Intracranial EEG: A Guide for Cognitive Neuroscientists

Foreword
Introduction: What Can You Expect in This Book?
Contents
Contributors
Part I Clinical Background, General Questions and Practical Considerations
1 How Are Patients Selected for Intracranial EEG Recordings?
	1.1 Prologue
	1.2 Who is a Candidate for a Presurgical Epilepsy Work-Up?
	1.3 Different Methodological Approaches to Intracranial EEG
	1.4 Conclusion
	1.5 Illustrative Case Scenarios
	References
2 Which is the Cognitive Status of Patients with Epilepsy Undergoing Intracranial Presurgical Studies, and How is This Affected by Antiepileptic Drugs?
	2.1 Introduction
	2.2 Cognitive Domains Frequently Affected in Presurgical Patients with Epilepsy
		2.2.1 Memory
		2.2.2 Language/Naming
		2.2.3 Executive Functions
		2.2.4 Attention
		2.2.5 Intelligence
	2.3 Variables Affecting the Cognitive Status in Presurgical Patients with Epilepsy
		2.3.1 Patient Variables
		2.3.2 Iatrogenic Effects
	2.4 Conclusion
	References
3 (How) Does Epileptic Activity Influence Cognitive Functioning?
	3.1 Introduction
	3.2 A Quick Look at Cognitive Networks
	3.3 Transient Disruption of Network Functions
	3.4 Chronic Remodeling of Neuronal Circuits
	3.5 Directions of Future Practice and Research
	References
4 Which Practical Issues Should I Consider When Planning and Conducting an iEEG Study?
	4.1 Study Design
	4.2 Task Design
	4.3 Patient Handling
	4.4 Technical Setup
	4.5 Event Synchronization
	4.6 Data Quality
	4.7 Conclusions
5 What Are the Practical Considerations for Building a Successful Intracranial EEG and Direct Brain Stimulation Research Program?
	5.1 Introduction
	5.2 Preparing Experiments for the Clinical Environment
	5.3 Interactions with Clinicians and Other Stakeholders
	5.4 Interactions with Patients and Their Families
	5.5 Interactions with the Nursing Staff
	5.6 Interactions with EEG Technicians
	5.7 Practical Considerations for Direct Brain Stimulation Studies
	5.8 Career Pathways to Starting a Successful iEEG Research Program
	5.9 Conclusion
	References
6 What Ethical Issues Need to Be Considered When Doing Research with Patients Undergoing Invasive Electrode Implantation?
	6.1 Introduction
	6.2 Risks and Benefits
		6.2.1 What Matters When Assessing Risks?
		6.2.2 What Matters When Assessing Benefits?
		6.2.3 What Is a Reasonable Risk/Benefit Ratio?
	6.3 Consent
		6.3.1 What Do Patients Understand?
		6.3.2 When Is Participation Voluntary?
		6.3.3 What Is the Impact of Clinical and Research Overlap?
		6.3.4 What Consent Practices Should Be Considered?
	6.4 Justice
		6.4.1 What Are the Pressures on Fair Access?
		6.4.2 What Are the Worries About Exploitation?
	6.5 Future Social Considerations
		6.5.1 How Might Potential Uses of iEEG Research Impact Neurodiversity?
		6.5.2 How Might Resulting Technology Affect Identity and Agency?
	6.6 Conclusions
	References
7 Which Ethical Issues Need to Be Considered Related to Microwires or Utah Arrays?
	7.1 Risks and Safety
	7.2 Signal Integrity and Integrity in Signal Translation
	7.3 Neural Data Privacy
	7.4 Personal Identity and Agency
	7.5 Post-study Obligations
	7.6 Neurophilosophical Implications
	7.7 Conclusions
	References
8 What Is the Contribution of iEEG as Compared to Other Methods to Cognitive Neuroscience?
	8.1 Introduction
	8.2 The Methodological Advantages of iEEG
		8.2.1 High Spatial–Temporal Resolution
		8.2.2 High Signal-to-Noise Ratio
		8.2.3 High-Frequency Activity
		8.2.4 Direct Electrical Stimulation of the Human Brain
	8.3 Characterizing the Dynamic and Transformative Nature of Neural Representations
	8.4 The Role of Hippocampal Ripple Activities in Memory
	8.5 The Information Coding Scheme of Human Single-Neuronal Activities
	8.6 The Common and Different Neural Mechanisms Between Humans, Primates, and Rodents
	8.7 iEEG Based Brain-Computer Interface and Closed-Loop Brain Stimulation
	8.8 Summary and a Practical Guidance
	References
9 How Many Data Do I Need for an iEEG Study? Treasure Maps and the Status of Variability
	9.1 Any Project Starts with a First Patient
	9.2 Confounding Factors
	9.3 When One Is Enough
	9.4 The Longer, the Better
	9.5 Naturalistic Studies: So Hard to Replicate
	9.6 How Many Patients Constitute an iEEG Study?
	9.7 Multiple-Case Studies: The More, the Merrier
	9.8 Towards Large Multicentric iEEG Databases
	9.9 A Final Word on Case-Reports
	9.10 Conclusion
	References
10 How Can iEEG Be Used to Study Inter-Individual and Developmental Differences?
	10.1 Introduction
	10.2 Minimize Inter-Individual Variability in Study Design and Analysis
	10.3 Define the Inter-Individual Factor(s) of Interest
	10.4 Understand (and Increase) the Sample Size
	10.5 Discussion
	References
11 Is IEEG-Based Cognitive Neuroscience Research Clinically Relevant? Examination of Three “Neuromemes”
	11.1 Introduction
	11.2 IEEG Research: A Cocoon for Neuromemes
	11.3 Dispelling the Myth of the Eloquent vs the Silent Cortex
	11.4 Eliminating the Implicit Dogma of “Localisationism”
	11.5 Updating and Validating the “Nociferous Cortex” Concept
	11.6 Conclusion
	References
Part II Physiological Basis and Functional Role of Intracranial EEG Signals
12 What Are the Advantages and Challenges of Simultaneous Scalp EEG and Intracranial EEG Data Recording?
	12.1 Technical Aspects and Challenges
	12.2 Methodological Aspects and Challenges
	12.3 Applications in Cognitive Neurosciences and Advantages
	12.4 Conclusions
	References
13 What Are the Promises and Challenges of Simultaneous MEG and Intracranial Recordings?
	13.1 Introduction and Motivation
	13.2 Setting up a Simultaneous Recording
		13.2.1 General Considerations
		13.2.2 Patient Selection
		13.2.3 Patient Preparation at the Time of the MEG Examination
		13.2.4 Protocols with the iEEG Patient in the MEG
	13.3 What Do Simultaneous Recordings Reveal?
		13.3.1 Methodological Approaches
		13.3.2 Precision of Localization
		13.3.3 Epileptic Discharges
		13.3.4 Cognitive Potentials and Oscillations
	13.4 Discussion and Future Avenues
	References
14 Why and How Should I Track Eye-Movements During iEEG Recordings?
	14.1 Anatomy and Activity of the Human Eye
	14.2 The Neural Correlates of Ocular Activity
		14.2.1 A Note on the Electro-Muscular Correlates of Oculomotor Activity
	14.3 The Functional Roles of Eye Movements for Brain Function and Behaviour
		14.3.1 Eye Movements Map Visual Space
		14.3.2 Eye Movements Align Brain Rhythms
		14.3.3 The Role of Eye Movements in Various Psychological Constructs
	14.4 Accounting for Ocular Activity in Cognitive Neuroscience Research
	14.5 A “How-To” of Eye Tracking and Intracranial EEG
		14.5.1 Recording Ocular Activity in the Clinic
		14.5.2 Detecting Ocular Events
		14.5.3 Saccade-/Fixation-Locked Analysis
		14.5.4 Encoding Models
		14.5.5 “Eye Movements-As-Covariates” Analysis
		14.5.6 Eye Movement Artefacts in Intracranial EEG
	14.6 Conclusion
	References
15 How Can I Combine Data from fMRI, EEG, and Intracranial EEG?
	15.1 Introduction
	15.2 How Can I Combine Data from fMRI and Intracranial EEG?
		15.2.1 Combining Univariate Signal in fMRI and iEEG
		15.2.2 Decoding with fMRI and iEEG
		15.2.3 Neural Connectivity Within fMRI and iEEG
	15.3 How Can I Combine Data from EEG and Intracranial EEG?
		15.3.1 ERP Analysis Based on EEG and iEEG
		15.3.2 Neural Oscillations in EEG and iEEG
		15.3.3 Combining Neural Connectivity in EEG and iEEG
	15.4 Discussion
	References
16 What is the Relationship Between Scalp EEG, Intracranial EEG, and Microelectrode Activities?
	16.1 Introduction
	16.2 Cellular Origin of iEEG, LFP and Scalp EEG
	16.3 Relationship Between Thalamic LFP and Scalp EEG
	16.4 Relationship Between Subthalamic iEEG and Scalp EEG
	16.5 Relationship Between Neuronal Firing, Local Field Potentials and Hemodynamic Activity in the Amygdala
	16.6 Relationship Between Scalp EEG, Hippocampal iEEG, and Single Neuron Firing
		16.6.1 Phase Locking Value Between Hippocampal iEEG and Scalp EEG
		16.6.2 Information Flow Between iEEG from Hippocampus and Auditory Cortex
		16.6.3 Neuronal Firing Patterns During the Trial
		16.6.4 Neural Communication in Working Memory
	16.7 Volume Conduction
	16.8 Conclusions
	References
17 How Do Local Field Potentials Measured with Microelectrodes Differ from iEEG Activity?
	17.1 Spatial Spread
	17.2 Stimulus Tuning Preferences of LFP and iEEG
	17.3 iEEG as an Average of LFPs
	References
18 What Do ECoG Recordings Tell Us About Intracortical Action Potentials?
	18.1 Introduction
	18.2 Microscale Surface Electrodes with Single-Cell Resolution
	18.3 Mesoscale Surface Electrodes for Network-Wide Coverage
	18.4 Opportunities and Challenges
	References
19 What is the Functional Role of iEEG Oscillations in Neural Processing and Cognitive Functions?
	19.1 Introduction
	19.2 iEEG Oscillations Supporting Cognitive Functions
	19.3 Speech Perception and Production
	19.4 Dysfunctional iEEG Oscillations
	19.5 Conclusion
	References
20 How Can I Run Sleep and Anesthesia Studies with Intracranial EEG?
	20.1 Introduction
	20.2 The Clinical Context for Sleep and Anesthesia Studies
		20.2.1 The Peri- and Post-operative Clinical Setting
		20.2.2 Factors Determining Sleep Quality in the Monitoring Unit
		20.2.3 The Effects of Antiepileptic Drugs
		20.2.4 Electrode Explantation as a Window into the Neural Correlates of Anesthesia
	20.3 Implications of Epilepsy as the Underlying Neurological Disorder
		20.3.1 Sleep Stages and Epileptic Activity
		20.3.2 Sleep Deprivation is a Powerful Trigger for Seizures
		20.3.3 The Relationship of Anesthesia and Epileptic Activity
	20.4 Analysis Strategies
		20.4.1 Technical Pre-requisites for Comparative Electrophysiology
		20.4.2 How to Determine the Current Behavioral or Brain State?
		20.4.3 How to Address Epileptic Activity?
	20.5 Insights into Sleep and Anesthesia
		20.5.1 The Human Memory Network During Sleep
		20.5.2 The Brain Under Anesthesia
		20.5.3 Comparative Electrophysiology of Sleep and Anesthesia
	20.6 Conclusions
	References
21 What Can iEEG Inform Us About Mechanisms of Spontaneous Behavior?
	21.1 The Fundamental Importance and Characteristics of Free Behavior
		21.1.1 Heterogeneity
		21.1.2 Spontaneity
		21.1.3 Personality Constraints
		21.1.4 Boundary Setting
		21.1.5 Pre-conscious Preparation
	21.2 Spontaneous (Resting State) Fluctuations: A Plausible Neuronal Generator of Free Behaviors
	21.3 iEEG Reveals the Neuronal Basis and Precise Dynamics of  Spontaneous Fluctuations
	21.4 The Role of Spontaneous Fluctuations in Free Behavior: An iEEG Study of Free Visual Recall
	21.5 Integration of Memory and Vision: Hippocampal Ripples Anticipating Recollection
	21.6 Ripple-Mediated Cortico-Hippocampal Dialogue During Free Recall
	21.7 Evidence for Recurrent Rather Than Unidirectional Information Flow
	21.8 Boundary Setting
	21.9 In Summary
	References
22 How Can We Differentiate Narrow-Band Oscillations from Aperiodic Activity?
	22.1 Introduction
		22.1.1 Narrowband Oscillations/Periodic Activity
		22.1.2 Broadband/Aperiodic Activity
		22.1.3 Overlap of Periodic & Aperiodic Components
	22.2 Analysis Methods
		22.2.1 Conventional Approaches for Analyzing Neural Time Series
		22.2.2 Methods for Dissociating Periodic & Aperiodic Activity
		22.2.3 Methodological Considerations
	22.3 Existing Studies Separating Periodic & Aperiodic Activity
	22.4 Discussion
		22.4.1 Interpretations of Periodic & Aperiodic Components
		22.4.2 Future Work
		22.4.3 Conclusion
	References
23 How Can We Detect and Analyze Navigation-Related Low-Frequency Oscillations in Human Invasive Recordings?
	23.1 Introduction
	23.2 A Practical Guide for Detecting Oscillations from Human Hippocampal Recordings During a Navigation Task
	23.3 Convergence: Detecting Navigation-Related Oscillations Using Other Available Methods
	23.4 What Do We Mean by “Oscillation?”
	23.5 Conclusions
	References
24 How Can I Disentangle Physiological and Pathological High-Frequency Oscillations?
	24.1 Introduction
	24.2 Lessons Learnt for HFO Research from Human Microelectrode Studies
	24.3 Why Is It Important to Separate Physiological from Pathological HFOs
	24.4 Approaches to Separate Pathological from Physiologic HFOs
	24.5 Building an Atlas of HFO Normative Rates and Its Use to Improve the Yield of Identification of Epileptic Tissue
	24.6 Evoked Responses Are Useful to Separate Physiological from Pathological HFOs
	24.7 Separating Physiological from Pathological HFOs Using Coupling to Sleep Features
	24.8 Conclusion and Outlook
	References
25 Which Rhythms Reflect Bottom-Up and Top-Down Processing?
	25.1 Introduction—Historical Overview
	25.2 Methodological Summary
	25.3 Top-Down Versus Bottom-Up Processing, and Cortical Hierarchy
	25.4 Frequencies of Neuronal Communication
	25.5 Part 1: Cognitive Operationalization
	25.6 Part 2: Laminar Studies
	25.7 Part 3: Inter-Areal Interactions
	25.8 Part 4: Causal Manipulation Studies
	25.9 Conclusion and Future Directions
	References
26 How Can We Study the Mechanisms of Memory-Related Oscillations Using Multimodal in Vivo and in Vitro Approaches?
	26.1 The Heredity of Brain Activity―Historical Approaches
	26.2 High-Throughput Sequencing Technologies: RNA Sequencing
	26.3 High-Throughput Sequencing Technologies: ATAC Sequencing
	26.4 High-Throughput Sequencing Technologies: Special Considerations
	26.5 The Genes Underlying Memory-Associated Oscillations
	26.6 Studying the Human Brain in Vitro
	26.7 Organotypic Brain Slice Culture
	26.8 Viruses and Gene Manipulation
	26.9 Optogenetics
	26.10 Electrophysiology
	26.11 Conclusion
	References
Part III Data Analysis
27 How Can I Integrate iEEG Recordings with Patients’ Brain Anatomy?
	27.1 Introduction
	27.2 Anatomical Images
		27.2.1 Determining the Coordinate System of the Anatomical Images
		27.2.2 Aligning Anatomical Images to a Standard Coordinate System
		27.2.3 Using FreeSurfer for Extracting Cortical Surfaces
		27.2.4 Coregistering the Anatomical Images
	27.3 Electrodes
		27.3.1 Localizing Electrodes in the Anatomical Image
		27.3.2 Compensating for Electrode Displacement due to Brain Shift
		27.3.3 Registering Electrodes to an Anatomical Template and Atlas
	27.4 Electrophysiological Recordings
		27.4.1 Preprocessing the Electrophysiological Recordings
		27.4.2 Re-referencing and Subsequent Analysis
	27.5 Discussion
	References
28 How Should I Re-reference My Intracranial EEG Data?
	28.1 Why Do We Need a Reference?
	28.2 How Can We Re-reference iEEG Data?
		28.2.1 Monopolar Reference
		28.2.2 Bipolar Reference
		28.2.3 Laplacian Reference
		28.2.4 Common Average Reference (CAR) and Median Reference
		28.2.5 Gram-Schmidt Orthogonalization
		28.2.6 White Matter Reference
		28.2.7 Independent Component Analysis
		28.2.8 Spatio-spectral Decomposition and Tailored Spatial Filtering Approaches
	28.3 What Does Re-referencing Do to My Data? An Empirical Comparison Between Different Re-referencing Schemes
		28.3.1 What Is Left in Your Data After Re-referencing?
		28.3.2 What Is Your Data Telling You After Re-referencing?
	28.4 Discussion of Results
	References
29 What Are the Pros and Cons of ROI Versus Whole-Brain Analysis of iEEG Data?
	29.1 Introduction
	29.2 Steps Before Electrode Selection
	29.3 Regions of Interest Analysis
		29.3.1 Anatomical ROI Definition
		29.3.2 Functional ROI Definition
		29.3.3 How Can I Handle a Different Number of Electrodes in the ROI for Each Patient?
		29.3.4 What Are the Advantages and Disadvantages of ROI Selection?
	29.4 Whole-Brain Analyses
		29.4.1 Electrode-Level Whole-Brain Analyses
		29.4.2 Group-Level Whole-Brain Analyses
		29.4.3 What Are the Advantages and Disadvantages of Whole-Brain Analyses?
	29.5 Summary
	References
30 How to Detect and Analyze Traveling Waves in Human Intracranial EEG Oscillations?
	30.1 Introduction
	30.2 Approach to Measure Traveling Waves of Neuronal Oscillations
		30.2.1 Identification of Oscillations and Clustering Algorithm
		30.2.2 Identification of Traveling Waves
	30.3 Features of Traveling Waves
	30.4 Discussion
	References
31 How Can I Investigate Perceptual and Cognitive Function Using Neural Frequency Tagging?
	31.1 Introduction
	31.2 Neural Frequency Tagging in Intracranial Electrocorticography (iEEG) Experiments
	31.3 How to Compute NFT
	31.4 How to Interpret NFT
		31.4.1 Advantage of Frequency-Domain Analyses
	31.5 Challenges and Pitfalls
	31.6 Promises of NFT and Future Directions
	References
32 How Can I Analyze Connectivity in iEEG Data?
	32.1 What is Connectivity, and Why Study It?
	32.2 IEEG-Based Connectivity Metrics
		32.2.1 Phase-Based Measures of Functional Connectivity
		32.2.2 Phase-Locking Value
		32.2.3 Coherence
		32.2.4 PLI, wPLI and the Question of Volume Conduction
	32.3 Alternative Metrics of iEEG Functional Connectivity
		32.3.1 Granger Causality
		32.3.2 Power Correlations
	32.4 Statistical Frameworks for Analyzing iEEG Connectivity
	32.5 Interpreting iEEG Connectivity (and Next Steps for the Field)
	References
33 How Can I Analyze Large-Scale Intrinsic Functional Networks with iEEG?
	33.1 Introduction
	33.2 Methodological Considerations
	33.3 Intrinsic Networks from Amplitude Coupling
	33.4 Intrinsic Networks from Phase Coupling
	33.5 Applications to the Study of Cognition
	33.6 Temporal Dynamics of Intrinsic Networks
	33.7 Conclusion
	References
34 What Do I Need to Consider for Multivariate Analysis of iEEG Data?
	34.1 The Multivariate Nature of iEEG Data Analysis
	34.2 Data Extraction
	34.3 Multivariate Analysis of iEEG Data via Similarity-Based Analyses and Classification
		34.3.1 Similarity-Based Analysis
		34.3.2 Multivariate Classification
	34.4 Summary
	References
35 How Can I Conduct Surrogate Analyses, and How Should I Shuffle?
	35.1 What is a Surrogate Analysis?
	35.2 When to Adopt a Surrogate Analysis?
		35.2.1 Not Normally Distributed Data
		35.2.2 Non-Matched Trial Numbers Between Conditions
		35.2.3 Correction for Multiple Comparisons
	35.3 How to Perform Surrogate Analyses?
		35.3.1 Switching Condition Labels
		35.3.2 Shuffling Condition Labels
		35.3.3 Shifting Permutation
		35.3.4 Procedure of the Surrogate Analysis
	35.4 Cluster-Based Permutation Analysis
		35.4.1 Criterions of Defining a Cluster
		35.4.2 Procedure of Performing the Cluster-Based Permutation Analysis
	35.5 How Many Permutations Are Appropriate?
	References
36 How Can iEEG Data Be Analyzed via Multi-Level Models?
	36.1 Introduction
	36.2 Linear Mixed Effect Model
	36.3 Understanding the Construction of LMEs from the Perspective of Multi-Level Linear Models
	36.4 Examples of LME with MATLAB
		36.4.1 Two-Level Model: Contacts that Are Nested in Patients
		36.4.2 Three-Level Model: Repeated Measurements Are Nested in Contacts that Are Nested in Patients
		36.4.3 Cross-Level Interactions Between Continuous and Categorical Independent Variables
	36.5 Discussion
	References
37 How Can I Avoid Circular Analysis (“Double Dipping”)?
	37.1 What Is Circular Analysis and Why Is It a Problem?
	37.2 Can Multivariate Analyses Be Circular?
	37.3 How Should I Select Data Points to Avoid Circularity?
	37.4 What if I Don’t Have a Specific Hypothesis?
	37.5 How Do I Ensure Independence Between Training and Test Set in Multivariate Analyses?
	37.6 Summary
	References
38 How Can Intracranial EEG Data Be Published in a Standardized Format?
	38.1 Introduction
	38.2 Study Design and Human Subjects
	38.3 From Source Data to Raw Data
		38.3.1 Creating the Overall Folder Structure
		38.3.2 Curating the iEEG Time Series Data and Channel Information
		38.3.3 Curating Electrode Positions
		38.3.4 Curating Task Information
		38.3.5 Derivatives
		38.3.6 Choosing a Publication Platform
	38.4 Discussion
	References
Part IV Advanced Topics
39 What Are the Contributions and Challenges of Direct Intracranial Electrical Stimulation in Human Cognitive Neuroscience?
	39.1 Introduction
	39.2 Challenges of DES Studies in the VOTC
	39.3 DES to Understand Human Face Identity Recognition
		39.3.1 Why Studying Face Identity Recognition with DES?
		39.3.2 DES of the Right Face-Selective IOG Impairs FIR
		39.3.3 DES of the Right Face-Selective Anterior Fusiform Gyrus Impairs FIR
		39.3.4 DES to the FFA: Subjective and Objective Effects
		39.3.5 What Can Be Learned from DES in Face-Selective VOTC Regions?
	39.4 Interpretations, Practical and Theoretical Considerations for Future DES Studies
		39.4.1 Bringing the Lab into the Clinical Room
		39.4.2 Group or Single Case Studies?
		39.4.3 A SEEG Advantage Over ECOG for DES?
		39.4.4 Functional Specificity of Local and Remote DES Effects
		39.4.5 Assessing the Connectivity of the Critical Sites
	References
40 How Can I Investigate Causal Brain Networks with iEEG?
	40.1 Introduction
	40.2 Types of Brain Connectivity
	40.3 History of CCEP
	40.4 Methods and Quantification of CCEP
		40.4.1 Eliciting and Recording CCEPs
		40.4.2 Design of CCEP Experiments
		40.4.3 Analysis of CCEPs
	40.5 Applications of CCEPs
		40.5.1 Investigate Inter- and Intra-Regional Connectivity of Functional Brain Networks
		40.5.2 Comparing CCEP Mapping to Other Non-invasive Connectivity Methods
		40.5.3 CCEP Mapping to Measure Pathophysiological Networks
		40.5.4 CCEP Mapping to Probe Brain Plasticity
	40.6 Mechanistic Basis of CCEPs
		40.6.1 Neurophysiology at Site of Stimulation
		40.6.2 Potential Propagation Pathways
		40.6.3 Electrophysiology Underlying the N1 and N2 of the CCEP
	40.7 Advanced Considerations
		40.7.1 Limitations and Caveats
	40.8 Future CCEP Mapping Approaches
	40.9 Conclusion
	References
41 What Are the Promises and Challenges of Closed-Loop Stimulation?
	41.1 Introduction
	41.2 Open-Loop Versus Closed-Loop
	41.3 Clinical Development of Closed-Loop Stimulation
	41.4 Promises and Challenges
		41.4.1 Causal Tests of the Neural Basis of Cognition
		41.4.2 Naturalistic Closed Loop
	41.5 Conclusion
	References
42 Which Are the Most Important Aspects of Microelectrode Implantation?
	42.1 Introduction
	42.2 Target Selection
		42.2.1 Technical “Pearls” for Electrode Planning
	42.3 Device and Methodology for Electrode Insertion
	42.4 Insertion of Electrodes
		42.4.1 Technical “Pearls” for Microelectrode Insertion
	42.5 Summary
	References
43 How Can We Process Microelectrode Data to Isolate Single Neurons in Humans?
	43.1 Introduction
		43.1.1 Epilepsy Patients
		43.1.2 Properties of Microwire Recordings
	43.2 Spike Sorting
		43.2.1 Preprocessing
		43.2.2 Spike Detection
		43.2.3 Spike Alignment
		43.2.4 Feature Extraction
		43.2.5 Clustering
		43.2.6 Quality Metrics
		43.2.7 A Practical Example
	References
44 How Is Single-Neuron Activity Related to LFP Oscillations?
	44.1 Introduction
	44.2 Analyzing the Relationship Between Spikes and LFPs
		44.2.1 Computing the Relationship Between Spiking and Spectral Power
		44.2.2 Computing the Relationship Between Spiking and Oscillatory Phase
	44.3 Relevance for Human Behavior and Cognition
		44.3.1 Spike-Power Associations During Human Cognition
		44.3.2 Spike-Phase Associations During Human Cognition
	44.4 Conclusion
	References
45 How Can We Use Simultaneous Microwire Recordings from Multiple Areas to Investigate Inter-Areal Interactions?
	45.1 Introduction
	45.2 Case Studies
	45.3 Current Methodological Approaches and Their Limitations
	45.4 Conclusions
	References
46 How Can Laminar Microelectrodes Contribute to Human Neurophysiology?
	46.1 Introduction
	46.2 Insights
		46.2.1 Oscillations
		46.2.2 Sleep Rhythms
		46.2.3 Wake Rhythms
		46.2.4 Cortical Physiology
	46.3 Challenges
		46.3.1 Referencing
		46.3.2 Recording Conditions
	46.4 Promises
		46.4.1 Macroelectrode-Laminar Correspondence
		46.4.2 Laminar Structure of Travelling Waves and Propagating IIDs
		46.4.3 Exotic (Non Somatic Action Potential) Waveform Physiology
		46.4.4 Validation of Extracranial Laminar Inference
	46.5 Conclusion
	References
47 How Does Artificial Intelligence Contribute to iEEG Research?
	47.1 AI-iEEG for Neuroscience
		47.1.1 Encoding Models of Perception and Cognition
		47.1.2 Decoding Models of Perception and Cognition
	47.2 AI-iEEG for Neurotechnology
		47.2.1 IEEG BCI for Speech and Communication
		47.2.2 IEEG BCI for Motor Control
		47.2.3 iEEG for Deep Brain Stimulation
	References
48 How Can I Identify Stimulus-Driven Neural Activity Patterns in Multi-Patient ECoG Data?
	48.1 Overview
		48.1.1 Why Is It Challenging to Identify Stimulus-Driven Brain Activity?
		48.1.2 How Can We Measure Neural ``Activity'' in the Human Brain?
		48.1.3 Building Explicit Stimulus Models
		48.1.4 What Are Some Modality-Specific Challenges to Identifying Stimulus-Driven Brain Activity from Intracranial Recordings?
	48.2 Identifying Stimulus-Driven Neural Activity
		48.2.1 Within-Participant Approaches
		48.2.2 Across-Participant Approaches
	48.3 Summary and Concluding Remarks
	References
49 How Can We Identify Electrophysiological iEEG Activities Associated with Cognitive Functions?
	49.1 Challenges of Mining Large-Scale Electrophysiology
	49.2 Manual and Automatic Detection of Signal Activities
	49.3 Electrophysiological Features of Neural Activities
	49.4 Applications for Investigating Memory and Cognition
	49.5 New Technologies and Future Directions
	References
50 How Can We Track Cognitive Representations with Deep Neural Networks and Intracranial EEG?
	50.1 Introduction
	50.2 Deep Neural Networks in Cognitive Neuroscience
	50.3 DNNs and iEEG: Insights from Memory Research
	50.4 Discussion
	References
51 How Can I Use Utah Arrays for Brain-Computer Interfaces?
	51.1 What Is a Brain-Computer Interface?
	51.2 Anatomy of the Utah Array
	51.3 Implantation
	51.4 Data Acquisition
	51.5 Decoder Loop
	51.6 Somesthetic Feedback
	51.7 Outlook
	References
52 Can Chronically Implanted iEEG Sense and Stimulation Devices Accelerate the Discovery of Neural Biomarkers?
	52.1 Introduction
	52.2 What Bidirectional, Chronically Implanted iEEG Devices Are Available?
	52.3 What Can Chronically Implanted iEEG Devices Provide That (Sub)acute iEEG Cannot?
	52.4 How Can We Discover Biomarkers Using Bidirectional iEEG Devices?
	52.5 What Are the Challenges in Using Chronically Implanted iEEG Devices?
	52.6 What Will Be Possible for Biomarker Discovery with the Next Generation of Chronically Implanted Bidirectional iEEG Devices?
	References
53 The Future of iEEG: What Are the Promises and Challenges of Mobile iEEG Recordings?
	53.1 Introduction
	53.2 Chronically Implanted Neural Sensing Devices
	53.3 Current Findings
	53.4 Technical Challenges
	53.5 Clinical Confounds
	53.6 Limited Sampling of Brain Regions
	53.7 Ethical Considerations
	53.8 Promises and Future Opportunities
	References