pulses of transcutaneous electrical spinal cord stimulation were applied at T12-L3 vertebral level, while monitoring the EMG activity from quadriceps, hamstrings, tibialis anterior and triceps surae in both legs. To examine the interaction between monoand polysynaptic responses, a second pulse was introduced to elicit a conditioning monosynaptic response, which was shifted from 10 to 300 ms. It was found that moving the conditioning monosynaptic response towards the polysynaptic activity, effectively reduces its RMS voltage. On the other hand, the PTP voltage of the conditioning monosynaptic response was significantly increased when entering the time window of polysynaptic activity. The developed driver brings a new level of flexibility, allowing other researchers to rapidly develop new applications not only for monitoring but also for active control of actuators like arm prosthesis or functional electrical stimulation.
