NeuralBench is a new benchmarking framework for neuroAI models on EEG data that finds foundation models only marginally outperform task-specific ones while many tasks like cognitive decoding stay highly challenging.
A comparison study of canonical correlation analysis based methods for detecting Steady-State visual evoked potentials
2 Pith papers cite this work. Polarity classification is still indexing.
2
Pith papers citing it
citation-role summary
dataset 1
citation-polarity summary
years
2026 2verdicts
CONDITIONAL 2roles
dataset 1polarities
use dataset 1representative citing papers
Introduces c-MVEP paradigm using motion stimulation, achieving 85.67% accuracy in online 4-class BCI with comparable SNR to c-VEP but different spatial distribution.
citing papers explorer
-
NeuralBench: A Unifying Framework to Benchmark NeuroAI Models
NeuralBench is a new benchmarking framework for neuroAI models on EEG data that finds foundation models only marginally outperform task-specific ones while many tasks like cognitive decoding stay highly challenging.
-
Beyond Flickering: Introducing Code-Modulated Motion Visual Evoked Potentials for Brain-Computer Interfacing
Introduces c-MVEP paradigm using motion stimulation, achieving 85.67% accuracy in online 4-class BCI with comparable SNR to c-VEP but different spatial distribution.