Keywords

1 Introduction

1.1 ICT and Learning

The advent of ICT is a key factor that allows significant improvements in the area of research and education. Simulations, virtual environments, serious games and other innovative technological means can facilitate and enhance people’s skills in everyday activities. The ICT-supported learning process mainly involves visual perception, decision-making, rational thinking and executive discrimination tasks that require skills which are not only exclusively connected to formal education, but also concern everyday life skills.

In many countries, an effort is being made to provide ICT courses for older people. As Dickinson & Gregorargue [1], just providing ICT skills does not automatically increase the quality of life and skills of the elderly. Learning plays a key role in ageing societies as it can help older people to address many challenges such as re-skilling and up-skilling in the knowledge-based society. As the majority of ICT tools are not user-friendly for older people, it is difficult for them to use this technology either for learning purposes, or as a part of everyday activities. The basic problem is often the user interface, which is rarely designed for older people. A tool that makes the user feel frustrated cannot be a motivating environment for learning. Considering the needs of older learners and the cognitive abilities that are connected with skills, such as working memory, reasoning, and speed of information processing is essential in developing ICT-supported learning for the elderly.

In recent years, many digital tools have been developed in order to educate about disaster prevention and protection [2]. As it is commonly accepted, education, targeting disaster preparedness, should be extended throughout the society in order to foster a more resilient population. Shaw & Kobayashi present a serious game that provides an engaging virtual environment for training on disaster communication and decision-making processes [3]. Based on the concept of problem-based learning, players develop communication and group decision-making skills. Also, Mitsuharaet al. propose a web-based system for designing game-based evacuation [4] The players can learn about disaster prevention measures by viewing the materials and real-world scenery and making appropriate decisions during a virtual evacuation. In [5] authors compared students’ views on three different web-based learning tools, an educational game, a dynamic simulation and a digital concept map. These three learning tools were used for educational purposes aiming at natural disaster readiness. The results showed that students remained highly-engaged while using all three learning objects. These examples suggest that educational material that is embedded in digital environments for disaster risk reduction should be designed and implemented in order to capitalize on the interactivity and the high sense of presence to promote the development of skills, such as critical thinking and problem-solving that users would carry in a real world situation. However, the basic principle for implementing an effective digital learning environment is to design sensory representations to be compatible with visual, acoustic and tactile perception of the user [6].

1.2 Educational Neuroscience

Neuroscience aims in the interpretation of human behavior through the study of the brain functions and more specifically, in the understanding of how neurons collaborate in order to produce a given behavior and how the environment affects them [7]. In this field, learning is defined as a process of creating neural connections in response to external stimuli and education as the process of creating and controlling these stimuli [8, 9]. Educational neuroscience is based on the general idea that anything that influences learning has its foundation in the human brain, therefore, the understanding of the human brain functioning could affect educational practices, create improved teaching methods and provide a conceptual framework for an in-depth understanding of how the human brain creates cognitive schemata based on the input of sensory stimuli.

Electroencephalography (EEG) is a popular method in recording the electrical brain activity, as it is non-invasive and provides an excellent temporal resolution [10, 11]. Event-Related Potentials (ERP) are the electrical responses of the cortex to a sensory, cognitive or emotional event . ERP components, as parts of the EEG signal, enable the recording and analysis of neural responses with high temporal resolution to specific visual events, which appear in a digital environment. The P300 and N200 signals are the two most prominent ERP components for decision-making tasks, as the one under study. The P300 wave is an event-related potential component with a parieto-central scalp distribution elicited in the processes of stimulus evaluation and decision-making [12]. Authors in [13] argue that the P300 latency is an indicator of the duration of the stimulus categorization. The N200 wave is considered to reflect processes involved mainly in the detection of novelty or mismatch [14].

The research objective of the present study was to compare two EEG systems, namely the G.tec EEG system with passive electrodes and the EMOTIV EPOCmobile EEG system. A visual decision-making task in constituting an earthquake survival kit from images of useful items and non-useful items was used for the experimental procedure. For the comparison of the two EEG systems, we used an online questionnaire and the measurements of the EEG systems. As the timeline of constituent processing steps in visual decision-making is below the earliest response time for a motor response (around 700 ms), the study hinges on measuring the electrical brain activity related to visual processing in terms of visual awareness and semantic recognition during the identification of ten different images depicting useful items (UI) and non-useful items (NUI) that they have to bring with them in their earthquake survival kit, in case of an earthquake.

2 Method

2.1 Research Questions

The research goal of this study was to record the electrical brain activity of male and female volunteers in order to compare the neurophysiological measurements received by using Emotiv’s and Gtec’s EEG systems. To reach this goal, the following research questions were set separately for both the EEG measurements:

  • Which brain regions are associated with semantic recognition of the visual stimuli under study?

  • How much time is needed for the semantic recognition of the visual stimuli under study?

2.2 Participants

To compare the two EEG systems, we recorded the brain activity of seven male (28 to 31 years old) and twenty female (20 to 21 years old) volunteers. All participants had normal vision, were right–handed native Greek speakers, without certain diagnosed learning difficulties or mental disease. None of the participants received any medication or substances that affected the operation of the nervous system and they had not consumed quantities of caffeine or alcohol in the last 24 h before the experiment. The alpha rhythm of all the participants was checked and found to be normal (8–12 Hz, 10 Hz peak).

2.3 Stimuli

Ten different images depicting useful items (UI) and non-useful items (NUI) in case of an earthquake, were presented to each subject (Fig. 1). The five useful items were a cereal bar, a flashlight, a pocketknife, a bottle of water and a whistle, whereas the non-useful items were an ice cream, a hamburger, a laptop, a bottle of milk and a tool kit. Each participant was comfortably seated at eye level and 100 cm away from α 17” TFT monitor, passively observing the displayed images. In the beginning of the experiment, each participant had a few minutes to adapt to the specific conditions, to relax and reduce the movements of their eyes. Before the EEG recording the researchers gave a briefing to each participant about earthquakes and the relevant precaution measures. The participant was familiarized with the useful and non-useful objects.

Fig. 1.
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Useful items (up) and non-useful items (bottom)

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2.4 Procedure

The EEG data were recorded during the observation of images, incorporated in an educational digital environment and depicting useful and non-useful objects with which they should provide a survival bag in the event of an earthquake. The experimental procedure completed in one session that included the presentation of 300 images of which 15% depicted a useful item and 85% a non-useful object (oddball experimental paradigm).

The visual stimuli, five different images of each of the two categories, appeared randomly at the center of the screen for 2000 ms. Between the sequential appearance of two stimuli of any category, a blue cross with a display duration of 1000 ms appeared at the center of the screen (Fig. 2). The participants were instructed to observe the images displayed on the screen and respond mentally only to the stimuli that depicted a useful object. For each participant the experimental procedure lasted 30–40 min.

Fig. 2.
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Schematic view of the experimental procedure

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The experimental procedure was performed twice for each participant, one for each EEG system. In order not to affect the results from the participants’ previous experience, the participants were divided into two groups where one was initially recorded with g.tec and then with Emotiv and vice versa. For every participant, there was at least a period of two weeks between the two recordings.

2.5 EEG Systems

G.tec.

EEG was recorded by using a g.tec 36-channel amplifier with 256 Hz sampling rate. The digital EEG data acquisition system had a 1–100 Hz band pass filter. EEG activity was monitored from 19 Ag/AgCl electrodes using an electrode cap with a standard 10–20 International Electrode Placement System layout. Raw EEG data was recorded from Fp1, Fp2, F7, F3, Fz, F4, F8, T3, C3, Cz, C4, T4, T5, P3, Pz, P4, T6, O1 and O2 electrode positions. All leads were referenced to linked ear lobe and a ground electrode was applied to the forehead. Horizontal and vertical eye movements were recorded simultaneously using four electrodes round the eyes. The electrodes impedance was kept below 5 KΩ.

Emotiv.

EEG was recorded using the 16 electrodes of the Emotiv system, according to the standard 10–20 International Electrode Placement System layout: AF3, F7, F3, FC5, T7, P7, O1, O2, P8, T8, FC6, F4, F8, FC4, M1and M2, with the maximum sampling rate of 128 Hz. Μ1 sensor was used as ground and M2 contributed to lowering the external electrical interference. The electrodes impedance was kept below 5 KΩ.

2.6 Offline Analysis

For the process of the offline analysis of the signal, after removing eye movement and other artefacts by inspection, individual subject EEG data were filtered with a 30 Hz low-pass digital filter and divided into epochs. Each epoch began 100 ms prior to stimulus onset and continued for 600 ms thereafter. Single trials were then averaged per UI and NUI and subject using a 100 ms pre-stimulus baseline. Finally, the grand mean for each group of objects across all subjects was calculated.

Concerning the Gtec data, event-related potentials were studied within frontal (F7, F3, Fz, F4, F8), fronto-parietal (Fp1, Fp2), central (C3, Cz, C4), parietal (P3, Pz, P4), temporal (T3, T4, T5, T6) and occipital (O1, O2) areas. The processing and analysis of the signals were performed by using the gBSanalyze and Matlab software packages.

Concerning the Emotiv data, event-related potentials were studied within frontal (AF3, F7, F3, FC5, FC6, F4, F8, FC4), temporal (T7, T8), parietal (P7, P8) and occipital (O1, O2) locations. The processing and analysis of the signals were performed by using the EEGLab Matlab toolbox.

The analysis of EEG data primarily focused on the examination of P300 and N200 ERP components. The analysis of the signals was conducted separately for the male and the female participants to examine possible gender differences in brain activity. After the exclusion of the artifacts that were included in the data due to eye movements or other factors, the raw EEG data were filtered applying a 30 Hz low pass filter and divided into epochs. Each epoch started 100 ms before the stimulus onset and extended for 600 ms after the stimulus onset. The single average and the grand average from the individual ERP waveforms for the two stimuli categories were calculated for all participants, using a 100 ms baseline before the stimulus appearance. The grand average for each item category was calculated taking into account the signals from all participants, but separately for men and women. Based on data collected from the visual inspection of ERP waveforms of grand averages and previous studies [13, 15, 16], we defined the P300 as the greatest positive point of the ERP waveform of total averages in the interval between 250 ms–600 ms (Fig. 3).

Fig. 3.
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Layout of electrode array, based on gtec’s measurements from male participants, utilized for grand-average ERPs inspection for each stimulus category (green signals: UI (target), red signals: NUI (non-target)) (Color figure online)

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2.7 Questionnaire

After the recordings, the participants were asked to fill in a questionnaire regarding the evaluation of both systems in terms of usability, comfort and speed of placement. Apart from some personal data i.e. gender, age etc. the participants had to answer the following questions in a five-level scale:

  1. 1.

    How do you evaluate the installation speed of the g.tec? (1 = Very fast/Fast/Moderate/Slow/Very slow)

  2. 2.

    How do you evaluate the installation speed of the emotiv? (Very fast/Fast/Moderate/Slow/Very slow)

  3. 3.

    How do you evaluate the comfort of the g.tec installation? (Very comfortable/Comfortable/Moderate/Inconvenient/Annoying)

  4. 4.

    How do you evaluate the comfort of the emotiv installation? (Very comfortable/Comfortable/Moderate/Inconvenient/Annoying)

  5. 5.

    How do you evaluate the difficulty of installing the g.tec system to the head? (Very Easy/Easy/Moderate/Difficult/Very Difficult)

  6. 6.

    How do you evaluate the difficulty of installing the emotiv system to the head? (Very Easy/Easy/Moderate/Difficult/Very Difficult).

3 Results

3.1 G.tec Data

EEG data from male and female participants gave almost the same results for P300 and N200 ERP components.

The stimuli depicting useful items (UI) appear to elicit a negative N200 component between 200 ms and 250 ms after the stimulus, and a positive P300 component between 500 ms and 600 ms. The stimuli depicting non-useful items (NUI) did not elicited any of the above-mentioned components. The P300 component appeared to have a greater amplitude in the parietal-central region of the skull and in particular at the Cz and Pz electrode locations. The latency of P300 component for both Cz and Pz appeared prolonged with the average latency for useful objects being approximately 550 ms (Fig. 4). The amplitude of the P300 component in the Cz and Pz locations was approximately 40 μV and 30 μV respectively.

Fig. 4.
figure 4

Grand-average ERP waveforms at electrode locations Pz and Cz for each stimulus category. Time 0 corresponds to the presentation of the stimulus (green signals: UI (useful items), red signals: NUI (non-useful items) (Color figure online)

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Figure 5 shows that the N200 component had a posterior distribution, showing greater amplitude in the parietal, temporal and occipital regions of the skull (i.e., in Pz, P3, P4, T5, T6, O1, O2 and Cz).

Fig. 5.
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The N200 component appears to have a posterior distribution

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3.2 EMOTIV Data

Based on the literature, the P300 component is mainly located at Cz and Pz electrode locations. However, according to [18] it also can be found in other places, i.e. P7 and O1. In our results, the P300 component was not detected in any electrode location, neither for UI nor for NUI signals. Moreover, we did not manage to have a good signal quality in most of the locations for almost all participants, especially for electrode locations F4 and P7, which were excluded.

3.3 Questionnaire Data

The absolute frequencies of the answers were calculated for each question separately. Regarding the evaluation of the installation speed of the two EEG systems, the participants’ answers are presented in Fig. 6.

Fig. 6.
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Participants’ answers regarding the installation speed of g.tec and emotiv system

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Regarding the installation comfort of the two EEG systems, the participants’ answers are presented in Fig. 7.

Fig. 7.
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Participants’ answers regarding the comfort of the g.tec and emotiv installation

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Regarding the difficulty of adjustment of the g.tec and EMOTIV system to the participants’ head, the participants’ answers are presented in Fig. 8.

Fig. 8.
figure 8

Participants’ answers regarding difficulty of installing the g.tec system to the head

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4 Discussion

Learning is changing, along with the availability of a multitude of ICT applications and research is needed to determine how learning can best be supported in an ageing society. Educational Technology is a promising tool that has the power to increase people’s skills through highly interactive and interesting digital environments. For the effective implementation of a learning environment, all sensory representations must be designed to be compatible with the visual, acoustic and tactile perception of the user. Brain functioning and the use of neuroimaging techniques such as EEG therefore seems to give tools beyond those of social sciences research methodology.

The present study examined two EEG systems in terms of signal quality and usability. Based on the EEG data, we argue that the G.tec system can be used to study cognitive processes in educational environments either in a continuous EEG recording or in ERP experimental set up. For EMOTIV EPOC system we argue that the recorded signals lag behind in quality. Moreover, EMOTIV EPOC does not support the recording in electrode locations that are essential for examining ERP components related to decision-making tasks. The EMOTIV EPOC system can be used in continuous EEG recordings in game environments and educational content.

Based on the questionnaire, we argue that participants did not find a big difference between the two systems regarding the installation speed. Although, gtec cap is harder to install, it is much easier to have good signal after the completion of the installation. On the contrary, the EMOTIV EPOC is more easier to install but it takes more time to get good signals. As far as installation comfort is concerned, we argue that the EMOTIV EPOC is much more comfortable as it allows participants to move partially. Due to the fact that EMOTIV EPOC does not provide caps for different head sizes, makes it sometimes hard to adjust all semi-rigid “fingers” to touch the skull. Table 1 presents comparative data for the two EEG systems.

Table 1. Comparison of Gtec and Emotiv EEG systems
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