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A researcher of the UPCT develops a methodology that allows to predict the intention of movement using between 3 and 5% of information (15/04/2019)

Being able to predict human thinking is the dream of many.

The already doctor by the UPCT, Juan Antonio Martínez, has developed in his doctoral thesis an evaluation of the benefits of a system of reading brain data of low cost used to predict the movement imagination, including also a methodology capable of reducing calculations required between 3% and 5% of the originals, from identifying the most relevant captured data.

The work, entitled 'Brain-computer interface systems based on low cost EEG devices and neurodifferent models applied to the imagination of movement' and directed by doctors José Manuel Cano and Julio José Ibarrola, is based on evaluating the device for capturing Emotiv EPOC data to identify the imagination of the movement of the person.

According to Martínez, the imagination of the movement "is the only way of communication that some people have with their environment".

The objective of the work, he explains, is to create a system that "can predict" what the person is thinking and, based on the reduction in the dimensionality of the problem, make the calculations to obtain these predictions "lighter".

The researcher, who has developed the work within the doctoral program Industrial Technologies, explains that at present brain data are usually captured with equipment of several tens of thousands of euros, which restricts them to medical and research environments.

However, devices such as Emotiv EPOC, with a reduced cost, can make access to a larger public possible, which should result in the democratization of the use of this type of interfaces to control other systems, which could allow disabled people, a better relationship with its environment.

During the work he created databases of volunteers and showed that with a cheap data capture device such as Emotiv EPOC the imagination of movement can be detected with sufficient precision.

In addition, the methodology contributed reduced the necessary variables to between 3% and 5% of the initial ones, improving even the initial precision of the complete system for around 70% of the collaborators.

It affirms, therefore, that this methodology could be used in the integration of motion prediction systems in operations with exoskeletons and artificial limbs to rehabilitate the independent movement of people who have lost that capacity.

"That's a big challenge of technology," he adds.

Source: UPCT

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