Knowledge Vault 3/91 - G.TEC BCI & Neurotechnology Spring School 2024 - Day 10
Functional mapping with the ECoG and Cortico-Cortical Evoked Potentials
Christoph Guger, g.tec medical engineering GmbH (AT)
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Concept Graph & Resume using Claude 3 Opus | Chat GPT4 | Llama 3:

graph LR classDef ecog fill:#f9d4d4, font-weight:bold, font-size:14px; classDef gtec fill:#d4f9d4, font-weight:bold, font-size:14px; classDef mapping fill:#d4d4f9, font-weight:bold, font-size:14px; classDef experiments fill:#f9f9d4, font-weight:bold, font-size:14px; classDef applications fill:#f9d4f9, font-weight:bold, font-size:14px; A[Christoph Guger] --> B[discusses ECoG
for brain-computer interfaces. 1] A --> C[G.Tec: BCI research, sales,
hardware worldwide. 2] C --> D[Invasive BCI enables seizure AI,
HFO biomarkers. 3] A --> E[Neurosurgeon places ECoG grids
for high gamma mapping. 4] A --> F[Neuromodulation: real-time measurement,
stimulation based on activity. 5] A --> G[Pfurtscheller: ERD from left/right
movement imagination. 6] A --> H[Precise electrode placement crucial. 7] H --> I[Kai Miller: 64 ECoG channels,
rest vs. movement spectra. 8] A --> J[High gamma: less energy at rest,
high spatial resolution. 9] J --> K[Optimal electrode positions key.
High gamma diminishes with repetition. 10] A --> L[ECoG grids: 1 cm spacing,
single-use, 1000, 64 channels. 11] A --> M[Schalk's early ECoG BCI: cursor control,
DOOM. Impressed US Army. 12] M --> N[High gamma overshoot with novel
movements, diminishes with repetition. 13] A --> O[Kamada experiments 2011: real-time
ECoG, gesture classification. 14] O --> P[Telepresence: epilepsy patient controls
1M robot in Japan via ECoG. 15] A --> Q[c-VEP avoids seizure risk in
SSVEP with epilepsy patients. 16] A --> R[Schalk, Brunner: Siegfried BCI maps
cortical activity in epilepsy patients. 17] A --> S[Kai Miller: decoded high gamma
gestures with speed, accuracy. 18] A --> T[ECS: clinical standard for mapping,
limitations vs. high gamma. 19] T --> U[ECS: manual probing, observing effects,
noting stimulated regions. 20] T --> V[ECS: time-consuming, may trigger seizures,
low temporal/spatial resolution. 21] A --> W[High gamma with Cortec-U: visualizes
seizure pathways for surgery. 22] A --> X[High gamma paradigms: rest, Rubik's,
tongue/kiss, story listening. 23] A --> Y[Validation: high gamma matches fMRI
for finger tapping. 24] A --> Z[Picture naming: visual, then language
areas activate. 25] A --> AA[High gamma mapping consistent.
Matches ECS for language areas. 26] A --> AB[Cortec-U used worldwide.
Ince: ultra-high gamma to 1 kHz. 27] A --> AC[CCEPs map networks by stimulation
and distributed recording. 28] A --> AD[High-res ECoG: precise somatotopic
finger mapping. 29] A --> AE[SSEP: median nerve stimulation
identifies central sulcus. 30] A --> AF[ECoG: fast, accurate robotic hand control,
even replicating errors. 31] class A,B,E,F,G,H,I,J,K,L,T,U,V,W,X,Y,Z,AA,AB,AC,AD,AE,AF ecog; class C,D gtec; class M,N,O,P,Q,R,S experiments; class AF applications;


1.- Dr. Christoph Guger discusses high gamma mapping with electrocorticogram (ECoG) for brain-computer interfaces, involving opening scalp and placing ECoG electrodes.

2.- Guger's company G.Tec does research, sales, and hardware production related to brain-computer interfaces in various locations worldwide.

3.- Projects like seizure AI for epilepsy monitoring and high-frequency oscillations as biomarkers are enabled by invasive brain-computer interface technology.

4.- In surgery, the neurosurgeon places ECoG strips/grids on the brain, connected to a biosignal amplifier for high gamma mapping.

5.- Neuromodulation involves measuring from and stimulating different brain regions like the thalamus in real-time based on brain activity.

6.- Gerd Pfurtscheller discovered event-related desynchronization - imagination of left/right hand movement activates the contralateral hemisphere, easily measurable with EEG.

7.- Implants require precise electrode placement. Kai Miller implanted 64 ECoG channels in an epilepsy patient to study resting vs movement spectra.

8.- High gamma (80-100 Hz) has less energy at rest than during movement, with high spatial resolution localized to single electrodes.

9.- Finding optimal electrode positions is crucial for ECoG. Repetitive movements cause high gamma to diminish as the spinal cord takes over.

10.- ECoG grids have 1 cm spaced platinum electrodes, are single-use, and cost ~1000. 64 channels output via cable from scalp.

11.- Early ECoG BCI experiments in 2004 by Gerwin Schalk enabled 2D cursor control and later control of the video game DOOM.

12.- Schalk's videos impressed the US Army, leading to research funding. High gamma overshot occurs with novel movements, diminishing with repetition.

13.- Experiments with Dr. Kamada in Japan in 2011 used MATLAB/Simulink for real-time ECoG processing to classify hand gestures with high accuracy.

14.- A telepresence experiment allowed an epilepsy patient to control a 1 million robotic system in Japan via ECoG BCI.

15.- Code-modulated visual evoked potentials avoid seizure risk in SSVEP experiments with epilepsy patients, as demonstrated by Christoph Kapeller.

16.- Gerwin Schalk and Peter Brunner developed the Siegfried BCI system, rapidly mapping cortical activity during various tasks in epilepsy patients.

17.- The temporal dynamics of high gamma activity during hand gestures were decoded with outstanding speed and accuracy by Kai Miller.

18.- Electrical cortical stimulation (ECS) is the clinical standard for cortical mapping but has limitations compared to high gamma mapping.

19.- ECS involves manually probing electrode pairs with increasing current, observing effects, and noting functions of stimulated regions.

20.- ECS is time-consuming, can trigger seizures if current is too high, and lacks the temporal/spatial resolution of high gamma mapping.

21.- High gamma mapping with Cortec-U enables visualization of seizure propagation pathways to guide surgical interruption by neurosurgeons.

22.- Paradigms for high gamma mapping include resting, solving Rubik's cube (fingers), tongue/kiss movements (mouth), and story listening (auditory cortex).

23.- Validation studies show strong overlap between high gamma mapping and BOLD fMRI for finger tapping in sensorimotor cortex.

24.- Temporal dynamics during picture naming show visual cortex, then Broca's/Wernicke's language areas activating to process and respond.

25.- Tests confirm high gamma mapping produces consistent results. Comparison with ECS shows close correspondence for identifying expressive language areas.

26.- Cortec-U is used worldwide. Research by Nuri Firat Ince using micro-ECoG grids found ultra-high gamma up to 1 kHz.

27.- Cortico-cortical evoked potentials (CCEPs), first described by Riki Matsumoto in 2004, map networks by stimulating one area and recording elsewhere.

28.- High-resolution ECoG allows precise somatotopic mapping of individual fingers in sensorimotor cortex for sensation and movement.

29.- Somatosensory evoked potentials from median nerve stimulation quickly identify the central sulcus by polarity reversal of waveforms across electrodes.

30.- ECoG enables fast, accurate control of robotic prosthetic hands, even replicating user errors, demonstrating effective brain embodiment of devices.

31.- In summary, the talk comprehensively covered the technology, applications, and research behind ECoG-based brain-computer interfaces for clinical and research purposes

Knowledge Vault built byDavid Vivancos 2024