A modular EEG-based BCI with S4D deep learning classifier achieves 84% offline accuracy and enables real-time control for a tetraplegic user, with 73% success in post-competition validation.
Factors of Influence on the Per- formance of a Short-Latency Non-Invasive Brain Switch: Evidence in Healthy Individuals and Implication for Motor Function Rehabilitation
1 Pith paper cite this work. Polarity classification is still indexing.
1
Pith paper citing it
fields
cs.HC 1years
2025 1verdicts
CONDITIONAL 1representative citing papers
citing papers explorer
-
Improving motor imagery decoding methods for an EEG-based mobile brain-computer interface in the context of the 2024 Cybathlon
A modular EEG-based BCI with S4D deep learning classifier achieves 84% offline accuracy and enables real-time control for a tetraplegic user, with 73% success in post-competition validation.