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| '''13:00–13:30 '''[[https://neuroimage.usc.edu/resources/CuttingEEG2023finalSlides/2023_lax_tm.pptx|Introduction to Brainstorm (lecture)]] | '''13:00–13:30 '''[[https://neuroimage.usc.edu/resources/CuttingEEG2023finalSlides/2023_lax_tm_bst.pdf|Introduction to Brainstorm (lecture)]] |
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| 5:30 – [''Global''] Deep Neural Network (DNN) analysis for MEEG data '' '' | '''5:30 –''' [''Global''] Deep Neural Network (DNN) analysis for MEEG data '' '' |
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| 8:30 – ''' Continental Breakfast''' '' '' | '''8:30 –''' ''' Continental Breakfast''' '' '' |
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| 9:00 – 17:30 – [''Local''] Machine Learning and EEG '' '' | '''9:00 – 17:30 –''' [''Local''] Machine Learning and EEG '' '' |
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| . Session 1: Computational Tools and Pipelines for ML analysis; session chair: Richard Leahy.'' '' . 9:00–9:25- Arnaud Delorme (University of California, San Diego): “''[[https://neuroimage.usc.edu/resources/CuttingEEG2023finalSlides/BIDS_ml_usc_delorme.pdf|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)'': [[https://neuroimage.usc.edu/resources/CuttingEEG2023finalSlides/2023_Garden_WorkloadEstimation_IvanTashev_final.pdf|“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)'': [[https://neuroimage.usc.edu/resources/CuttingEEG2023finalSlides/Cutting_EEG_2023_Duncan.pdf|“Unsupervised Multivariate Time-Series Transformers for Seizure Identification on EEG”]]'' '' '' . 11:25–11:50''– ''Wenhui Cui (University of Southern California)'': [[https://neuroimage.usc.edu/resources/CuttingEEG2023finalSlides/eeg_foundation_model_talk_Cui.pdf|“Neuro-GPT: A Foundation Model Pretrained on Large-Scale EEG Data”]]'' '' '' |
'''Session 1: Computational Tools and Pipelines for ML analysis; session chair: Richard Leahy.'' '' ''' |
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| ''' Lunch ''''''Break (12:00–13:00)''' '' '' | . '''9:00–9:25- Arnaud Delorme '''(University of California, San Diego): “''[[https://neuroimage.usc.edu/resources/CuttingEEG2023finalSlides/BIDS_ml_usc_delorme.pdf|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)'': [[https://neuroimage.usc.edu/resources/CuttingEEG2023finalSlides/2023_Garden_WorkloadEstimation_IvanTashev_final.pdf|“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)''' '' '' |
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| ''' Session 3: Machine Learning for brain computer interfaces; session chair: Shrikanth Narayanan''' '' '' | '''Session 2: Self Supervised Leaning; session chair: Takfarinas Medani '' '' ''' |
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| . 13:00–13:30- Alexander Silva (University of California, San Francisco): [[https://neuroimage.usc.edu/resources/CuttingEEG2023finalSlides/USCsymposium_Alex_Silva.pdf|“A high performance neuroprosthesis for speech decoding and avatar control]]''[[https://neuroimage.usc.edu/resources/CuttingEEG2023finalSlides/USCsymposium_Alex_Silva.pdf|“]]'' '' '' . 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) '': [[https://neuroimage.usc.edu/resources/CuttingEEG2023finalSlides/CuttingEEG20231029_LBellier.pdf|“Reconstructing Pink Floyd from human auditory cortex”]]'' '' '' |
. '''11:00–11:25''– ''Dominique Duncan''' (University of Southern California)'': [[https://neuroimage.usc.edu/resources/CuttingEEG2023finalSlides/Cutting_EEG_2023_Duncan.pdf|“Unsupervised Multivariate Time-Series Transformers for Seizure Identification on EEG”]]'' '' '' . '''11:25–11:50''– ''Wenhui Cui '''(University of Southern California)'': [[https://neuroimage.usc.edu/resources/CuttingEEG2023finalSlides/eeg_foundation_model_talk_Cui.pdf|“Neuro-GPT: A Foundation Model Pretrained on Large-Scale EEG Data”]]'' |
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| ''' Panel Discussion''' '' '' | Lunch Break (12:00–13:00)''' '' '' ''' |
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| . 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“'' '' '' | '''Session 3: Machine Learning for brain computer interfaces; session chair: Shrikanth Narayanan '' '' ''' |
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| ''' Coffee ''''''Break(15:10–15:40)''' '' '' | . '''13:00–13:30- Alexander Silva '''(University of California, San Francisco):''' [[https://neuroimage.usc.edu/resources/CuttingEEG2023finalSlides/USCsymposium_Alex_Silva.pdf|“A high performance neuroprosthesis for speech decoding and avatar control]]''[[https://neuroimage.usc.edu/resources/CuttingEEG2023finalSlides/USCsymposium_Alex_Silva.pdf|“]]'' '' '' ''' . '''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) '': [[https://neuroimage.usc.edu/resources/CuttingEEG2023finalSlides/CuttingEEG20231029_LBellier.pdf|“Reconstructing Pink Floyd from human auditory cortex”]]'' '' '' |
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| ''' Session 4: Machine Learning for neurological disorders; session chair: Kristina Lerman''' '' '' | Panel Discussion''' '' '' ''' |
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| . 15:40–16:05''– ''Srikantan Nagarajan (University of California, San Francisco)'': [[https://neuroimage.usc.edu/resources/CuttingEEG2023finalSlides/USCTalk_Oct2023_v3_Sri.pdf|“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)'': [[https://neuroimage.usc.edu/resources/CuttingEEG2023finalSlides/Cutting_Gardens_daSilvaCastanheira_short.pdf|“Inter-individual differences in neurophysiology vary with age and disease”]]'' '' '' |
. '''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“'' '' '' |
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| ''' Closing and final remarks''' '' '' | 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)'': [[https://neuroimage.usc.edu/resources/CuttingEEG2023finalSlides/USCTalk_Oct2023_v3_Sri.pdf|“Machine learning algorithms for electromagnetic brain imaging in dementia”]]'' '' '' . '''16:05–16:30''– ''Dimitrios Pantazis''' (Massachusetts Institute of Technology)'': [[https://neuroimage.usc.edu/resources/CuttingEEG2023finalSlides/CuttingEEG_GardenLA2023_Pantazis.pdf|“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)'': [[https://neuroimage.usc.edu/resources/CuttingEEG2023finalSlides/Cutting_Gardens_daSilvaCastanheira_short.pdf|“Inter-individual differences in neurophysiology vary with age and disease”]]'' '' '' ''' '''Closing and final remarks''' '' '' ''' ''' |
CuttingGarden2023 Symposium: LA Garden Program
WEDNESDAY 18th
5:30 – [Global] Reproductible processing pipelines and multiverses
8:30 – Registration & Continental Breakfast
9:15 – Richard Leahy (University of Southern California):Opening of the LA Garden
9:30 – David Shattuck (University of California, Los Angeles): Introduction and Overview of Brainsuite
10:15 Coffee Break
10:30 – Anand Joshi (University of Southern California): Brainsuite Tools & 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