# A Wireless EEG System for Neurofeedback Training
## 1 Introduction  
The popularity of neurofeedback (NF) has grown significantly in recent years, making neuroscience more accessible to scientists and engineers as well as for various fields of physiological activity and tasks in pediatric studies. In computational neuroscience in particular, the functional neural network has been used to determine the relation between NF training and EEG connectivity in groups of children [1, 2]. Currently, NFs are actively used for medical purposes [1, 3] in combination with multi-modal stimuli for children with dyslexia [4, 5, 6, 7, 8]. An overview of the possibilities of EEG NF training also described quite favorably the prospects in this area [1, 3, 4, 5, 9, 10]. One of the limiting factors for the widespread use of NF is the difficulty in reading EEG signals, as they are susceptible to various types of noise (e.g., electromagnetic), motion artifacts [11], and variability in EEG signal features [12]. Due to the non-stationary neuronal activity, the requirements for professional EEG mobile devices are high quality and a high resolution for the applied analog front-ends in them. The guidelines of the American Society of Clinical Neurophysiology recommend that the required features of effective EEG systems be a minimum number of channels to obtain a good spatial resolution (of 32 channels) and application of effective algorithms to detect events, improving the efficiency level of the system [13]. Commonly used EEG acquisition systems are either expensive or difficult to deploy on a large scale, as the system is fixed and difficult to expand, or the performance and usage scenarios are limited [1]. Wireless systems have significant improvements compared with traditional wired EEG systems, such as protection against electromagnetic interference and the flexibility of monitoring the out-of-hospital environment to transmit data in any situation [1; 14]. Being lightweight and small enough, these systems are wearable, consume little power for long-term monitoring, and have the computing power to process data for online decision making [1; 14]. The limitations that slow the widespread implementation of wireless EEG systems are a short battery life, moderate signal resolution, small number of channels, and wireless bandwidth [1]. Such quality can be better achieved by low-noise programmable analog-to-digital convertors (ADCs) for EEG and ECG signals, such as ADS1298/9 [9; 15; 16; 17; 18; 19; 20; 21]. The professional mobile EEG apparatuses on the market are not for outdoor use or extensive NF research [1]. There are no low-cost and compact data acquisition systems available that allow good reconfiguration of scaling, hardware, and up to 32 or more channels spatially for outdoor widespread applications [1]. The EEG systems support streaming and event detection modes that have different power consumption. High sampling rates and a large number of channels are rarely achieved in a single device on modern wireless EEG acquisition platforms [1; 9; 15; 16; 17; 18; 19; 20; 21; 22]. In applications intended for children in outdoor environments, the need for wireless systems capable of autonomous survey and record NF information processing becomes apparent. In outpatient settings of pediatric studies, EEG systems should be small and portable with low power consumption for long-term operation and have a sufficient computing ability for online data transferring and processing during various tasks [14; 16]. Low-cost modular board amplifiers were used for EEG acquisition in the current study, maintaining significant cost savings. The proposed design would allow an easily scalable system with adaptive spatial and frequency characteristics for different NF-EEG tasks. Section 2 presents an overview of the real-time EEG acquisition system solutions and state-of-the-art designs. Different views of the architecture and technical decisions are shown in Section 3. The zNF-EEG procedure and results are described in Section 4. An overview of the results is discussed in Section 5.