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