The End Of Knowledge - Vault 3 - G.TEC BCI & Neurotechnology Spring School 2024 - Day 4

graph LR classDef spring fill:#f9d4d4, font-weight:bold, font-size:14px; classDef bci fill:#d4f9d4, font-weight:bold, font-size:14px; classDef eeg fill:#d4d4f9, font-weight:bold, font-size:14px; classDef vr fill:#f9f9d4, font-weight:bold, font-size:14px; classDef intracranial fill:#f9d4f9, font-weight:bold, font-size:14px; A[Slobodan Tanackovic] --> B[Massive attendance,
global representation. 1] A --> C[BCI-game integration
using Unity. 2] A --> D[Deep learning outshines
classical for EEG. 3] D --> E[TMS-EEG compares
electrode types. 4] E --> F[TMS-EEG requirements:
sampling rate, artifact removal. 5] A --> G[MicroECoG captures
layer 1 neurons. 6] G --> H[Layer 1 conveys
top-down information. 7] A --> I[BCI usability
research overview. 8] I --> J[Co-adaptive training
improves performance. 9] A --> K[BCI-VR visualizes
brain networks. 10] K --> L[Neurotech, AI, VR
for design projects. 11] L --> M[EEG emotions
adapt VR. 12] A --> N[Brain circuit biomarkers
from intracranial recording. 13] N --> O[Intracranial EEG measures
brain development speeds. 14] N --> P[Limbic memory
subsystem biomarker identified. 15] N --> Q[Intracranial ERP clustering
links waveforms, anatomy. 16] N --> R[Intracranial ERPs track
excitability after DBS. 17] A --> S[VR neurofeedback
for ADHD therapy. 18] S --> T[EEG-VR targets
ADHD symptoms. 19] S --> U[EEG-controlled chess
in VR demonstrated. 20] S --> V[Reimbursement goal
for VR neurofeedback. 21] A --> W[Wireless multi-device
EEG acquisition. 22] W --> X[Synchronized hyperscanning,
biosensor integration. 23] W --> Y[LSL enables
real-time synchronization. 24] A --> Z[fNIRS brain
imaging principles. 25] Z --> AA[EEG-fNIRS acquisition
and analysis. 26] AA --> AB[EEG-fNIRS benefits:
complementary information, resolution. 27] Z --> AC[fNIRS-EEG integration
workflows demonstrated. 28] A --> AD[Evolution of
EEG-VR headsets. 29] AD --> AE[EEG-VR applications:
games, therapy, BCI priming. 30] class A,B spring; class C,I,J,K,L,S,T,U,V bci; class D,E,F,W,X,Y,Z,AA,AB,AC eeg; class G,H,N,O,P,Q,R intracranial; class AD,AE,M vr;

Resume:

1.- Over 78,000 attendees joined Spring School, with 118 countries represented. The peak lecture had 17,867 attendees.

2.- Michele Romani from G-Tech presented on integrating BCI technology into games using the Unity environment.

3.- Deep learning models like CNNs outperformed classical machine learning for decoding EEG motor imagery data.

4.- Slobodan Tanackovic and colleagues demonstrated TMS-EEG experiments comparing active and passive electrodes.

5.- For TMS-EEG, the EEG device needs high sampling rates, at least 5-10 kHz. TMS artifact removal is crucial.

6.- Jack Low from Singapore discussed recording microECoG signals to capture layer 1 neuron activity for BMIs.

7.- Layer 1 neurons convey integrated top-down information. MicroECoG enables single-unit resolution over large cortical areas.

8.- Rainer Scherer from Essex University presented an overview of their BCI research on improving usability.

9.- Online co-adaptive training improved first-time BCI performance. Mental task selection is crucial for good accuracies.

10.- BCI-VR was used to visualize brain networks in real-time. Angular gyrus showed consistent changes in pilots.

11.- Firas Kassem-Moussa demonstrated projects integrating neurotechnology, AI and VR for architecture and design.

12.- EEG-based emotional states were used to adapt virtual environments. AI generated personalized spaces from prompts.

13.- Dora from Mayo Clinic discussed brain circuit biomarkers from intracranial stimulation and recording in patients.

14.- Transmission speeds across brain development were measured with intracranial EEG. Speeds increased over 2-fold from childhood to adulthood.

15.- A robust hippocampal-anterior cingulate-posterior cingulate waveform was identified as a limbic memory subsystem biomarker.

16.- Basis profile curve clustering of intracranial ERPs enabled data-driven discovery of waveform clusters linked to anatomy.

17.- Intracranial ERPs were used to track brain network excitability changes after deep brain stimulation for epilepsy.

18.- Si En Christian Reinhardt from Brainsure presented a VR neurofeedback system for enhancing ADHD therapy.

19.- The Brainsure system uses EEG neurofeedback and VR games to target attention, hyperactivity and impulsivity symptoms.

20.- A chess game controlled by focus measured through EEG demonstrated the Brainsure system's VR-EEG integration.

21.- Brainsure aims to get their VR neurofeedback system reimbursed as a prescription digital therapeutic in Germany.

22.- Mika Jiang from G-Tech Vancouver demonstrated wireless multi-device acquisition with G-Tech's Nautilus and Unicorn EEGs.

23.- Up to 4 G-Tech wireless amplifiers can be synchronized for hyperscanning. Other biosensors can be integrated.

24.- Lab streaming layer (LSL) enables real-time synchronization of multiple biosignal streams between acquisition devices.

25.- Maria Antonia Piedrahita-Valdes from G-Tech Colombia explained the principles of fNIRS brain imaging.

26.- She demonstrated how to combine wireless EEG and fNIRS acquisition and analyze the joint data.

27.- Benefits of joint EEG-fNIRS include complementary neural information and improved spatiotemporal resolution.

28.- Different fNIRS devices and software integration workflows with G-Tech EEGs were shown with live demos.

29.- Tiago Falk presented the evolution of his lab's EEG-VR headsets designed for naturalistic brain monitoring.

30.- Applications included adaptive VR games, predicting experiences, multisensory mental health therapies, and boosting motor imagery BCI with multisensory priming.

Knowledge Vault built byDavid Vivancos 2024