Felix Schroeder
@knusperpsy.bsky.social
Cognitive Neuroscience & Neuroergonomics
Back soon - just need to write up my PhD..
Back soon - just need to write up my PhD..
Our results demonstrate how easily CV choices can distort conclusions, particularly for more complex models, and underscore the importance of transparency.
Thanks to Stephen Fairclough, Frederic Dehais, and Matt Richins for their guidance!
📄 Paper: doi.org/pt6h
#Neurotech #BCI #OpenScience #EEG
Thanks to Stephen Fairclough, Frederic Dehais, and Matt Richins for their guidance!
📄 Paper: doi.org/pt6h
#Neurotech #BCI #OpenScience #EEG
Frontiers | The impact of cross-validation choices on pBCI classification metrics: lessons for transparent reporting
Neuroadaptive technologies are a type of passive Brain-computer interface (pBCI) that aim to incorporate implicit user-state information into human-machine i...
doi.org
July 2, 2025 at 11:48 AM
Our results demonstrate how easily CV choices can distort conclusions, particularly for more complex models, and underscore the importance of transparency.
Thanks to Stephen Fairclough, Frederic Dehais, and Matt Richins for their guidance!
📄 Paper: doi.org/pt6h
#Neurotech #BCI #OpenScience #EEG
Thanks to Stephen Fairclough, Frederic Dehais, and Matt Richins for their guidance!
📄 Paper: doi.org/pt6h
#Neurotech #BCI #OpenScience #EEG
🔍 Takeaways for offline pBCI studies:
- Clearly document your data-splitting strategy — subtle choices can skew results
- If using blocks: keep them short, interleave conditions, and don’t split a block across train/test
- Trial-level randomisation is usually better to avoid time-based confounds
- Clearly document your data-splitting strategy — subtle choices can skew results
- If using blocks: keep them short, interleave conditions, and don’t split a block across train/test
- Trial-level randomisation is usually better to avoid time-based confounds
July 2, 2025 at 11:48 AM
🔍 Takeaways for offline pBCI studies:
- Clearly document your data-splitting strategy — subtle choices can skew results
- If using blocks: keep them short, interleave conditions, and don’t split a block across train/test
- Trial-level randomisation is usually better to avoid time-based confounds
- Clearly document your data-splitting strategy — subtle choices can skew results
- If using blocks: keep them short, interleave conditions, and don’t split a block across train/test
- Trial-level randomisation is usually better to avoid time-based confounds
Reposted by Felix Schroeder
It was probably this! datavizsp25.classes.andrewheiss.com/resource/ai-...
AI, LLMs, and BS – Data Visualization with R
Use R, ggplot2, and the principles of graphic design to create beautiful and truthful visualizations of data
datavizsp25.classes.andrewheiss.com
April 26, 2025 at 11:42 PM
It was probably this! datavizsp25.classes.andrewheiss.com/resource/ai-...