**1. Introduction**

Noninvasive monitoring of brain activity in daily life is an important research topic in the field of healthcare that aims at a comfortable lifestyle. Mihajlovic et al. [1] demonstrated various aspects of wireless and intelligent wearable lifestyle electroencephalogram (EEG) solutions, and the technology behind the development of convenient, intelligent, and wearable, wireless EEG devices was explored. In addition, in their study, personality traits, sensory input, neuronal activity, conductive tissues, electrode-tissue interface, miniaturized and ergonomic EEG headsets, wireless and wearable EEG system designs, brain activity analysis, and output interfaces were discussed. Lin et al [2] proposed a Bluetooth-based real-time brain-computer

© 2016 The Author(s). Licensee InTech. This chapter is distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/3.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. © 2017 The Author(s). Licensee InTech. This chapter is distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/3.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

interface (BCI) system that can be used to detect drowsiness while driving. This system had integrated wireless physiological signal-acquisition and embedded signal-processing modules, and it featured real-time wireless drowsiness detection, long-term daily life EEG monitoring, high computation capacity, and low power consumption. The design and implementation of a Bluetooth-based wearable brain monitoring system was investigated by Sawan et al. [3]. A wireless data recording system was utilized for noninvasive and long-term monitoring of near-infrared spectrometry (NIRS) EEG signals. In addition, the wireless data recording system was applied to the field of invasive cerebral EEG detection. This system has a graphical user interface that is user-friendly and can be used to extract brain activity during dynamic tasks. The advantages of the designed system are portability, wireless connectivity, high throughput, reliable communication, and low-power consumption. Liao et al. [4] proposed a design method for the 16-channel EEG measurement system utilizing dry spring-loaded sensors, a Bluetooth-based acquisition system, and a size-adjustable wearable soft cap. Vos et al. [5] demonstrated an efficient low-cost mobile EEG system that utilizes a P300-based speller for wireless BCI.

The development of high-speed, and reliable EEG transmission schemes are interesting research topics. Channel coding is a solution that can be used to achieve a lower error probability for EEG communication. The fundamental design parameters of channel coding are error probability, complexity, and decoding time. Low-density parity-check (LDPC) code is a channel coding technology that was proposed by Gallager [6]. Limpaphayom et al. [7] proposed a power and bandwidth-efficient communication system that utilizes irregular LDPC component codes of block length 100,000, multilevel coding, multistage decoding, 64-quadrature amplitude modulation, and trellis-based signal shaping schemes. The proposed system achieved a bit-error rate (BER) of 10− <sup>5</sup> at an Eb/No of 6.55 dB. Franceschini et al. [8] described the concept ofLDPC codes. The regular (v, c) LDPC codes are linear block codes with a sparse parity-check matrix H, In H, the number of nonzero elements in the columns is v, while the number of nonzero elements in the rows is c. In addition, the code rate is defined as 1 − v/c. In an (N,K) LDPC code, the block length is N and the information length is K. Ohtsuki [9] applied LDPC codes to various transmission systems with excellent performance, and illustrated some of the LDPC code designs. LDPC codes are included in the second-generation specification for satellite broadband applications, and in IEEE 802.16e.

Filter bank-based multicarrier modulations (FBMC) is an interesting research topic, and it is being considered as a potential candidate for the fifth generation (5G) mobile systems. FMBC is a modified version of orthogonal frequency division multiplexing (OFDM), and a tutorial review of FBMC modulations was discussed by Boroujeny et al. [10]. Compared to cyclic prefix (CP)-based OFDM modulation, FBMC offers better spectral efficiency in multipath channels. Bouhadda et al. investigated the BER performance of nonlinear distortion in high-power amplifiers for FBMC using offset quadrature amplitude modulation (OQAM) [11]. Caus et al. [12] studied the effects of multi-tap filtering on FBMC/OQAM systems to combat intersymbol and inter-carrier interferences due to multipath fading. Caus et al. [12] proposed a low-complexity transmission power estimation method, and their simulation results show that the proposed transmission power estimation method is excellent. Further, Bellanger et al. [13] proposed an FBMC-based physical layer solution for 5G mobile systems.

In previous studies, a survey study of mobile telemedicine [14], mobile telemedicine using an advanced wireless multimedia communication application [15], an 802.11n wireless telemedicine application [16], a direct sequence ultra-wideband (DS-UWB) wireless telemedicine application [17], a multi-code code division multiple access (CDMA) mobile medicine system [18], a Ka band OFDM-based multi-satellite mobile telemedicine system [19], a Ka band wideband CDMA mobile telemedicine system [20], and a mobile cloud-based blood pressure healthcare system [21] were investigated. In this chapter, an advanced FBMC-based EEG transmission scheme is proposed. The design concept of the proposed advanced wireless EEG transmission system includes FBMC, LDPC, BPSK or OQAM, and a power assignment mechanism. Low power high-speed wireless EEG transmission was achieved.
