Design of a Neurofeedback Training System for Meditation Based on EEG Technology

Meditation is a form of mental training that has therapeutic potential and cognitive benefits that may enhance attention, mental well-being, and neuroplasticity. However, the learning process is not easy because meditators do not receive immediate feedback that lets them know if they are correctly doing the activity. EEG Neurofeedback training is one of the techniques to train brain self-regulation and it has the potential to increase the effectiveness of meditation. However, the benefits greatly differ between subjects with a high percentage of inefficacy. In this work, an EEG Neurofeedback Training System is proposed based on user-centered design methodology to provide real-time performance feedback to meditators to increase levels of attention and relaxation through a visual, sound and smell stimuli interface. Levels of attention and relaxation of nine participants were measured with a mobile Neurosky EEG headset biosensor during meditation practice to analyze the incidence of each type of stimuli during activity. Visual stimuli feedback was able to increase attention levels of 78% of the participants by 11.8% compared to a 1 M. Sc. Pontificia Universidad Javeriana (Bogotá-Distrito Capital, Colombia). nieto-andres@javeriana.edu.co. ORCID: 0000-0003-1934-8552 2 M. Sc. Pontificia Universidad Javeriana (Bogotá-Distrito Capital, Colombia). omar.ramirez@javeriana.edu.co. ORCID: 0000-0003-1492-7010 3 Pontificia Universidad Javeriana (Bogotá-Distrito Capital, Colombia). luis.ballesteros@javeriana.edu.co. ORCID: 0000-0003-4660-2739 4 Pontificia Universidad Javeriana (Bogotá-Distrito Capital, Colombia). angela.aragon@javeriana.edu.co. ORCID: 0000-0003-2968-244X Design of a Neurofeedback Training System for Meditation Based on EEG Technology Revista Facultad de Ingeniería (Rev. Fac. Ing.) Vol. 30 (55), e12489. Enero-Marzo 2021. Tunja-Boyacá, Colombia. L-ISSN: 0121-1129, e-ISSN: 2357-5328. DOI: https://doi.org/10.19053/01211129.v30.n55.2021.12489 meditation session without any stimuli. The sound stimuli feedback was able to increase the relaxation levels of 44.4% of the participants by 16% compared to a session without any stimuli. These results might bring new insights for the design of a neurofeedback system interface for meditation. Further research on neurofeedback training interfaces for meditators is suggested to validate these results with more participants.

meditation session without any stimuli. The sound stimuli feedback was able to increase the relaxation levels of 44.4% of the participants by 16% compared to a session without any stimuli. These results might bring new insights for the design of a neurofeedback system interface for meditation. Further research on neurofeedback training interfaces for meditators is suggested to validate these results with more participants.
Diseño de un sistema de retroalimentación neuronal para el entrenamiento de la meditación basado en electroencefalograma

I. INTRODUCTION
Over the last thirty years, science has been proving numerous physiological and psychological benefits of the meditation practice [1][2][3]. One of the most extensive and detailed research on this topic has been carried out by Richard Davidson at Madison University in Wisconsin EU and the Center for Healthy Minds. He and his colleagues have been studying for decades the effects of meditation across a vast number and variety of practitioners. Some research has shown the relation between mental disposition and pain experience [4], the effects of anxiety on the immune system [5], the reduction of cardiovascular risk and healthy aging [6] and how meditation can induce plastic changes in the brain, creating new circuits and new neurons [7]. To develop these studies, researchers have been using neuroimaging technologies such as functional magnetic resonance imaging (fMRI) and electroencephalography (EEG).
German neurologist Hans Berger is considered the father of EEG technology. He created the first device able to record brain wave electrical activity in 1924. Since then, EEG technology has been getting cheaper and smaller. Today it is possible to find a variety of mobile EEG headsets such as the one used in this research. EEG biosensors record the electrical brain activity, specifically the post-synaptic potential, created by some neurons. Groups of more than one thousand neurons can get synchronized and generate different rhythms or frequencies, beta (>13 Hz), alpha (8)(9)(10)(11)(12)(13), theta (4-8 Hz), delta (0.5-4 Hz) which are linked to distinct cognitive function [8]. Laboratories and companies have been studying the correlations between brain activity patterns and different cognitive functions like attention or relaxation and processes as learning and meditation. Meditation is described as a self-regulatory technique in which the meditator is focused on maintaining attention and reaching a restful state. However, the learning process is not easy for meditators and particularly for beginners because there is no immediate feedback, so they usually do not know if they are doing the activity correctly [9].
Neurofeedback is a non-invasive technique in which the brain itself is responsible of self-regulating the electrical activity that is reflected in the brain waves, based on the feedback stimulus that receives from its own functioning. The feedback stimulus can be visual, auditory, olfactory or tactile, to let the users know their brain activity and become aware of their reactions. Therefore, it generates an increase in the levels of self-control and self-awareness. Neurofeedback training can be done by measuring the electrical activity of the brain with EEG technology and giving some kind of feedback stimulus to the brain.
In this work, an EEG Neurofeedback Training System (EEG-NFTS) is proposed, based on user-centered design methodology to provide real-time performance feedback to meditators to increase attention and relaxation levels through a visual, sound and smell stimuli interface. The system was evaluated by measuring the effects during meditation sessions.
This article is divided as follows: Section 2 presents a description of the methodology, Section 3 presents the design of the EEG Neurofeedback Training System, Section 4 shows the experimental data results and analysis after testing the EEG Neurofeedback Training System, and Section 5 presents the conclusions of the work.

II. METHODOLOGY
The experiment was done in a controlled environment with nine healthy participants ranging from 20 to 35 years old. Five participants were experienced meditators and four participants were non-meditators. The experiment has three stages and participants were evaluated individually. In the first stage, participants were asked to fill in a form with name, age, gender, meditation experience and their current mood Colombia.

A. System Design
An EEG Neurofeedback Training System (EEG-NFTS) was designed and implemented to measure the brain activity of the participants using EEG Headset Mind Wave Mobile 2 from Neurosky to obtain attention and relaxation levels during meditation sessions. Levels of attention and relaxation were sent to an App to register the information and to evaluate the incidence of each stimulus in the performance of the participants during a meditation session. Figure 1 shows a block diagram of the EEG-NFTS. The system contains a microcontroller ATmega2560 that was used to receive and process the brain activity data from the EEG headset to control the actuators and     Figure 3 shows the EEG-NFTS interface which is composed of an encoder, a visual light ring indicator, a water flow output and a smell output. The encoder works as a button to turn on/off the system and a knob to set up the time duration of the meditation session (during the test participants were not allowed to set the time duration of the meditation session neither choose which stimuli to activate). The cylinder contains the electronic main circuit board, a water tank and a water pump. To do the test, the system was configured so that only one type of stimuli was enabled per meditation session to evaluate the incidence of each type of stimuli in the performance of the user during the development of the activity. The time duration of each meditation session was set to 10 minutes.      Table 2 and Table 3 shows the average levels of attention and relaxation respectively of each participant during the meditation session. Table 4 shows the data collected from each participant at the beginning and at the end of each meditation session. practitioners are more sensitive to variations of their attention levels during a meditation session while receiving different stimuli feedback. Standard deviation levels for relaxation were similar for both practitioners and non-practitioners. This means that all the participants are more sensitive to variations of their relaxation levels during a meditation session while receiving different stimuli feedback.
Following the self-reported mood, one can deduce that at the beginning of the test most participants felt excited-lively. At the end of the test, most participants felt cheerful-happy and relaxed-carefree. This resonates with the biometric measurements taken at the beginning and at the end of the test in which most participants show a decrease in both, pulse variability and arterial pressure, this behavior is directly related to low anxiety and high wellbeing. These results show that different stimuli feedback can improve the meditation performance of the practitioners by increasing attention and relaxation levels leading to a desired mindful state.

IV. CONCLUSIONS
An EEG Neurofeedback training system (EEG-NFTS) was proposed to increase attention and relaxation levels during a meditation practice by giving real-time feedback to the user through visual, sound and smell stimuli. The system was validated with nine participants by using a Neurosky EEG headset to measure attention and relaxation levels during meditation practice to evaluate the incidence of each stimulus in the performance of the activity. The visual stimuli feedback was able to increase the average attention levels of 78% of the participants by 11.8% compared to a session without any feedback. The sound stimuli feedback was able to increase the average relaxation levels for 44.4% of the participants by 16% compared to a session without any feedback. The visual stimuli feedback has a positive impact in attention levels but reduces the relaxation levels of the 88.9% of the users by 19.9%.
The system has a greater impact on the average attention levels of the inexperienced meditators during a meditation session, so it can be useful during the meditation learning process.