Resumen

MORENO ESCOBAR, Jesús Jaime et al. Computational System for Determining the Efficiency of Dolphin-Assisted Therapy. Comp. y Sist. [online]. 2020, vol.24, n.4, pp.1471-1482.  Epub 11-Jun-2021. ISSN 2007-9737.  https://doi.org/10.13053/cys-24-4-3318.

The recent proliferation of sensors technology applications in therapies to children disabilities to promote positive behavior among such children has produced optimistic results in developing a variety of skills and abilities in them. Dolphin-Assisted Therapy (DAT) has also become a topic of public and research interest for these disorders intervention and treatment. This work exposes the development of a computational system that controls brain-computer interaction when a patient with different abilities takes a DAT. The study was carried out at Definiti Ixtapa, Guerrero facilities and shows that brain activity increases by 376 % during a DAT. A TGAM1 sensor was used to develop the system, which is connected to the Bluetooth 4.0 communication protocol, which is isolated from environmental conditions, which is brackish and humid. In this way, we explore the behavior of Obsessive Compulsive Disorder and neurotypic children using Fast Fourier Transform (FFT) from Electroencephalogram (EEG). The EEG RAW data are time series that showed the cerebral brain activity, voltage versus time, at rest and during a DAT for both children, as recorded by the first frontopolar electrode (FP1) by means of an EEG biosensor TGAM1 Module. Our findings indicate that this computational system measures the RAW EEG fluctuations during DAT display a collective behavior with positive increments of neuronal activity that could be followed by much more neural activity. Thus, the brain could react to DAT gradually over a period of time. In addition, the patient with OCD gets an apparent relaxation since its Power Spectrum Density (PSD) decreases 20 % regarding its initial rest state meanwhile the control patient increases its PSD 53 % regarding its initial rest state.

Palabras llave : Brain-computer interface; artificial intelligence; dolphin-assisted therapy; TGAM1 sensor; EEG; FFT; power spectrum density.

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