High-Resolution EEG Brain And Brain/Body Imaging
High-Resolution EEG Brain andBrain/Body ImagingNew Methods forSocial Neuroscience ResearchScott MakeigInstitute for Neural ComputationUniversity of California San DiegoElectrical Neuroimaging Workshopon the Dynamic Social BrainSan Diego, CANovember, 2016William BlakeS. Makeig (2016)
Swartz Center for Computational Neuroscience, th Anniversary SCCN Impromptu celebration 1/2/12
Embodied AgencyBrain processeshave evolved and functionto optimize the outcomesevaluationperception actionof the behaviorthe brain organizesin response toperceived challengesand opportunities.Brains meet the challenge ofthe moment!S. Makeig 2011
Embodied CognitionBoth our ‘abstract’ and our ‘aesthetic’cognition are built on and are grounded inour embodied experienceà as is our social experienceS . Makeig, 2007
Human Functional Brain ImagingSome human brain imaging milestones1926 1st human EEG recordingsEEG era19381st EEG spectral analysis1962 1st computer ERP averaging (CAT)ERP era19791st event-related desynchronization19931st fMRI BOLD recordingsfMRI era19931st broadband ERSP1995 1st multisource EEG filtering by ICA2009 1st commercial dry electrode EEG toysfEEEG / BMI / MoBI era S. S.MakeigMakeig,20102009
Three Modern Eras of EEG eLoo, Lenartowicz & Makeig, 2015gure 1. Relative number of PubMed citations retrieved by ‘All Fields’ search terms: ‘EEG,’ ‘ERP,’ and ‘Brascillations.’ The percent of citations for each search term relative to the total number of citations returney a search for any of the three terms is plotted relative to the other two search terms. For visual clarityFigure 1. Relative number of PubMed citations retrieved by ‘All Fields’ search terms: ‘EEG,’ ‘ERP,’ and ‘Brainrain Oscillations’ citations are graphed with a green dotted line according to the Y-axis labels on the righOscillations.’ The percent of citations for each search term relative to the total number of citations returned‘EEG’ with a blue solid line and ‘ERP’ with a red dashed line according to the Y-axis labels on the left.by a search for any of the three terms is plotted relative to the other two search terms. For visual clarity,254x104mm (72 x 72 DPI)‘Brain Oscillations’ citations are graphed with a green dotted line according to the Y-axis labels on the right;‘EEG’ with a blue solid line and ‘ERP’ with a red dashed line according to the Y-axis labels on the left.254x104mm (72 x 72 DPI)S. Makeig, 2016
EEG (scalp surface fields)LocalExtracellularFieldsECOG (larger corticalsurface fields)At each spatial recording scale, the signalis produced by active partial coherence ofdistributed activities at the next smallerspatial scale.Intracellular andperi-cellular fieldsSynaptic andother transmembranepotentialsBrain dynamics areinherently multi-scaleScott Makeig 2007
Brain dynamics areinherently multi-scaleLocalExtracellularFieldsCross-scale couplingis bi-directional!LargerSmallerEEG (scalp surface fields)ECOG (larger corticalsurface fields)At each spatial recording scale, the signalis produced by active partial coherence ofdistributed activities at the next smallerspatial scale.Synaptic andother transmembranepotentialsAny signal one recordsis not one unitary signal!Intracellular andperi-cellular fieldsElectrical effects do not allproceed from small tobig!Scott Makeig 2007
Brain dynamics areinherently multi-scaleEEG (scalp surface fields)ECOG (larger corticalsurface s-scale couplingis bi-directional!LargerAt each spatial recording scale, the signalis produced by active partial coherence ofdistributed activities at the next smallerspatial scale.Intracellular andperi-cellular fieldsSynaptic andother transmembranepotentialsSmallerScott Makeig 20 emotionEEG & EmotionJulie Onton & Scott Makeig, Frontiers in Human Neuroscience, 2009
EEG Dynamics of Emotion ImaginationSuggest the imaginativeexperience of 15emotions:§ after Helen Bonny§ relaxation§ alternate positive and negativeemotions§ relaxation between emotionepisodes§ obtained 1-5 min periods ofeyes-closed spontaneous EEG§ 33 subjectsOnton & Makeig, Frontiers in Human NeuroscienceS. Makeig, 200909
Independent ModulatorsOnton & Makeig, Frontiers in Human Neuroscience 09
Independent ModulatorsOnton & Makeig, Frontiers in Human Neuroscience 09
Onton & Makeig, Frontiers in Human Neuroscience 09
Independent ModulatorsOnton & Makeig, Frontiers in Human Neuroscience 09
Changes in distribution of broadband high-frequencyEEG power with imagined emotionJulie Onton & Scott Makeig, Frontiers in Human Neuroscience, 2009
Onton & Makeig, Frontiers in Human Neuroscience 09
LROnton & Makeig, Frontiers in Human Neuroscience 09
RT. Fritz, 2009fMRI BOLDLOnton & Makeig, 2009EEG HFBJulie Onton & Scott Makeig, Frontiers in Human Neuroscience, 2009
z -4z -3Mona Park et al., 2015fMRI BOLDOnton & Makeig 2009EEG HFBJulie Onton & Scott Makeig, Frontiers in Human Neuroscience, 2009
Embodied Agency & Social NeuroscienceS . Makeig, 2007
‘Theory of Heart’To discern & empathically experience thefeelings of another (and, thereby, know theirmotivation to act and to interact), we typicallyScott Makeig, 2015Francis Baconmust use quite subtle cues
We have many reasons to hide our feelings – but correctly sensingothers’ feelings is essential for free social interactions. Brain network dynamics supporting affective perception usingsubtle affective cues are still little explored. These same brain networks also support our aesthetic senses ofart, music, dance, etc.Scott Makeig, 2015
Measuring Emotional Engagementand CommunicationAttentionActionEngagementGrace Leslie & S Makeig, 2013
The Heart is aLonely Hunter (1968)Expressive gesturing taskTwo conditions:- Fully engaged- Less engagedConducting Experiment (2013)Modeling Musical EngagementG Leslie & S Makeig, 2011Grace Leslie & S Makeig, 2013
EEG Result: Full affective engagementFrequency (Hz)ΔRight temporalparietal junction(rTPJ)Swing Cycle (%)Grace Leslie & S Makeig, 2014
EEG Result: Full affective engagementΔFrequency (Hz)The TPJ controls representations of the self or of anotherindividual across a variety of low-level and high-level and sociocognitive processes (mentalizing, empathy, agency discrimination,visual perspective taking, imitation) Right temporal-parietal junction(rTPJ)The rTPJ is a key cortical structure for both motor and emotionalcontrol; rTPJ volume predicts level of emotional awareness ofothers in autistics; etc. Swing Cycle (%)Grace Leslie & S Makeig, 2014
Brain imaging during motor behavior? Nearly all brain imaging studies (MEG, PET, fMRI, andEEG) are conducted in rigidly static seated or pronepositions with only the most minimal finger movementsallowed.MEGfMRIEEGWhy?PET In all modalities but EEG, the sensors are heavy. Muscle and movements contribute ( noise ) signals. But this limitation is highly artificial. Nearly all our life involves activemovements and interactions within a 3-D environment. à Brain activity during free movement in 3-D spacehas never been observed or modeled!Scott Makeig 2008
Mobile Brain/Body Imaging (MoBI)1. Record simultaneously, during naturally motivated action & interaction,What the brain does(high-density EEG)What the brain experiences (sensory scene recording)What the brain organizes(body & eye movements,psychophysiology)2. Then –Use evolving machine learning methodsto find, model, and measurenon-stationary (context- and intention-related)functional relationships among these data modalities.Scott Makeig, 2011
MoBI Lab at SCCN, UCSDLab Streaming Layer (LSL) software framework forsynchronous multi-stream, multi-platform recordingand feedback – freely available on github.comXDF multimodal file format (github)SNAP environment for simple/complex exp. ccn-open-house/inc-sccn-open-house-hi-lite-reel
MoBILABa multimodal browserfor MoBI data analysisAlejandro Ojeda et al., 2011
MoBI Lab: Dart Game ExperimentImaging NaturalCognitionwith MoBIS Makeig & M Miyakoshi, 20112
Walking modulates midline(leg) sensorimotor rhythmsSMAWagner et al., Neuroimage, 2012
MoBI Juggling Experiment
The Audio Mazen of the hand position tracking and tight synchronization between datarating applications, after some period such auditory feedback can create anndfolded participants. That is, after some minutes of acclimatization, pilotg able to ‘feel’ the shape of walls while learning to navigate confidentlyonly the body-position dependent auditory feedback based on virtual wallently using these cues to ‘follow’ walls and ‘feel for’ maze doorways andI sparse AR audio maze experiment at SCCN. (Left) A participant freely exploring awhile 32-channel body markers and 128-channel EEG are recorded. (Center)ectory and wall-touch feedback points recorded while the participant attempted toGoal position indicated by a global landmark signal (a repeating buoy sound deliveredbehind the Goal location). The body motion track during the 12-min session is colore at the start to red at the goal. Wall touches, as signaled by (442) audible wallshort gray lines indicating the distance and angle of the reaching hand from the body.elated spectral perturbation (ERSP) analysis for an independent component localizeduivalent current dipole; bottom, ERSP image averaging log spectrograms of EEGh feedback onsets. In this component process, wall-touch feedback was followed bythe alpha and then the beta band, demonstrating our ability to resolve cortical events navigate the audio maze.ments at BIT will compare sparse-AR visual maze learning with learningin a rich VR condition in which the full VR scene will be continuouslyugh the head-mounted VR display. A key question will be whether or not theith spatial orientation in visual and audio sparse-AR experiments (e.g.,ehavioral navigation decisions, arriving at landmark locations, etc.) areost likely similarly reflect integration of location and orientation informationesentation.to the sensory cues defining the positions of maze walls, visual or auditoryented to participants to probe the integration of egocentric and allocentricels of the maze environment. Landmarks can be local or global. LocalFigure 3. Pilot data from a MoB5x5 grid ‘audio maze’ (center)J. Iversen, M. Miyakoshi, K. Gramann, S. Makeig, 2016
Development of Shared Attention –A Mother and Child MoBI ExperimentImaging SocialInteractionsGedeon Deak et al., 2011
Gedeon Deak et al., 2011
3-yr old child – Reward ObservationHe watches Mom pop the bubble!mublank screen(baseline)Yu Liao, T Mullen, S Makeig, G Deak
Two Poles ofEmpathy / CompassionResearchImaging EmpathyEmpathy à compassionfor all sentient beings Empathy à sympathyfor another’s pain
Two Poles ofEmpathy / CompassionResearchCompassion involves feeling and activelywishing to alleviate another’s suffering.Sympathy is a felt and/or expressedconcern for another’s suffering.
Empathy
Fan, Y., Duncan, N. W., de Greck, M., Northoff, G. (2011). Is there a core neural network in empathy? An fMRI basedquantitative meta-analysis. Neuroscience & Biobehavioral Reviews 35(3), 903-911. A meta-analysis of 40 studies.
What form ofempathy to study ?Empathic Listening and Communication
Empathic Communication
Empathic communication through listeningEmpathy is a respectful understanding of what others areexperiencing. Instead of offering empathy we often have astrong urge to give advice or reassurance and to explain ourown position or feeling. Empathy, however, calls upon us toempty our mind and listen to others with our whole being.In Nonviolent Communication, no matter what words others may use to expressthemselves, we simply listen for their observations, feelings, needs, andrequests. Then we may wish to reflect back, paraphrasing what we haveunderstood. We stay with empathy, allowing others the opportunity to fullyexpress themselves before we turn our attention to solutions or requests forrelief Empathic connection is an understanding of the heart in which we see the beautyin the other person, the life that's alive in them With empathy we don't direct, we follow. Don't just do something, be there!- Marshall Rosenberg (Nonviolent Communication)
What is empathy?“Empathy, I would say is presence. Pure presence to what isalive in a person at this moment, bringing nothing in from thepast. The more you know a person, the harder empathy is. Themore you have studied psychology, the harder empathy reallyis. Because you can bring no thinking in from the past It isabout being present It is not a mental understanding.”Is empathy ‘speaking from the heart?’“In empathy, you don't speak at all. You speak with the eyes. You speak with thebody. If you say any words at all, it's because you are not sure you are with theperson. So you may say some words. But the words are not empathy. Empathy iswhen the other person feels the connection to what's alive in you. The greatest gift one can give another person is empathy.”- Marshall Rosenberg (Nonviolent Communication)
Possible MoBI Experiment Design‘Non-Violent Communication’ Training FormatLeader participant group training sessionsParticipant pair practice sessionsMoBI Measures (two-person EEGs face video ?)Video debriefing (each participant separately)During utterances, contrast speaker-experiencedand listener-experiencedincreases vs. decreases in degree ofexperienced empathic connectionGoals- Image EEG source dynamics- Image EEG source network dynamics- Explore EEG feedback tools, uses in training, etc.S Makeig, 2012
Psychiatry dept response to this proposed experiment‘Non-Violent Communication’ practiceTrainer volunteer participant groupGroup practice sessionsParticipant pair practice sessionsMeasuresVideo debriefing (both participants, separately)During utterances, contrast listener-experiencedincreases vs. decreases in feltempathic connectionGoals- Image EEG source dynamics- Image EEG source network dynamics- Explore EEG feedback tools, uses in training, etc.S Makeig, 2012
Beginningan electrophysiology of human social interactionusing Mobile Brain/Body Imaging (MoBI)High-resolution imaging (in time and space)of cortical dynamics supporting social cognition.sccn.ucsd.edu
Mobile Brain/Body Imaging ( MoBI) 1. Record simultaneously, during naturally motivated action & interaction, What the brain does (high-density EEG) What the brain experiences (sensory scene recording) What the brain organizes (body & eye movements, psychophysiology) 2. Then – Use evolving machine learning methods to find, model, and measure
4 R. EEG T. Study Guide II. Common Neurologic Disorders 25% Study Resources: A. CNS infections Online course: EEG 210: EEG in Neurological Disorders Publication: EEG Clinical Correlations: Infectious, Vascular, & Structural Disorders, 3rd Ed. Publication: Handbook of ICU EEG Monitoring.LaRoche B. Head injury Online course: EEG
May 18, 2020 · Normal EEG Part 1 Dr. Hope 9:00 – 10:30 Normal EEG Part 2 Conference Dr. Hope 9:00 – 10:30 Sleep and EEG Benign Variants Dr. Hope 9:00 – 12:00 Epilepsy Case Management 9:00 – 10:30 Activation methods Dr. Hope 11:00 – 12 EEG Discussion Unknowns and Routine EEG Dr. Hope 11:00 – 12 EEG Discussion Unk
Normal EEG patterns found in the waking and sleep states are identified. Assignments focus on descriptive EEG terms, waveform descriptions, and features that promote the visual analysis of EEG. Information related to medication effects on the EEG is also provided. Normal EEG variants are a ke
EEG signal processing is spectral analysis. Spectral analysis, which converts the original time series to the frequency domain, is a natural choice for EEG signal processing because EEG signals are often described by a, b, u, and d waves, whose Figure 1. EEG signals from a healthy person a
Computer Interface(BCI). BCI is an interfacing system that uses electrical signals from the brain (eg: EEG) as an input to control other devices such as a computer, wheel chair, robotic arm etc. The aim of this work is to analyse the EEG data to see how humans can control machines using
the American EEG Society) and the American Academy of Neurology, reviewed and summarized the relevant literature. The assessment cites some of the reviewed literature, but does not attempt to cite all QEEG literature comprehensively here. Specific panel members reviewed individual topics in detail as well as drafts of the overall assessment.
neto/electroencephalography (M/EEG), these hidden brain states are observed indirectly by sensors, as mixtures from widespread brain sources. Consequently, one of the central themes in M/EEG methods research is the devel-opment of models that solve the ‘‘spatiotemporal inverse problem,’’ i.e., which brain sources caused the observed
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