Biofeedback or neurofeedback is an operant conditioning paradigm to learn how to control physiological behaviors through a series of trial-and-error processes . Neurofeedback provides valuable information derived from real-time brain activity, such as electroencephalography (EEG), and displays the result on an interface of visual, audio, or other modalities. Thus, users can learn to control their brain activities through a neurofeedback apparatus. The control ability of brain rhythms from users often elicits a neural plasticity in the brain then affects their behaviors and cognitive functions as well [1–3].

The alpha rhythm of 8–12 Hz displays in the occipital cortex and its neighboring area under an eye-closed condition. Originally, functional hypothesis of alpha rhythm is related to cortical inhibition [4], which may be crucial to lower brain activity. Neurofeedback training (NFT) of alpha rhythm is employed quite frequently as a treatment for several clinical disorders such as anxiety [5] or depression [6]. However, its effectiveness is still debated in clinic (for review see [7, 8]). In addition to clinical application, NFT of alpha rhythm is also assessed on cognitive function in healthy subjects [2]. Numerous studies show positive effect of the alpha NFT on attention or memory [9, 10]. However some studies have no enhancement in memory or cognitive function with altered amplitude or peak frequency of alpha rhythm [11–13]. Several reasons are crucial for these controversial effects. The trainability for an NFT system is the first issue. Previous studies have indicated a short training session being insufficient for cognitive enhancement [12–14]. Can we develop and validate a possible NFT apparatus for a long-term training with a great usability? That will be very important to increase training sessions of the NFT under laboratory/clinical settings or daily environment.

Additionally, most previous studies are lack of comparable control group [9–11] or absence of the control group [15, 16]. Different experimental designs often cause controversial results in the findings. Identical exposure for a training apparatus and the same protocol between the control and experimental groups are crucial to identify effect of an intervention. To reduce possible selection bias of participants and related internal validity, a sham-controlled experimental design is more appropriate to validate effect of an NFT on memory or cognitive function [12, 17]. Thus, the experimental design with a sham control group is beneficial to determine the NFT effect in a new developed system [18].

Most available neurofeedback systems are laboratory-designed and contain wires to the training machine, resulting in inconvenience or constraints for subjects. Wireless technologies are widely utilized in medical devices and biomedical research [19, 20]. A wireless recording not only improves the system convenience but also reduces the possible artifacts from recording wires [21]. Additionally, the main functions of a neurofeedback training program are easily implemented with a smartphone application. Users can install the application and perform the training procedures on their own devices. Due to the portability brought by the utilization of the smartphone, the proposed system can be easily used in a daily life [22, 23].

The present study aimed to develop a portable neurofeedback training system for subjects to perform trainings in a flexible training environment. The proposed system comprised an EEG signal analysis device that was wirelessly connected to smartphones by Bluetooth low energy wireless technology. The system illustrated interactive information of current alpha power to training subjects. Subjects learned how to produce and control the alpha rhythm as much as possible through visual feedback indexes. Moreover, both working memory and episodic memory before and after the training were evaluated compared with a sham-controlled group to assess performance of the wireless NFT system.

Thirty healthy participants (age 26 ± 3 years) from the National Cheng Kung University were blinded and randomly assigned into two groups (i.e., Alpha, n = 15; control, n = 15). All participants were right-handed and had no experience of taking NFT in the past. The two groups had no difference in the factors of gender (p = 0.75), age (p = 0.46), and education (p = 0.39). The entire experiment complied with guidelines and regulations in the Institutional Review Board of National Cheng Kung University Hospital. Informed consent was provided and signed for all participants.

Hardware architecture

The hardware architecture of our portable neurofeedback system (Fig. 1) primarily contained an EEG amplification board, a microcontroller module, and a smartphone. The EEG amplification board aimed to amplify brain activity. The microcontroller module was responsible for EEG data sampling and to control wireless transmission of a Bluetooth module. The smartphone was used to receive and calculate wireless EEG data as a visual feedback and to save data.

The present study used a single-channel EEG recording through Ag/AgCl electrodes. Based on previous neurofeedback studies [1, 17], we selected a C3 channel as an active lead with a reference over the contralateral mastoid area (M2) according to the 10–20 system [24]. A ground electrode was placed over the Fpz region. The EEG signal was amplified with a gain of 10,000 through an instrumentation amplifier (AD623, Analog Device, Texas) in combination with two non-inverting operational amplifiers (AD8538, Analog Device, Texas) within a frequency range of 0.15–50 Hz [25]. The amplified EEG was then positive-biased to an analog-to-digital converter (ADC) of the microcontroller.

The microcontroller module included a MSP430F5438 integrated chip, which embedded with a MSP430 microcontroller unit, 256 kB flash memory, 16 kB RAM, and other peripherals such as an 8-bit ADC and three 16-bit timers. The MSP430 digitized data through an embedded ADC with 128 Hz and transferred sampled EEG data to a Bluetooth module. Afterwards, the Bluetooth module transmitted the data to a smartphone. The core component of this Bluetooth module was a Nordic nRF8001 chip that integrated a fully compliant Bluetooth radio and link layer controller. Bluetooth is designed for short-range and low power wireless communication, and it is widely adopted in personal computers and consumer electronic devices, e.g., mobile phone or media player. The present study used the Bluetooth version 4.0, which aimed at applications in the fitness, healthcare and security areas because it provided lower cost, lower power consumption, and a comparable communication range than a traditional Bluetooth protocol [20, 23].

Data analysis and wireless transmission of the NFT

The data analysis task fetched the 1-s sampled EEG data in the buffer and then performed fast Fourier transform (FFT) to calculate the power of the alpha rhythm. Both raw data and calculated data were transmitted immediately to the smartphone via Bluetooth communication. Figure 2 shows the flowchart of the firmware (left) of the microcontroller, including the main program for EEG acquisition, EEG analysis, and wireless transmission. The smartphone received and displayed the alpha power and the total success duration for 1-s alpha events. Participants saw all training performance in terms of changes of alpha power and alpha duration throughout the training sessions over a smartphone. In addition, information of EEG changes with regard to training number per day displayed on the smartphone at the end of each training session.

Figure 3 reveals timing diagram of the EEG signal acquisition, data analysis, and wireless transmission tasks running on the microcontroller unit (MCU). The timing was obtained by toggling an MCU I/O pin at the start and end of the task and measuring the duration via an ADC (USB-6009, National Instruments, TX). The data analysis task was performed every 128 sampling periods. The Bluetooth transmission was triggered immediately after the completion of the data analysis task. The execution time of the data analysis task and the latency for wireless transmission were 172 and 2.7 ms, respectively. Although the data analysis task spanned several sampling periods, sampled data collection always performed with a higher priority.

Training interface of the NFT

The training application running on the smartphone was utilized for displaying real-time feedback. When the application started, users entered the desired time length of the training procedure and set up a Bluetooth connection between the signal analysis device and the smartphone (Fig. 4a). The connection was set up by clicking the “Discover All Devices” button to search nearby Bluetooth devices. The identification and type of Bluetooth devices displayed in a list to allow a user to select a target for Bluetooth connection setup. Thus, the training procedure was ready. Thereafter, quality of EEG recording was ascertained when the amplitude was < 100 μV_rms after properly adjusting electrode–electrolyte-scalp conjunction.

During the training procedure, the training application received real-time consecutive 1-s EEG data wirelessly and displayed the information of calculated alpha power on the screen of the smartphone. Figure 4b shows the information of success episode number, alpha power of the current episode, and changes of consecutive alpha powers. The blue bar of Fig. 4b reflects current alpha power. The waveform shown in the bottom panel represented consecutive alpha powers. The received EEG data was stored in the internal storage of the smartphone for future analysis. Users were able to terminate the training procedure before the end of the procedure by clicking the Disconnect button. At the end of each session, alpha powers and success number of 300-s training episodes displayed on the smartphone to allow user/researcher to develop or establish their strategy through trial-and-error learning [26].

Experimental procedure

To verify the effect of the proposed system on memory, three-stage experiment (i.e., pretest, training, and posttest) was designed. The pretest and posttest of three cognitive tests were performed immediately before and after the training stage. During the training stage, the 1-channel EEG signal (C3-M2) was utilized. Subjects in the Alpha group received the projection of alpha power (8–12 Hz) on the screen of a smartphone. The control group received various randomly selected 4-Hz bandwidth in the range of 7–20 Hz for every 1-s event, which was used in our previous study [26].

At the beginning, brain activity was recorded and analyzed to assess its noise level, including artifacts of eye blink or muscle contraction, etc. To reduce possible artifact signals, each subject was reminded before the training [26]: (1) avoiding frequent eyes blink; (2) eyes closure or fall asleep was informed as an invalid strategy; (3) avoiding body’s movement or shaking/nodding head; (4) avoiding too much facial expression intentionally. A digital camera was used to rule out the effects of these behavioral artifacts.

Twelve training sessions were performed within 3 weeks (Fig. 5). Four sessions were performed per week. A session contained 5 blocks, and each block took 5 min. In the beginning of a training, a 2-min baseline EEG was recorded followed by 1-min rest. Thereafter, a 5-min training block followed by a 1-min resting period was performed. Subjects used the proposed system and attempted to increase activities of particular rhythms shown on the screen of a smartphone.

In an NFT, participant can see the instantaneous information of the 1-s power of a selected bandwidth and the waveform of all consecutive 1-s powers of a selected bandwidth. The instantaneous power was expressed in a horizontal bar (Fig. 4b). If an EEG power increased, the bar moves to right side. Otherwise, an EEG power decreased, the bar moved to left side. Participants were instructed to move the bar to the rightmost position and to maintain the bar as long as possible.

During the 1-min rest period between two blocks, we tried to help participants to develop a good strategy using the information of consecutive 1-s power information (the bottom panel of Fig. 4b). For example, we pointed out timestamps with higher power in the training block and asked participants to recall the strategy they used. During the inter-block rest, we encouraged participants to try their best to move/control the bar. Although the control group seemed to be uneasy with controlling their brain activities, they reported no difficulty and frustration during the training.

A portable system based on low-power wireless Bluetooth technology was developed for NFT of EEG’s alpha rhythm. The proposed portable system utilized a modern smartphone to control the training procedure and visual feedback of 1-channel EEG information. The present study provided evidence that neurofeedback can be implemented in a tiny size of EEG signal device with the mobility of a smartphone. The NFT system increased alpha power and alpha duration throughout the training in the Alpha group exclusively. The Alpha group had significant elevation of memory in terms of the word-pair task and backward digital span task compared to the control group. The findings demonstrate the effect of alpha rhythm on both working memory and episodic memory, which extends the findings on enhancement of working memory and attention in previous studies [9, 10, 26]. These results indicate that memory enhancement is greatly related to activation of alpha rhythm.

Most previous research related to portable or wireless biomedical systems mainly focused on the applications of physiological signal recordings or health monitoring [20, 30]. Compared to these wireless systems, the present study added a feedback module and provided evidence of memory enhancement with a well experimental design. Most previous studies use neurofeedback apparatus with wire connection and considerably bulky size [9, 10, 31]. Our mobile device provides a feasibility with wireless connection and advantage of tiny size for portability. Moreover, both working memory and episodic memory were enhanced in our wireless NFT apparatus, which seems to be comparable to some systems [10, 26] or superior to previous apparatus [9, 11]. According to portability of tiny size and friendly interaction with a mobile device, our NFT system can motivate people to increasingly use in our living environment to enhance cognitive function. With this scenario, participants can receive more training opportunities conveniently in our portable NFT system.

The system showed significant increases in alpha power and duration between the two groups in the 5th–12th sessions (Fig. 8). In our previous study [26], a significant increase in alpha power began in the 8th session. The slight discrepancy between our previous study and the present study may be due to lower threshold to determine alpha rhythm (1.2- vs. 1.5-fold) or short training schedule (12 sessions in 3 or 4 weeks). When we calculated the results using 1.5-fold threshold, there were significant differences in alpha power and duration between the two groups in the 8th–12th session (data not shown). Obviously, a higher threshold delays the occurrence of significant difference between the two groups. The present study advances our understanding of using a lower threshold and intensive training to indicate a better performance in both power and duration of the alpha band. The early remarkable increase in alpha power or duration perhaps creates potential motivation of a user in an NFT.

The wireless neurofeedback system showed a great ability to enhance both alpha rhythm and memory performance. Our device demonstrated its trainability of alpha rhythm within a short period. This is remarkable because there is typically a three- to tenfold higher number of sessions being utilized in clinical therapy [32, 33]. In general, a portable and flexible training apparatus has a potential advantage to be increasingly used in a laboratory environment or in our daily life setting. To motivate the use of NFT in a daily life, our system provides the information of training date and time as well as changes of EEGs for all training sessions. The information may be useful to drive user’s motivation or to trace neural plasticity for clinical studies [23]. As so to ascertain the system on a daily environment, usability of the portable training device in clinical settings needs to be further evaluated.

Most concerns on a portable device will be artifacts in the recording [14, 18, 23]. In the present study, we have illustrated two major kinds of artifacts, i.e., EOG and facial EMGs, during the NFT. Fortunately, the alpha NFT has little influence by these two external interference. These results not only support the recording quality of our portable apparatus but also suggest a successful training on alpha rhythm.

The present study used an 8-bit ADC and showed progressive increases in the duration and power of alpha rhythm in the Alpha group exclusively. The results indicate that specifications of our system are able to provide valuable advantage for alpha neurofeedback training. Similar resolution of an 8-bit ADC has been used in other physiological recordings, such as electrocardiogram [30]. Compared to an 8-bit ADC, higher resolution ADCs, such as 12–24 bits, are available in some applications to extract subtle changes [23]. As more and more 24-bit ADCs appear on the market and propose theoretical sensitivity for the least significant bit of 3 nV, the remaining issue will be to improve the signal-to-noise ratio to be able to take advantage of this decrease in quantization error of the EEG signals. Whether a high-resolution ADC shows a better performance on NFT still remains to be demonstrated.

Previous neurofeedback studies have used within-subject experimental designs with inappropriate control group or absence of the control group [9–11, 15, 16]. They mostly provide the information about individual effects of cognitive performance or memory before and after training or only provide correlations between the alpha power increase and memory enhancement [9, 10]. A sham-controlled group is better to explain the effect of neurofeedback on cognitive function [3, 12, 17]. The current study validate the effect of alpha neurofeedback training on memory in terms of a mixed-subject experimental design with a sham-controlled group. The control group showed no change throughout 12 sessions training. Only the Alpha group revealed progressive and significant increase in alpha power and alpha duration throughout the training. The brain training has a good control here. On the other hand, both groups had no different memory performance before NFT, which implies a good behavioral control in the current study. The Alpha group showed significant memory enhancement compared to the control group after the training. These results suggest absolute enhancement on working and episodic memories in response to alpha NFT.

In the backward digital span task, there was no accuracy change before and after NFT in the control group. However, the control group had significantly increased accuracy in the word pair task after NFT. The phenomenon is similar to our previous study [26]. A possible reason for the phenomenon may be a learning effect in the word pair task. The Alpha group had significant higher accuracy compared to the control group after NFT. Under a sham-controlled design, we can measure true effect of the alpha NFT on memory with a consideration of possible learning or placebo effect.

In terms of alpha power or total alpha duration throughout the 12 training sessions, alpha rhythm had a progressive increase (Fig. 6). The phenomenon supports that the alpha power of a person is trainable. There was no dramatic change in these two indexes at the beginning of the training session, which may a latent trial-and-error learning behavior [26]. The entire training causes significant alpha enhancement, which suggest a brain plasticity taking place during the training. Most interestingly, both working memory by the backward digit span task and episodic memory by the word-pair task had significant improvement in the Alpha group (Fig. 9). Previous studies have indicated that higher alpha rhythm are associated with better memory or cognitive performance [2, 34]. The present study provides more supporting evidence that a portable device for neurofeedback training induces neural plasticity and enhances cognitive functions. A possible mechanism for alpha enhancement on memory may be related to active cortical inhibition before cognitive task [2, 35], which results in increased capacity for learning or storage. Another possibility may be due to substantial alpha rhythm as a selective filter to enhance signal-to-noise ratio of perception and/or attention (i.e., neural efficiency hypothesis) [36], which may lead to a better memory process.

Participants in a neurofeedback paradigm gain significant cognitive improvement or ameliorate clinical symptoms [2, 7, 32, 33]. This portable system enhanced alpha rhythm and elevated memories. The present study provides additional evidence to determine a non-pharmacological alternative intervention on memory enhancement. Brain rhythms, such as sensorimotor rhythm and theta wave, reveal different effects on cognition or clinical syndromes [2, 17, 33]. It will be interesting to extend the system application in various brain rhythms to enhance cognitive functions in healthy subjects or to ameliorate pathological symptoms in patients.

A portable wireless neurofeedback training system of EEG alpha rhythm was developed and validated in terms of trainability by changes of alpha power and alpha duration and enhancement of working and episodic memory. The portable system may be better to gain more training opportunities in a daily setting to continuously elevate or maintain memory. The device provides an alternative non-pharmacological intervention for memory enhancement.