1. Introduction

Variegated branching patterns and trends of sympathetic neurons for realizing the brain function/dysfunction have yet to be completely definitized so far. A functional neuroimaging technique of the human brain has established itself as a trustworthy visible tool to definitize indeterminate patterns and discover new functions [1]. Indeed, visualized information through neuroimaging techniques has contributed building intuitive understanding and relative quantification of brain functions [2, 3].

Key benefits of the electroencephalographic (EEG) modality hold over other neuroimaging techniques (e.g., local field potential, near infrared spectroscopy, and electrocorticogram) are the high

© The Author(s). Licensee InTech. This chapter is distributed under the terms of the Creative Commons © 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.

Attribution License (http://creativecommons.org/licenses/by/3.0), which permits unrestricted use, distribution, and eproduction in any medium, provided the original work is properly cited.

temporal resolution on the order of milliseconds, the small installation space for operating systems, and its usability in noninvasive recording [4]. Although the spatial resolution and specificity are low because it observes the volume conduction effects in brain network [5], this has been attracted attention as a viable and inexpensive modality to study kaleidoscopic functional states of the cerebral cortex: where, when, how, and under what our brain functions come into being [6]. Therefore, providing a capacity to adapt EEG systems to real environments is always a major challenge for neuroscientists and neuroengineers on the final stretch of constructing systems.

Using an extremely small number of electrodes (the single-electrode case would be an extreme case) for signal acquisition should result in better practical application in daily life. Recently, specialized (headband type or headset type) devices, which are endowed with small number of electrodes less than gold standard devices having 16, 32, 64, or more channels, have been developed as for compact, portable, and feasible EEG systems to use themselves in the real environments [7]. The devices are usually implemented with dry electrodes and wireless sensor network technology for recordings. These can diminish the burden on the user caused by oppressive feeling in the head, eliminate the discomfort from conductive gel or paste, and improve degree of freedom of movements by doing away with wires plugged into an amplifier [8].

However, technical/biological artifacts, such as active power line interference, eyeblink, and muscle activity caused by recording mistake, good conductivity of the scalp, and so on, are often mixed with EEG signals whether the type of device is gold standard or specialized. They ingeniously disguise themselves as EEG components in observed EEG signals and cause a discrepancy between research motivation and system realization. Removing mimetic components (artifacts) or extracting intrinsic EEG components from observed EEG signals will become a more important process in all EEG systems for practical use even if single electrode is integrated with data acquisition module by a specialized device.

Disclosing the meaning of electric signals comprising various neuronal populations (sources) breaks down the EEG inverse (blind source separation (BSS)) problem [9]. It is well known that the enormous indeterminacies in brain make the BSS problem ill-posed; however, statistical natures lead to restoring the well-posedness of the problem in a biosignal processing. By the properties, theoretically multivariate statistical analysis approaches like independent component analysis (ICA) can separate observed EEG signals into spatially and temporally distinguishable components effectively, and then, estimated components will be identified as neuronal or artifactual sources by hard/soft threshold to reconstruct artifact-free EEG matrix [10, 11]. Whereas there are several reviews on artifact rejection methods including overall procedure (signal separation, component identification, and signal reconstruction) for multi-channel EEG signals [12–16], we have never seen review of artifact rejection methods for single-channel EEG signals. In this chapter, we therefore describe algorithms for artifact rejection in multi-/single-channel EEG signals.
