Design, development and evaluation of a cognitive training platform through a brain-computer interface system.
In Spain, the percentage of people over 60 has doubled in less than three decades. According to estimates by the United Nations Department of Economy and Social Affairs (UNDESA, 2015), in 2050 the total number of people over 60 will represent 41.4% of the Spanish population, placing the country in fourth place among the world's oldest countries. This estimate highlights the need to take into account the major social and economic challenges arising from such a marked ageing of the population. One of the greatest problems associated with this situation is the progressive cognitive deterioration of this group (e.g., memory loss, attention, speed of information processing, coordination of movements, etc.). For these reasons, it is urgent to propose new methods and therapies that favour active ageing and make it possible to combat the effects of cognitive deterioration produced as a consequence of age.
Currently, the most used cognitive training techniques are based on the repetition of exercises focused on specific skills involving calculus, memory, mental agility, etcetera. Around all of these Brain Training exercises, numerous commercial applications and products have been developed that increasingly present an attractive aspect. However, despite the remarkable growth of this market, products based on Brain Training exercises are being scientifically questioned. In fact, there is no scientific evidence to support their effectiveness in improving overall cognitive function. In addition, studies conducted by manufacturers ignore relevant aspects, such as the placebo effect; or confuse procedural learning with general and real cognitive improvement.
This scenario offers the opportunity to propose alternative technologies to combat these effects and to demonstrate their effectiveness scientifically. One of them could be Neurofeedback (NF), an experimental technique based on the hypothesis that a voluntary regulation of brain activity produces an improvement in certain cognitive functions. The user's goal during NF therapy is to modify their brain activity through different mental tasks to meet a pre-established goal. For this, the electroencephalogram (EEG) is used, placing a series of electrodes on the user's scalp. In summary, NF training uses the EEG signal to inform the user indirectly, through games or visual tasks, of their brain activity at all times. This feedback allows the user, therefore, to "learn" to modify their brain activity to achieve the goal of the task.
Currently, NF training is based on using rudimentary feedback: LED lights, simple tones, etc.; requiring between 30 and 50 training sessions before being able to detect changes in brain plasticity. Some studies suggest that changes in brain plasticity depend on the training protocol used, as well as the quality of feedback offered. In this sense, we believe that Brain-Computer Interface (BCI) systems can drastically improve the feedback offered to the user, strengthening the weakened cognitive functions in a few training sessions.
BCI systems allow applications or devices to be controlled solely by the user's brain waves, typically derived from the EEG. Because intentions or thoughts are not directly reflected in the EEG signal, BCI systems apply mathematical signal processing algorithms to extract certain characteristics that reflect changes in the signal, related to cognitive tasks. The feedback must therefore be related to the neuropsychological function you wish to work with.
The main objective of this research work is to design, develop and evaluate a BCI NF training platform to effectively combat the deterioration of cognitive functions produced by normal aging. To begin with, the cognitive functions to be improved will be identified. Subsequently, each of the functions will be related to one or more characteristics of the EEG, in order to select the appropriate feedback for the user. Finally, cognitive training tasks will be designed and developed to improve each of the objective cognitive functions. The tasks will be displayed in an Android Tablet, visually feedback to the user showing the increases and decreases of these features in real time. In this way, users will learn to regulate their own activity to achieve the goal of each task. This protocol will be evaluated with at least 20 people over the age of 60. Neuropsychological tests will be performed before and after the protocol on each of them, in order to identify whether training with the platform has produced improvements in objective cognitive functions.
