Biomedical Signal Acquisition Using Sensors under the Paradigm of Parallel Computing

Area of Science:

• Neuroscience
• Biomedical Engineering
• Rehabilitation Therapy

Background:

• Central nervous system pathologies require diverse therapies to mitigate patient deficits.
• Neurorehabilitation therapies aim to improve patient quality of life and societal function.
• Assessing therapy efficacy often involves measuring brain activity via electroencephalograms (EEG), which are data-intensive.

Purpose of the Study:

• To propose an integrated system for acquiring and analyzing multiple biomedical signals (EEG, ECG, bioacoustics, digital images).
• To provide neuroscience experts with tools for estimating the efficiency of various neurorehabilitation therapies.
• To establish a wireless benchmark system for capturing and analyzing brain interactions during therapeutic interventions.

Main Methods:

• Development of a system for parallel/distributed capture, filtering, and adaptation of biomedical signals.
• Implementation of real-time synchronization for sampling epochs.
• Integration of modular sensors adaptable to research needs and environmental conditions.

Main Results:

• The system was validated using Dolphin-Assisted Therapy for patients with Infantile Cerebral Palsy and Obsessive-Compulsive Disorder.
• Event synchronization successfully isolated therapy stimuli for analysis.
• Analysis tools like Power Spectrum and Fractal Geometry were applied to assess therapy impact.

Conclusions:

• The proposed integrated system offers a versatile and robust platform for evaluating neurorehabilitation therapies.
• The system's modularity and adaptability support diverse research applications in neuroscience.
• This wireless benchmark has the potential to advance the field of brain-computer interfaces and therapeutic monitoring.