Portable wireless neurofeedback system of EEG alpha rhythm enhances memory

Abstract

Background: Effect of neurofeedback training (NFT) on enhancement of cognitive function or amelioration of clinical symptoms is inconclusive. The trainability of brain rhythm using a neurofeedback system is uncertainty because various experimental designs are used in previous studies. The current study aimed to develop a portable wireless NFT system for alpha rhythm and to validate effect of the NFT system on memory with a sham-controlled group.

Methods: The proposed system contained an EEG signal analysis device and a smartphone with wireless Bluetooth low-energy technology. Instantaneous 1-s EEG power and contiguous 5-min EEG power throughout the training were developed as feedback information. The training performance and its progression were kept to boost usability of our device. Participants were blinded and randomly assigned into either the control group receiving random 4-Hz power or Alpha group receiving 8-12-Hz power. Working memory and episodic memory were assessed by the backward digital span task and word-pair task, respectively.

Results: The portable neurofeedback system had advantages of a tiny size and long-term recording and demonstrated trainability of alpha rhythm in terms of significant increase of power and duration of 8-12 Hz. Moreover, accuracies of the backward digital span task and word-pair task showed significant enhancement in the Alpha group after training compared to the control group.

Conclusions: Our tiny portable device demonstrated success trainability of alpha rhythm and enhanced two kinds of memories. The present study suggest that the portable neurofeedback system provides an alternative intervention for memory enhancement.

Keywords: Alpha rhythm; Bluetooth; Memory; Neurofeedback; Wireless.

Fig. 1

Schematic drawing of the neurofeedback training (NFT) apparatus for user from signal amplification, data transfer, and display on a smartphone through Bluetooth transmission. ADC analog-to-digital converter, BLE Bluetooth low energy, EEG electroencephalogram, MCU microcontroller unit

Fig. 2

Schematic flowchart of the EEG signals acquisition, data analysis, and wireless transmission between the microcontroller and smartphone through Bluetooth transmission for NFT. FFT fast Fourier transform

Fig. 3

Timing diagram of the EEG signal acquisition, data analysis, and wireless transmission tasks

Fig. 4

The setup window (a) and the visual feedback (b) of the neurofeedback training system. Information of the visual feedback contains duration of success alpha episode (top) and alpha power (bottom)

Fig. 5

Schematic plot for a training session (top panel) and procedure of the three-phase experiment (bottom panel) in an NFT. The pretest and posttest phases contain three cognitive tasks. Twelve sessions are performed in an NFT. MMSE Mini-Mental State Examination

Fig. 6

Example of artifact of eye blink in electrooculogram (EOG) and EEGs of the Fp1 and C3 leads accompanied by their time–frequency spectrograms. The trace contains numerous eye blinks at the beginning followed by obvious alpha activation

Fig. 7

Example of artifact of EEG of the C3 lead and activities of mentalis muscle (EMG1) and masseter muscle (EMG2) accompanied by their time–frequency spectrograms. The trace at the beginning contains numerous muscular responses

Fig. 8

Dynamic changes of the mean alpha power ratio and alpha duration throughout 12 sessions in the two groups. *p < 0.05 when compared with the 1st session; ^#p < 0.05 when compared with the Control with Bonferroni correction

Fig. 9

Changes in the accuracies of the backward digit span test (left panel) and word-pair test (right panel) before and after the neurofeedback training of the two groups. *p < 0.05 when compared with the pretest; ^#p < 0.05 when compared with the Control with Bonferroni correction