2.1 Design
The flow chart (Fig. 1) showed the experiment design detailedly. For each subject of the NFT group, the experiment consisted of twenty-four training sessions within two months. Participants were supposed to have three sessions (every session lasted for an hour) a week. In each session, subjects were asked to play an EEG feedback game. They were free to select three days to receive training in a week, with at least one day between two sessions. In order to ensure the interests of the subjects, the feedback interface was presented to the subjects in the gamification approach with a controllable process. The SSVEP typing test was arranged to compare performance about ten days before and after the two months of training.
2.2 Participants
A total of forty-seven subjects (they were randomly assigned to two groups), twenty-five in the NFT group and twenty-two in the control group, took part in the experiment. Subjects with normal vision or corrected vision and no epilepsy or other diseases were selected. The written consent form was received from all of the participants’ guardians. Moreover, the project is also reviewed by the Ethics Committee of the Tsinghua University School of Medicine. The final sample consisted of 24 subjects in the NFT group(six females and eighteen males, 9.42 ± 2.58 years) and 22 subjects in the control group(eight females and fourteen males, 9.86 ± 3.14 years). As van Praag's experiment suggested, aging causes changes in the hippocampus that may lead to cognition and neural plasticity decline in older adults (van Praag, 2005). Therefore, this research chose children to participate in neurofeedback training out of consideration for the young brains are more neuroplastic.
2.3 EEG Recordings
EEG data were recorded from 64 electrodes positioned according to the international 10–20 system (AF3/4, AF7/8, Fp1/2, FPz, Fz, F1/2, F3/4, F5/6, F7/8, FC1/2, FC3/4, FC5/6, FCz, FT7/8, Cz, C1/2, C3/4, C5/6, T3/4, T7/8, TP7/8, CP1/2, CP3/4, CP5/6, Pz, P3/4, P5/6, P7/8, PO3/4, PO5/6 PO7/8, POz, Oz, O1/2, HEOL, HEOR, ECG, VEOL, VEOU) and referenced to CPz. A portable wireless EEG amplifier (NeuSen.W64, Neuracle, China) was used for data recording at a sampling rate of 1000 Hz. Electrode impedances were kept below 10 kOhm for all electrodes throughout the experiment. The experiment was carried out in a natural office environment without any electrical shielding.
2.4 alpha-band power ratio
In this study, neurofeedback training was performed to increase the alpha-band amplitude. The alpha-band power was calculated in order to examine the training outcome. Since the absolute value of the alpha-band amplitude is affected by the current state of the subject and the environment, they are not comparable between different trials. In the aim to contrast the results of NFT parallelly, we calculate the ratio of the power which the alpha-band to the 0.8 Hz-20 Hz band to characterize whether the alpha amplitude is improved. In the following formula, X1 and X2, denoting the power of the alpha band and full band. In the formula, X denotes the EEG signal and f denotes the frequency.
The resting-state data was obtained by two-minute open-eye and two-minute closed-eye experiments. The closed-eye data totaled 120,000 sampling points with a sampling rate of 1000, and the frequency domain resolution was 1/12 Hz.
2.5 SSVEP stimulation
The processing system consists of a stimulation device, an EEG acquisition device, and a processing computer. The stimulation device is ASUS MG279Q 27-inch display with 2560*1440 resolution and 143 Hz refresh rate. The processing device is an Intel [email protected] GHz CPU and DDR4 32 GB memory. Stimulation and processing programs were developed under Matlab 2015a and Psychophysics Toolbox Version 3.
During the experiment, the stimulation was presented in the form of a multi-target flicker. A start trigger and an end trigger signal were sent to the EEG collector at the beginning and end of each trial. The processing device receives the EEG data from the EEG amplifier in real-time and feeds the judgment results back to the stimulation device after the identification is completed.
As the Fig. 2 and Fig. 3 shown, in the SSVEP test, a 40-target SSVEP speller based on frequency phase modulation coding (frequency range: 8-15.8 Hz, frequency interval: 0.2 Hz) was used (Chen et al., 2015). A total of 40 stimulus target blocks were set on the screen, which respectively represents A-Z with a total of 26 letters, 0–9 total ten digits, spaces, commas, periods, and backspace keys. In the experiment, each stimulus target was constructed by sampling the sinusoidal method (Chen et al., 2014; Manyakov et al., 2013).
In the entire SSVEP task, in order to guarantee the actual state of subjects and the validity of the data, the staff required each subject to complete six blocks (40 trials each block). Participants can choose to rest between blocks to perform their best. Thus, the entire test takes about 40 minutes.
2.6 SSVEP performance calculation
The entire online test (feedback results in real-time) is divided into six blocks. The four blocks prior use a 3–4 second dynamic window and are classified applying the filter bank canonical correlation analysis (FBCCA) algorithm (Chen et al., 2015). The last two blocks use a 1–2 second dynamic window and applying the task-related component analysis (TRCA) algorithm for classification (Nakanishi et al., 2017), where the training data for TRCA was the four blocks prior. Moreover, for each target, six trials take into consideration.
The average SNR of nine leads in the occipital lobe (PO3/4, PO5/6 PO7/8, Oz, O1/2) and the information transfer rate (ITR) were examined to estimate the SSVEP performance of the subjects. In the formulas below, N is total target numbers, A means accuracy, T represents the time required to identify.
For the SNR calculation, flexible band definitions around signals and noise could be made as needed in the engineering project. In this study, SNR was used as an indicator of SSVEP performance to minimize the effect of the background EEG activity across subjects. Therefore, the power of the signal is specified that the power of a narrow frequency interval centered on the stimulus frequency (f±0.2). At the same time, the power of a broader scale (f±1) is noise. In the formula, X denotes the EEG signal and f denotes the frequency.
2.7 Brain network analysis
The whole-brain could be evolved into a complex network while a neuron or a group of nerve cells and the neural connection is regarded as a node and an edge, respectively. Human response to stimulation and emotional behavior might represent the performance of network overlays. Therefore, the research methods for complex networks could be applied to the research of brain science, and the results of network analysis reflect the structural or functional connection between various brain regions that could provide more clues for explaining the deep mechanism. Traditional network analysis methods are canonical correlation analysis (CCA) and directed transfer function (DTF). In this study, the DTF and information flow were computed with the eConnectome Toolbox (Babiloni et al., 2005; Wilke et al., 2010; He et al., 2011) developed by the He Bin team. Define the DTF as y2ij and the information flow of region m is computed as the formula below.
Referring to Yan's work (Yan and Gao, 2011), the information flow gain ρ was also calculated in this study. The information flow gain ρ is defined as the ratio of the outflow information and the inflow information. The higher the value of the node, the more significant the contribution of the node in the transmission of network information. The smaller the value of ρ, the lower the contribution of the node in the transfer of network information.
2.8 Feedback procedure
This study analyses the global brain information connection network induced by SSVEP using the information flow method. In Fig. 4, it can be seen that there is abundant information flow in the SSVEP state network. The parietal-central (FC1, FCz, FC2, C1, Cz, C2, CP1, CP2) plays an essential role in the induction network while the node size stands for ρ. The higher the value ρ of the node, the more significant the contribution of the node in the transmission of network information. Consistent with this, in 2000 Corbetta reported the control effect of the parietal lobe in visual tasks (Corbetta, 2000). Taken together, it suggested that the parietal lobe plays as a central region in visual tasks with attention reorienting effect. The position of the neurofeedback training was selected in the parietal lobe, in which the location with the lowest alpha amplitude was regulated. The electrode selected varies from subject to subject. The training only targets one electrode-position in order to better detect the result.
The NFT system is arranged like Fig. 5. Considering that suspension will require the neuroplasticity of children is stronger, the study selected subjects aged from 7 to 12. In order to attract the interest of the subjects and make them be co-operative, the neurofeedback interactive interface is designed in the form of a game (the interface display like Fig. 6). The program has three options and subjects could select one of them to execute: a. Pacman (A kid eats beans on a given route. Feedback parameters above and below the threshold set corresponding to game pause and progress status respectively). b. Fly the airplane (The aircraft flies on a given route. Feedback parameters above and below the threshold set corresponding to game pause and progress status respectively). c. Racing car (Driving the car on a given route. Feedback parameters above and below the threshold set corresponding to game pause and progress status respectively). Regardless of the form of the game, the subject is always able to keep the EEG parameters above the feedback parameters, thus achieving the effect of neurofeedback training.
The setting of the feedback parameter is divided into two parts, first is the frequency setting and the other is the amplitude setting. Before the formal training, the subjects tested for different feedback frequencies in alpha-band. And the frequency under which the rate of success (neurofeedback training) achieves 60% − 70% is set as the feedback frequency. For the amplitude, it has been found that the amplitude is set to 0.2–0.3 times of the alpha amplitude when subjects are closing their eyes. In this case, the success rate of the training is also 60% − 70%. The feedback process is controlled as Fig. 7 shown. Besides, we adjusted the feedback parameters appropriately according to the success rate (usually increasing the feedback frequency) as the training goes on. The sampling rate resampling to 250 Hz during calculation while the original sampling rate is 1000 Hz. The length of the calculation window is 2 seconds, and the overlap of the calculation window is 50%. Data update time shall not exceed 1 s.
There are 2000 points used to compute the feedback parameters. The sampling rate is resampled to 250 Hz during calculation while the original sampling rate is 1000 Hz. The length of the calculation window is 2 seconds, and the overlap of the calculation window is 50%. Data update time shall not exceed 1 s. The welch method is used for the power computation. And there is a bandpass filter (0.5–40 Hz) added to the original data.
Subjects involved in neurofeedback training will sit in front of the computer wearing an EEG cap, gaze at the screen and follow the instructions to complete the task. The system can feedback EEG parameters in real-time and determine whether the respondents are successful. If the feedback is successful (feedback parameters above the threshold), subjects can continue the game. Otherwise, the suspension will require the subject to adjust their status. At this time, the instructor would lead the subject to pay attention to the game. And the subjects should try to adjust state by themselves.
2.9 Integrated Visual and Auditory Continuous Performance Test
The Integrated Visual and Auditory (IVA) continuous performance test (CPT) is a screening tool testing sustained attention (Sandford and Turner, 2000). It was developed by John Sandford (psychologist) and Anne Turner (physician). The initial test consisted of a series of pseudo-randomized auditory and visual stimulation (totally 500 trials) and asked the subject to click on the target they heard or saw. The entire test lasted approximately 20 minutes and our research selected part of the parameters to examine subjects. They were asked to complete some paper quality tables and tested under the guidance of the instructor. The test is a real-time task consisting of repeated auditory and visual stimulations. During the test, the instructor would guide the subject to observe a series of graphics and texts on the scale. Subjects would also listen to a short essay with overt information. The instructor would then ask the participant to give the information required and answer the questions on the scale. The test is used to monitor the effectiveness of neurofeedback training or medication generally.