19-channel EEG
19-channel EEG
RESEARCH / BIOMEDICAL AI
From raw signal to meaningful representation.
EEG seizure classification through signal processing, fuzzy recurrence plots and deep learning.
EEG / REPRESENTATION & EVALUATION
Patient-aware by design.
Validated 19-channel EEG → FRP preprocessing, completed four-class Swin segment experiments and a separate three-class patient pilot; CNN-vs-Swin comparison remains ongoing.
SIGNAL → REPRESENTATION
SCHEMATIC / NOT PATIENT DATA
Five-band filtering
Five-band filtering
1-second windows
1-second windows
AMI / FNN parameters
AMI / FNN parameters
Research boundary
Four-class segment discrimination remains challenging; accuracy is modest across bands. MYSZ has only one patient and is excluded from the patient-separated pilot. Only five held-out test patients; initial generalization pilot, not a definitive cross-patient performance estimate. Segment and patient results are not directly comparable: class sets, evaluation units and split strategies differ. Controlled CNN-vs-Swin patient-separated comparison is ongoing/planned; it has no supplied completed results. Internal Biomedical Sensors and Systems Lab, University of Memphis research; no public repository, publication URL or stable external artifact link is supplied.