Neural Interface Engineering 3030418537, 9783030418533

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Liang Guo  Editor

Neural Interface Engineering Linking the Physical World and the Nervous System

Neural Interface Engineering

Liang Guo Editor

Neural Interface Engineering Linking the Physical World and the Nervous System

Editor Liang Guo Department of Electrical and Computer Engineering The Ohio State University Columbus, OH, USA Department of Neuroscience The Ohio State University Columbus, OH, USA

ISBN 978-3-030-41853-3    ISBN 978-3-030-41854-0 (eBook) https://doi.org/10.1007/978-3-030-41854-0 © Springer Nature Switzerland AG 2020 This work is subject to copyright. All rights are reserved by the Publisher, whether the whole or part of the material is concerned, specifically the rights of translation, reprinting, reuse of illustrations, recitation, broadcasting, reproduction on microfilms or in any other physical way, and transmission or information storage and retrieval, electronic adaptation, computer software, or by similar or dissimilar methodology now known or hereafter developed. The use of general descriptive names, registered names, trademarks, service marks, etc. in this publication does not imply, even in the absence of a specific statement, that such names are exempt from the relevant protective laws and regulations and therefore free for general use. The publisher, the authors, and the editors are safe to assume that the advice and information in this book are believed to be true and accurate at the date of publication. Neither the publisher nor the authors or the editors give a warranty, expressed or implied, with respect to the material contained herein or for any errors or omissions that may have been made. The publisher remains neutral with regard to jurisdictional claims in published maps and institutional affiliations. This Springer imprint is published by the registered company Springer Nature Switzerland AG The registered company address is: Gewerbestrasse 11, 6330 Cham, Switzerland

Preface

Following recent worldwide advocations on brain-related research and bioelectric medicine, neurotechnologies have become one of the hottest scientific and technological frontiers attracting enormous academic and public interests. Not only are governments and private foundations generously investing in this field, but many industrial giants like Facebook, Google, and GlaxoSmithKline together with new startups like Neuralink and Kernel are also enthusiastically stepping into this venture. As the projected technological market is expanding unprecedentedly, interests in further learning the neurotechnological developments are growing fast in both the technical community and the general public. In developing such body-machine symbiotic systems, the scientific community recognized the neural interfaces as the technological bottleneck hindering further advance of the field. As a result, tremendous efforts have been invested on neural interface engineering, leading to booming of this area over the past decade with a variety of exciting new developments. This book thus focuses on this important topic of neural interface engineering. This book is targeted for graduate and advanced undergraduate students of bioengineering, biomedical engineering, applied physiology, biological engineering, applied physics, and related fields; for biomedical engineers, neuroscientists, neurophysiologists, and industry professionals wishing to take advantage of the latest and greatest in this emerging area; and for medical practitioners using products of this field. Readers in public services and government funding agencies may also find this book useful in learning the latest in the field. This book provides an introduction to and summary of representative major neural interfacing technologies used to directly transmit signals between the physical world and the nervous system with the ultimate goals for repairing, restoring, and even augmenting body functions. It offers the readers a unique opportunity to obtain a panorama of this vibrant area in a handy format while elaborating the most important new developments. It covers classic noninvasive and invasive approaches for neural interfacing, as well as recent emerging techniques including advanced implantable neural electrodes and nanomaterial-assisted and genetically engineered neural interfaces. v

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Chapter authors on each topic are carefully selected among leading and practicing scientists. While it is not possible to cover all of the important approaches, for example, magnetothermogenetics and sonogenetics, due to unavailability of certain contributors, this book nonetheless strives to offer a comprehensive overview of the neural interfaces area to the readers, and it will be a valuable and convenient resource for grasping this specialized area comprehensively and in depth. Columbus, OH, USA  Liang Guo

Contents

  1 Electroencephalography��������������������������������������������������������������������������    1 Yalda Shahriari, Walter Besio, Sarah Ismail Hosni, Alyssa Hillary Zisk, Seyyed Bahram Borgheai, Roohollah Jafari Deligani, and John McLinden   2 Functional Magnetic Resonance Imaging-­Based Brain Computer Interfaces ��������������������������������������������������������������������   17 Jeffrey Simon, Phillip Fishbein, Linrui Zhu, Mark Roberts, and Iwan Martin   3 Transcranial Magnetic Stimulation��������������������������������������������������������   49 Gregory Halsey, Yu Wu, and Liang Guo   4 Intracortical Electrodes��������������������������������������������������������������������������   67 Meijian Wang and Liang Guo   5 Peripheral Nerve Electrodes ������������������������������������������������������������������   95 Yu Wu and Liang Guo   6 Failure Modes of Implanted Neural Interfaces ������������������������������������  123 Jean Delbeke, Sebastian Haesler, and Dimiter Prodanov   7 Strategies to Improve Neural Electrode Performance��������������������������  173 Katrina Guido, Ana Clavijo, Keren Zhu, Xinqian Ding, and Kaimin Ma   8 3D Cell Culture Systems for the Development of Neural Interfaces ��������������������������������������������������������������������������������  201 Omaer Syed, Chris Chapman, Catalina Vallejo-Giraldo, Martina Genta, Josef Goding, Emmanuel Kanelos, and Rylie Green   9 Conductive Hydrogels for Bioelectronic Interfaces������������������������������  237 Teuku Fawzul Akbar, Christoph Tondera, and Ivan Minev

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10 Biofluid Barrier Materials and Encapsulation Strategies for Flexible, Chronically Stable Neural Interfaces ������������  267 Jinghua Li 11 Regenerative Neural Electrodes ������������������������������������������������������������  281 Gildardo Guzman, Muhammad Rafaqut, Sungreol Park, and Paul Y. Choi 12 Passive RF Neural Electrodes ����������������������������������������������������������������  299 Katrina Guido and Asimina Kiourti 13 Wireless Soft Microfluidics for Chronic In Vivo Neuropharmacology ������������������������������������������������������������������  321 Raza Qazi, Joo Yong Sim, Jordan G. McCall, and Jae-Woong Jeong 14 Gold Nanomaterial-Enabled Optical Neural Stimulation�������������������  337 Yongchen Wang 15 Nanomaterial-Assisted Acoustic Neural Stimulation ��������������������������  347 Attilio Marino, Giada Graziana Genchi, Marietta Pisano, Paolo Massobrio, Mariateresa Tedesco, Sergio Martinoia, Roberto Raiteri, and Gianni Ciofani 16 Perspectives for Seamless Integration of Bioelectronic Systems in Neuromedicine����������������������������������������������������������������������  365 Vishnu Nair and Bozhi Tian 17 Voltage-Sensitive Fluorescent Proteins for Optical Electrophysiology ������������������������������������������������������������������������������������  383 Teresa A. Haider and Thomas Knöpfel 18 Optogenetics ��������������������������������������������������������������������������������������������  409 Aaron Argall and Liang Guo Index������������������������������������������������������������������������������������������������������������������  423

Chapter 1

Electroencephalography Yalda Shahriari, Walter Besio, Sarah Ismail Hosni, Alyssa Hillary Zisk, Seyyed Bahram Borgheai, Roohollah Jafari Deligani, and John McLinden

1.1  Introduction to Electroencephalography Human electroencephalography (EEG) was first introduced by the German psychiatrist, Hans Berger, who first recorded EEG denoting the potential activity of the brain in 1924 (Haas 2003). His first description of EEG noted, “The electroencephalogram represents a continuous curve with continuous oscillations in which…one can distinguish larger first order waves with an average duration of 90 milliseconds and smaller second order waves of an average duration of 35 milliseconds.” While EEG is one of the most common noninvasive approaches used to record the brain’s electrical activities, invasive recordings can be obtained on the cortical surface that yields an electrocorticogram (ECoG) or in deeper structures yielding intracortical recordings, including local field potentials (LFPs) and single-­unit recordings. The recorded EEG is the superposition of thousands to millions of neuronal potentials within a volume–conductor medium. A single electrode’s recording reflects a spatially smoothed version of the synchronized neural activities beneath a scalp surface on the order of 10 cm2 (Nunez and Srinivasan 2006; Nunez 2000). When many dipoles (~60 million) in the same area discharge synchronously, the

Y. Shahriari (*) · W. Besio Department of Electrical, Computer & Biomedical Engineering, University of Rhode Island (URI), Kingston, RI, USA Interdisciplinary Neuroscience Program, URI, Kingston, RI, USA e-mail: [email protected] S. I. Hosni · S. B. Borgheai · R. J. Deligani · J. McLinden Department of Electrical, Computer & Biomedical Engineering, University of Rhode Island (URI), Kingston, RI, USA A. Hillary Zisk Interdisciplinary Neuroscience Program, URI, Kingston, RI, USA © Springer Nature Switzerland AG 2020 L. Guo (ed.), Neural Interface Engineering, https://doi.org/10.1007/978-3-030-41854-0_1

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superposition of their action potentials causes deflections in the cortical potential that can be detected through noninvasive recordings such as EEG as a macroscopic measure of a large population of synchronous neural spikes (Lopez-Gordo et al. 2014).

1.2  Introduction to Brain Anatomy The human brain consists of two paired cerebral hemispheres covered with the cerebral cortex, which is a layered structure with a thickness that varies from 1.5 to 4 mm. It is a highly folded surface with gyri (ridges) and sulci (grooves) that enhance the processing capabilities of the brain while maintaining thickness. The cortex and a significant volume beneath it consist of the brain’s gray matter structures (neural cell bodies), while deeper white matter structures connect different gray matter areas and carry nerve signals between neurons. The cerebral cortex includes four major lobes: frontal, parietal, occipital, and temporal lobes. The frontal lobe includes the prefrontal area, which is involved in higher-order executive functions, including cognitive workload, decision-making, planning, and personality. The central sulcus (CS) separates the frontal and parietal lobes. The primary motor area (M1) and somatosensory area (S1) are located anterior and posterior to the CS, respectively. The parietal lobe consists of the somatosensory area (S1), which is associated with somatosensory information processing, and the posterior parietal cortex (PPC), which is associated with different sensory inputs, including somatosensory, visual, and auditory information. The occipital lobe is associated with visual processing, and the temporal lobes are associated with auditory and memory processing. Each of these areas produces a different type of detectable EEG response applicable to the development of cutting-edge techniques in brain–computer interfaces (BCIs) and neuromodulation protocols (Wolpaw, J., & Wolpaw, E. W. Eds. 2012). In particular, EEG responses are typically used for various purposes, including controlling devices (e.g., a prosthetic arm), providing communication channels for patients lacking voluntary muscle control, and providing biomarkers for diagnostic applications and biofeedback for rehabilitation and treatment strategies. In human brain science, three principal anatomical planes are considered to describe the brain’s anatomy, including the sagittal (longitudinal anteroposterior), coronal (vertical frontal or lateral), and transverse (axial or horizontal) planes. Typically, in discussions of animal neuroanatomy, such as that of rodents, the sections of the brain are named homologously to human brain sections. Figure 1.1 illustrates the major divisions of the human cortex in two views, transverse (top) and sagittal (bottom). This figure has also shown the four major lobes—frontal, parietal, occipital, and temporal—as well as several cortical functions

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Fig. 1.1  Major divisions of the human cerebral cortex in two main views of transverse (top) and sagittal (bottom). The four major lobes of frontal, parietal, occipital, and temporal as well as several cortical functionalities (e.g., primary auditory cortex, primary visual cortex) also are shown. (Adapted from Kandel et al. (1991) (Kandel et al. 2000))

1.3  Brain Rhythms EEG signals are typically described in terms of transient and oscillatory activities. Transient EEG features include sleep spindles, various components of event-related potentials (e.g., P200, P300, N100, and N200), and spikes corresponding to certain clinical conditions (e.g., seizures). Oscillatory activities are considered with respect to oscillatory frequency and are primarily divided into the delta, theta, alpha, beta, and gamma bands as explained below:

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Fig. 1.2  Five major EEG oscillatory activities of delta, theta, alpha, beta, and gamma (from bottom to top) over 3-second segment

• Delta oscillations span frequencies up to 4 Hz and are normally associated with adult slow-wave sleep (Amzica and Steriade 1998) and attention-demanding tasks (Kirmizi-Alsan et  al. 2006). Delta is also seen in infants’ EEGs (Korotchikova et al. 2009). Typically, children’s delta activity is greatest in the posterior cortical regions, while adult delta is strongest in the frontal regions. • Theta oscillations span the 4–8 Hz range and can be seen in drowsiness or arousal (Daniel 1967), conflict error (Cohen and Donner 2013; van Driel et al. 2012), and mental workload (Käthner et al. 2014). Theta has also been associated with relaxation and creative states. • Hans Berger named the alpha frequency band, which is associated with oscillations which span the 8–12 Hz range. This rhythmic activity largely is observed in the posterior regions during meditation, relaxation, and with closed eyes, while it is suppressed during mental tasks. A sensorimotor task-related Mu rhythm in this same frequency range also may be observed in sensory and motor cortical regions. • Beta spans the 12–30 Hz range, and while it is strongly associated with motor tasks (Pfurtscheller et al. 1997), it also is seen during alert and anxious states (Kamiński et al. 2012). • Gamma oscillations are 30 Hz or faster and are associated with a wide range of cognitive and motor functions (Fitzgibbon et  al.  2004; Tallon-Baudry 2009; MacKay 1997). These frequency bands have applications in various clinical conditions including neurodegenerative diseases (e.g., Parkinson’s disease (PD)) (Weinberger et al. 2006; Heinrichs-Graham et al. 2014), neuro-psychiatric conditions (e.g., schizophrenia) (Kwon et al. 1999; Gotlib 1998), and trauma and brain injuries (Roche et al. 2004). Figure 1.2 illustrates the aforementioned five major EEG oscillatory activities over 3-second segments.

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1.4  EEG Data Acquisition 1.4.1  EEG Sensors The recorded scalp activity can be detected through EEG sensors (electrodes) that send relatively small recorded signals to an amplifier for amplification. While EEG electrodes can be made of various metals, the most common types are gold or Ag/ Ag-Cl. Electrodes may be dry (gel-free) or wet (used with additional conductive material such as gel). Typically, dry electrodes use spiky contacts to minimize interface with the hair and outer skin. However, wet electrodes are considered the gold standard. A conducting electrolyte gel or paste is placed between the wet electrode and the skin to reduce skin–electrode impedance and thereby allow efficient current transduction. Although impedance is less than 5 KΩ ideally, impedance between 5 and 20 KΩ is considered acceptable (Nunez and Srinivasan 2006). One common problem with wet electrodes is that impedance deteriorates as the gel dries gradually, which makes these electrodes unsuitable for long-term use (Gargiulo et al. 2010). Electrodes also may be active or passive. Passive electrodes connect the metal disk to the amplifier directly through a wire. As EEG signals are of relatively low amplitude, environmental factors, including movement and electromagnetic noise, can affect signal quality. Therefore, electrode locations may be rubbed with an abrasive paste to remove the outer layer of the skin, which reduces the signal quality. Active electrodes contain a built-in preamplifier that increases the signal’s gain and signal-to-noise ratio (SNR). While these electrodes reduce possible environmental issues, they can amplify unwanted factors, such as input impedance or facial artifacts. Figure 1.3 shows an electrode cap on which active electrodes are mounted and an experimenter injecting conductive gel prior to a recording.

1.4.2  EEG Electrode Placement Standard electrode montages use the 10–20, 10–10, and 10–5 international systems for EEG electrode positions. The most common landmarking methods are based on the bony parts of the skull beginning from the nasion (Nz) to inion (Iz) and left to right preauricular points (LPA and RPA) to determine the electrodes’ placement on top of the head. The “20,” “10,” and “5” refer to interelectrode intervals 20%, 10%, or 5% of the total nasion–inion or left–right span of the head. The smaller the interelectrode interval, the higher the system’s resolution. The standard 10–20 system consists of 21 electrodes, while the 10-10 system consists of 74 electrodes, and the 10-5 system consists of 142 electrodes (Oostenveld and Praamstra 2001). Each electrode name is a combination of letter and number that refers to a specific anatomical location (“Fp,” frontal pole; “F,” frontal; “T,” temporal; “C,” central; “P,” parietal; and “O,” occipital). The subscript “z” stands for zero for the midline

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Fig. 1.3  (Right) An electrode cap with active electrodes mounted. (Left) The experimenter is injecting conductive gel prior to the recording

electrodes. Even numbers refer to the electrode positions in the right hemisphere and odd numbers to those in the left hemisphere. Smaller numbers are closer to the midline zero (“z”), and larger numbers represent more lateral electrodes. Figure 1.4 demonstrates the montages for the international 10–20 system as well as the extended 10–20 system.

1.4.3  Amplifiers Hans Berger’s initial human EEG recording used sensors and galvanometers that reside in museums now. However, the brain’s potential today is detected using advanced amplifiers attached to fast computers for storage and analysis. EEG signals are relatively small (e.g., ~20 μv) and, therefore, must be amplified before any further processing. EEG amplifiers are differential amplifiers with two input terminals that output the amplified version of the voltage difference between the input terminals. Thus, EEG amplifiers measure the potential difference and attenuate common signals that appear at both input terminals. As EEG has a very low amplitude, it is usually contaminated with electromagnetic interference from nearby instruments and power lines. The output of a real differential amplifier is defined as below:



Vout = Ad (V+ − V− ) +

1 Acm (V+ + V− ) 2

(1.1)

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10%

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Fig. 1.4  International 10–20 system electrode placement on a 3-D head from two views of top (top left) and side (top right). The bottom figure shows 10–20 system (red electrodes) and the extended system (white electrodes) on a 2-D plot

where Vout is the output voltage, V+ and V− are the amplifier’s two inputs, Acm is the common-mode gain, and Ad is the differential gain, respectively. EEG differential amplifiers have a high common-mode rejection ratio (CMRR) that amplifies the potential of interest and attenuates the interference from non-­ cerebral sources that appear simultaneously on both input terminals. Typically, the amplification factor is between 103 and 105, which results in a CMRR that ranges from 60 to 110 dB (Oostenveld and Praamstra 2001). All EEG recordings measure the difference in potentials between two signals. Indeed, the output voltage (Vout) of an EEG amplifier with two inputs (V+ and V−) is the algebraic sum of the difference between two inputs minus the references

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Fig. 1.5  Two main types of bipolar (top) and monopolar (bottom) recordings. For the bipolar recording, the differential potential of two channels (Fz and Cz) is the input of the amplifier to make one channel (Fz-Cz). For the monopolar recording, the differential potential of one channel (Fz) and the ear reference is the input of the amplifier to make one channel (Fz)

(V+ − Vref) - (V− − Vref). Conventional EEG recordings can be monitored either with monopolar or bipolar recording. In monopolar recordings, the electrode potential is measured with respect to a common reference electrode that is distant from the recording electrodes. Usually, this reference electrode is placed either on a mastoid or an earlobe for monopolar recording. Bipolar recordings use the difference between two electrode potentials to generate a recording channel. While bipolar recordings are less sensitive to common artifacts, they are more sensitive to localized brain activity (Oostenveld and Praamstra 2001)—this is the reverse for monopolar recording. However, as all channels in monopolar recordings have a common reference, further processing to make any montage desired can be achieved easily. Figure 1.5 shows a typical montage for two types of bipolar and monopolar recordings.

1.4.4  Digitization Most amplifiers have an analog-to-digital converter (ADC) to digitize analog signals necessary for computer-based processing and storage. An ADC block in the amplifiers discretizes both amplitude and time and converts them into a series of numerical values. The sampling rate, expressed typically in Hz, or samples per

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second, indicates the frequency at which the data from the electrode is sampled in time. That is, a sampling rate of 256  Hz records 256 data points per second. According to the Nyquist criterion, to be able to fully reconstruct the information of interest from a sampled signal, the sampling rate should be at least twice the highest frequency of interest in the original signal. If the sampling rate does not meet the Nyquist criterion and the signal contains frequency components higher than half of the sampling rate, then aliasing will happen which will distort the digital signal. Under-sampling (aliasing) can lead to loss of information from the data that makes it impossible to fully reconstruct the signal when it is converted back to analog form. Thus, because of possible practical issues in anti-aliasing filters, the sampling rate should be several times higher than the highest frequency of interest. However, this is a trade-off, as higher sampling rates require greater data storage space. The quantization block, which converts the analog amplitude into discrete form, is another important aspect of the digitization process. Binary bits are used to determine the quantization level, with 2k possible values with k bits. Therefore, ADC amplitude resolution depends on the number of bits that represent the digital signal amplitudes. Most ADC blocks digitize signals using 16 bit (= 216 = 65,536 levels), 24 bit (= 224 = 16.8 million levels), or 32 bit (=232 = 4.3 billion levels). Depending on the input voltage range and the number of bits, the ADC amplitude resolution is obtained as below:



Vres =

Vrange 2N

(1.2)

where Vres, Vrange, and N are the resolution, input range, and number of bits, respectively. For example, a 16-bit amplifier with an input voltage range of ±100  mV (range of 200  mV) has a 3  μV (=200  mV/216) resolution. This indicates that an amplifier with a 16-bit ADC can detect a signal as small as 3 μV for an input voltage of ±100 mV. However, as with increased sampling rates, higher resolution quantization requires more digital storage space.

1.4.5  Temporal Filtering Because biological signals typically contain a large range of frequency components, generally they must be filtered to extract the desired activities. Filtering can remove certain unwanted activities including biological artifacts (e.g., electromyogram (EMG)), electrode-related noise (e.g., motion), and electromagnetic interference (e.g., mobile phones). Analog and digital filters establish the frequency components of the signal. Analog filters are implemented prior to the digitization block, and digital filters are implemented after digitization. While digital filters have no effect on source signals, analog filters do, and thus, the original unfiltered signal is no longer accessible. Anti-aliasing analog filters are required to avoid aliasing, which digital filters cannot accomplish, as aliasing occurs at the digital processing block.

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Thus, to ensure that there is no frequency component above the Nyquist rate, anti-­ aliasing filters with a cutoff frequency equivalent to half of the sampling frequency (Nyquist rate) are applied to analog signals. Most filters are described with respect to three main parameters: filter order, phase, and cutoff frequency. Filter order refers to the length of the filter, which determines its roll-off properties, i.e., the slope of the magnitude response in the transition bands. Sharp filters have narrow transition bands and steep roll-off with a longer response than do filters with a wide transition band. Filter phase refers to the frequency-dependent time displacement that causes delay at a particular frequency component. Group delay, which refers to general envelope delay, is among the filtering-­related parameters important in EEG processing and results into two main classes, linear phase and nonlinear phase. The linear phase introduces a constant delay across all frequency components, while the nonlinear phase causes different delays at different frequency bands. The delay caused by linear phase filters can be corrected by filtering the filter output a second time in a backward direction. Typically, in broadband EEG components, nonlinear phase filters are undesirable, as they can distort the signal’s temporal shape completely. The cutoff frequency, the frequency at which the signal is attenuated by 3-dB, refers to the transition frequency that separates the filter’s passband and stopband. Depending on the filter type and frequency band of interest, the cutoff frequency should be accurately determined to pass the desired activities while blocking those unwanted. Four main types of filters include low pass (pass the low-frequency components), high pass (pass the high-frequency components), band pass (pass frequency components in a specific frequency range), and band stop (attenuate specific frequency components). Both band-pass and band-stop filters combine high- and low-pass filters to achieve the frequency range of interest.

1.5  Artifacts EEG amplitude is small and, therefore, the signal is highly vulnerable to artifacts. The two primary artifact categories that affect EEG are biological (originating in the subject but from outside the brain) and nonbiological artifacts. Biological artifacts may include eye blinks, electrooculogram (EOG), EMG, and respiratory artifacts. Eye blink results from fast eyelid movement that generates changes in the dipole charge, which usually is observed as a strong, sharp deflection in the frontal EEG channels. EOG (ocular) also results from eye movement and can have symmetric or nonsymmetric polarity across channels, depending on whether the movement is vertical or horizontal. EOG artifacts are most profound in the frontal and frontal–temporal regions. Muscular artifacts (i.e., EMG) are caused typically by facial muscular (e.g., jaw or eyebrow) movements. EMG artifacts are most prominent at the temporal, frontal, and occipital peripheries. While EOG and eye blink artifacts have low-­ frequency spectral components (~1 Hz) largely and, therefore, are easier to remove, EMG artifacts have a broad frequency distribution and often are difficult to eliminate.

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Fig. 1.6  Examples of EEG contaminated with various noises (eye blink, EMG, nearby instrument, and EMG) over a 3-second segment

Nonbiological artifacts may include electromagnetic interference from nearby instruments and electrical power lines which use sinusoidal voltages with a frequency of 50 Hz (in Europe, Asia, Africa, and South America), or 60 Hz in North America, and electrode movement. Figure 1.6 shows examples of EEG contaminated with various biological (eye blink, EMG) and nonbiological (line noise, nearby instruments) artifacts over a 3-second segment. Methods used commonly to remove artifacts include band-pass filtering, manual artifact rejection, and source decomposition techniques, such as independent component analysis (ICA) and principal component analysis (PCA). Band-stop (notch) filters that remove 58–62 Hz signals also may be used to remove power line interference if needed. Each type of artifact can affect the EEG signals differently in different frequency bands, and different processing methods are used depending on the frequency of interest. For example, if we are interested in EEG frequencies below 30 Hz, widespread EMG artifacts are more important to correct than 50 or 60 Hz line noise. Although using source decomposition methods, such as ICA and PCA, can be beneficial in many cases, selecting the optimal number of artifactual components can be challenging. Generally, rejecting contaminated EEG segments results in loss of data, and losing considerable information can damage the results and data interpretation. Thus, selecting proper artifactual components needs to be carefully investigated to minimize damage to the contents of the data.

1.6  Spatial Filtering Considering EEG’s low SNR, spatial filtering methods can improve source localization and increase SNR by making a particular channel more sensitive to certain sources and less sensitive to others (Oostenveld and Praamstra 2001). Typically, spatial filters use linear combinations of weighted channels with predefined geometrical patterns. Common spatial filters include common average reference (CAR) and surface Laplacian (small and large) filters (McFarland et al. 1997).

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A CAR filter is implemented by subtracting the average activity across all digitized channels from each individual channel of interest as below: n



Vi CAR = Vi ER −

∑ V jER j =1

n

(1.3)



where Vi ER is the potential difference between the ith electrode and the reference and n is the number of electrodes in the montage. Typically, CAR filtering is used to reduce the effect of global artifacts that appear across all channels, such as line noise. A surface Laplacian spatial filter is approximated by the second derivative of spatial voltage distribution, which is equivalent to subtracting the weighted sum of channels within a fixed distance from the channel of interest, as below:

Vi LAP = Vi ER − ∑ gij V jER j∈Si



(1.4)

where gij = 1 / dij / Σ j∈Si 1 / dij . In this equation, Si is the set of surrounding electrodes within the predetermined fixed distance from the electrode of interest (ith), and dij is the distance between the ith and jth electrodes, j ∈ Si. Depending on the type of Laplacian filter (small or large), the electrode set Si is defined as the set of nearest neighbor electrodes or next nearest neighbor electrodes for small and large Laplacian filters, respectively. The radius for small Laplacian is dij