|
Size: 4637
Comment:
|
Size: 4545
Comment:
|
| Deletions are marked like this. | Additions are marked like this. |
| Line 14: | Line 14: |
| . Raymundo Cassani (McGill University) Takfarinas Medani (University of Southern California) 12:30–13:00 Onsite assistance in installing the material for the training session |
. Raymundo Cassani (McGill University) Takfarinas Medani (University of Southern California) 12:30–13:00 Onsite assistance in installing the material for the training session |
| Line 21: | Line 20: |
| <<BR>>Set anatomy | Set anatomy |
| Line 23: | Line 22: |
| <<BR>>Review Raw recordings | Review Raw recordings |
| Line 25: | Line 24: |
| <<BR>>Import events | Import events |
| Line 29: | Line 28: |
| <<BR>>Frequency filters | Frequency filters |
| Line 31: | Line 30: |
| <<BR>>Artefact detections | Artefact detections |
| Line 33: | Line 32: |
| <<BR>>Artifact correction with SSP | Artifact correction with SSP |
| Line 39: | Line 38: |
| <<BR>>''Import recording'' | ''Import recording'' |
| Line 41: | Line 40: |
| <<BR>>Review trials | Review trials |
| Line 43: | Line 42: |
| <<BR>>Trial averages | Trial averages |
| Line 47: | Line 46: |
| <<BR>>Forward Model (aka Head model) | Forward Model (aka Head model) |
| Line 49: | Line 48: |
| <<BR>>Noise covariance matrix | Noise covariance matrix |
| Line 51: | Line 50: |
| <<BR>>Source estimation (from EEG and MEG recording) | Source estimation (from EEG and MEG recording) |
| Line 55: | Line 54: |
| <<BR>>Cortex parcellations: Atlases and scouts | Cortex parcellations: Atlases and scouts |
| Line 57: | Line 56: |
| <<BR>>Noise covariance matrix | Noise covariance matrix |
| Line 59: | Line 58: |
| <<BR>>Source estimation (from EEG and MEG recording) | Source estimation (from EEG and MEG recording) |
WEDNESDAY 18th
5:30 – [Global] Reproductible processing pipelines and multiverses8:30 – Registration & Continental Breakfast9:15 – Richard Leahy (University of Southern California): Opening of the LA Garden9:30 – David Shattuck (University of California, Los Angeles): Introduction and Overview of Brainsuite10:15 Coffee Break
10:30 – Anand Joshi (University of Southern California): Brainsuite Demo & Discussion
11:00 – Richard Leahy (University of Southern California): Introduction to EEG/MEG Analysis
11:45 – Cameron Sacks (Wearable Sensing): EEG Live Demo & Discussion
12:00 – Lunch Break
12:30–17:30 – Tutorial – Hands-On Brainstorm
Raymundo Cassani (McGill University) Takfarinas Medani (University of Southern California) 12:30–13:00 Onsite assistance in installing the material for the training session
13:00–13:30 Introduction to Brainstorm (lecture)
13:30–14:35 Loading anatomy and recordings
Set anatomy
Review Raw recordings
Import events
14:35–15:35 Pre-processing
Frequency filters
Artefact detections
Artifact correction with SSP
15:30–15:45 Coffee Break
15:45–16:20 Analysis sensor level
Import recording
Review trials
Trial averages
16:20–16:55 Source estimation
Forward Model (aka Head model)
Noise covariance matrix
Source estimation (from EEG and MEG recording)
16:55–17:15 Analysis source level
Cortex parcellations: Atlases and scouts
Noise covariance matrix
Source estimation (from EEG and MEG recording)
THURSDAY 19th
5:30 – [Global] Deep Neural Network (DNN) analysis for MEEG data
8:30 – Continental Breakfast
9:00 – 17:30 – [Local] Machine Learning and EEG
Session 1: Computational Tools and Pipelines for ML analysis; session chair: Richard Leahy
9:00–9:25- Arnaud Delorme (University of California, San Diego): “Machine learning and the BIDS EEG data format” 9:25–9:50- Tim Mullen (Intheon Labs): “Creating Deployable Workflows for EEG Signal Processing and ML/DL Using NeuroPype” 9:50–10:15– Ivan Tashev (Microsoft Research): “Workload estimation using brain- and bio- signals for adaptive training system” 10:15–10:40– Bin He (Carnegie Mellon University): “AI/ML Enhances Dynamic Brain Imaging from EEG/MEG” Coffee Break (10:40–11:00)
Session 2: Self Supervised Leaning; session chair: Takfarinas Medani
11:00–11:25– Dominique Duncan (University of Southern California): “Unsupervised Multivariate Time-Series Transformers for Seizure Identification on EEG” 11:25–11:50– Wenhui Cui (University of Southern California): “Neuro-GPT: A Foundation Model Pretrained on Large-Scale EEG Data”
Lunch Break (12:00–13:00)
Session 3: Machine Learning for brain computer interfaces; session chair: Shrikanth Narayanan
13:00–13:30- Alexander Silva (University of California, San Francisco): “A high performance neuroprosthesis for speech decoding and avatar control“ 13:30–14:00– Maryam Shanechi (University of Southern California): “AI-powered next-generation neurotechnologies” 14:00–14:30– Ludovic Bellier (University of California, Berkeley) : “Reconstructing Pink Floyd from human auditory cortex”
Panel Discussion
14:40–15:10– Moderator: Shrikanth Narayanan and Kristina Lerman (University of Southern California): “The Role of Foundational Models in Spontaneous and Event Related EEG“
Coffee Break(15:10–15:40)
Session 4: Machine Learning for neurological disorders; session chair: Kristina Lerman
15:40–16:05– Srikantan Nagarajan (University of California, San Francisco): “Machine learning algorithms for electromagnetic brain imaging in dementia” 16:05–16:30– Dimitrios Pantazis (Massachusetts Institute of Technology): “Graph representation learning of MEG signals opens a window to aging trajectories and Alzheimer’s disease” 16:30–16:55– Jason da Silva Castanheira (McGill University): “Inter-individual differences in neurophysiology vary with age and disease”
Closing and final remarks