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EEG Signal Processing & Classification: Sleep Spindle Detection and Motor Imagery Classification: Baseline CSP classifiers achieve approximately 61.5% / 61.7% mean session-wise LOSO accuracy. Baseline CSP + LDA and CSP + linear SVM mean LOSO accuracy. ~61.5% Baseline CSP + LDA LOSO accuracy · local research. PhysioNet EEG Motor Movement/Imagery: clean 106-subject baseline, sessions R04, R08 and R12. 8–30 Hz filtering; cue-locked epochs -1.0 to 4.0 s, baseline -1.0 to 0.0 s, classification window 0.5 to 3.5 s. Fit CSP on training folds only. Subject-wise Leave-One-Session-Out across R04, R08 and R12, avoiding random trial splits. Research workflows and benchmarks, not clinical diagnostic systems or production medical devices. EPCTL11 detection-count agreement is a calibration-participant result, not universal detection improvement or validated accuracy, sensitivity or specificity. EPCTL14 generalized well; EPCTL12 showed moderate over-sensitivity; EPCTL13 and EPCTL15 exposed cross-subject failure modes. Manual detections generally had shorter average durations than YASA. No aggregate cross-participant quality metric is supplied. S088, S092 and S100 were excluded because their 128 Hz recordings differed from the expected 160 Hz; the clean baseline contains 106 of 109 audited subjects. LOSO means Leave-One-Session-Out within subjects across R04, R08 and R12, not leave-one-subject-out or clinical performance. Tuning cohort size and uncertainty estimates are not separately supplied. Tuning yielded only about 1–1.2 percentage points of reported mean LOSO improvement; subject/session non-stationarity remained a larger constraint than classifier choice alone. ANPHY-Sleep / PhysioNet methods, metrics and artifact belong to this project, separately from Swin/FRP seizure classification on TUSZ. EEG Signal Processing & Classification: Sleep Spindle Detection and Motor Imagery Classification: Tuned CSP classifiers achieve approximately 62.7% / 62.4% mean session-wise LOSO accuracy. Tuned CSP + LDA and CSP + linear SVM mean LOSO accuracy; no inferred tuning cohort size. ~62.7% Tuned CSP + LDA LOSO accuracy · local research. PhysioNet EEG Motor Movement/Imagery left-vs-right motor imagery; tuning cohort size not separately supplied. 8–30 Hz filtering; cue-locked epochs -1.0 to 4.0 s, baseline -1.0 to 0.0 s, classification window 0.5 to 3.5 s. Fit CSP on training folds only. Subject-wise Leave-One-Session-Out across R04, R08 and R12, avoiding random trial splits. LDA: 6 CSP components, LSQR, shrinkage = 0.3. Linear SVM: 8 CSP components. Confusion matrices, CSP spatial patterns and CSV summaries inspected in a Streamlit experiment dashboard. Research workflows and benchmarks, not clinical diagnostic systems or production medical devices. EPCTL11 detection-count agreement is a calibration-participant result, not universal detection improvement or validated accuracy, sensitivity or specificity. EPCTL14 generalized well; EPCTL12 showed moderate over-sensitivity; EPCTL13 and EPCTL15 exposed cross-subject failure modes. Manual detections generally had shorter average durations than YASA. No aggregate cross-participant quality metric is supplied. S088, S092 and S100 were excluded because their 128 Hz recordings differed from the expected 160 Hz; the clean baseline contains 106 of 109 audited subjects. LOSO means Leave-One-Session-Out within subjects across R04, R08 and R12, not leave-one-subject-out or clinical performance. Tuning cohort size and uncertainty estimates are not separately supplied. Tuning yielded only about 1–1.2 percentage points of reported mean LOSO improvement; subject/session non-stationarity remained a larger constraint than classifier choice alone. ANPHY-Sleep / PhysioNet methods, metrics and artifact belong to this project, separately from Swin/FRP seizure classification on TUSZ.

[1] Research evaluation[2] Research evaluation
direct evidenceResearch evaluation

EEG Signal Processing & Classification: Sleep Spindle Detection and Motor Imagery Classification

Baseline CSP classifiers achieve approximately 61.5% / 61.7% mean session-wise LOSO accuracy. Baseline CSP + LDA and CSP + linear SVM mean LOSO accuracy.

Documented resultBaseline CSP classifiers achieve approximately 61.5% / 61.7% mean session-wise LOSO accuracyMotor imagery baseline LOSO (local research; completed): 8–30 Hz filtering; cue-locked epochs -1.0 to 4.0 s, baseline -1.0 to 0.0 s, classification window 0.5 to 3.5 s. Fit CSP on training folds only. Subject-wise Leave-One-Session-Out across R04, R08 and R12, avoiding random trial splits. PhysioNet EEG Motor Movement/Imagery: clean 106-subject baseline, sessions R04, R08 and R12. Baseline CSP + LDA LOSO accuracy: ~61.5%. Baseline CSP + LDA; reported mean research LOSO accuracy. PhysioNet EEG Motor Movement/Imagery: clean 106-subject baseline, sessions R04, R08 and R12. Within-subject Leave-One-Session-Out (R04, R08, R12). Not a clinical or held-out-subject result. Baseline CSP + linear SVM LOSO accuracy: ~61.7%. Baseline CSP + linear SVM; reported mean research LOSO accuracy. PhysioNet EEG Motor Movement/Imagery: clean 106-subject baseline, sessions R04, R08 and R12. Within-subject Leave-One-Session-Out (R04, R08, R12). Not a clinical or held-out-subject result. Research workflows and benchmarks, not clinical diagnostic systems or production medical devices. S088, S092 and S100 were excluded because their 128 Hz recordings differed from the expected 160 Hz; the clean baseline contains 106 of 109 audited subjects. LOSO means Leave-One-Session-Out within subjects across R04, R08 and R12, not leave-one-subject-out or clinical performance. Tuning cohort size and uncertainty estimates are not separately supplied. Tuning yielded only about 1–1.2 percentage points of reported mean LOSO improvement; subject/session non-stationarity remained a larger constraint than classifier choice alone.
direct evidenceResearch evaluation

EEG Signal Processing & Classification: Sleep Spindle Detection and Motor Imagery Classification

Tuned CSP classifiers achieve approximately 62.7% / 62.4% mean session-wise LOSO accuracy. Tuned CSP + LDA and CSP + linear SVM mean LOSO accuracy; no inferred tuning cohort size.

Documented resultTuned CSP classifiers achieve approximately 62.7% / 62.4% mean session-wise LOSO accuracyMotor imagery tuned LOSO (local research; completed): 8–30 Hz filtering; cue-locked epochs -1.0 to 4.0 s, baseline -1.0 to 0.0 s, classification window 0.5 to 3.5 s. Fit CSP on training folds only. Subject-wise Leave-One-Session-Out across R04, R08 and R12, avoiding random trial splits. LDA: 6 CSP components, LSQR, shrinkage = 0.3. Linear SVM: 8 CSP components. Confusion matrices, CSP spatial patterns and CSV summaries inspected in a Streamlit experiment dashboard. PhysioNet EEG Motor Movement/Imagery left-vs-right motor imagery; tuning cohort size not separately supplied. Tuned CSP + LDA LOSO accuracy: ~62.7%. 6 CSP components, LSQR and shrinkage = 0.3; reported mean research LOSO accuracy. PhysioNet EEG Motor Movement/Imagery left-vs-right motor imagery; tuning cohort size not separately supplied. Within-subject Leave-One-Session-Out (R04, R08, R12). Not a clinical or held-out-subject result. Tuned CSP + linear SVM LOSO accuracy: ~62.4%. 8 CSP components, linear SVM; reported mean research LOSO accuracy. PhysioNet EEG Motor Movement/Imagery left-vs-right motor imagery; tuning cohort size not separately supplied. Within-subject Leave-One-Session-Out (R04, R08, R12). Not a clinical or held-out-subject result. Research workflows and benchmarks, not clinical diagnostic systems or production medical devices. S088, S092 and S100 were excluded because their 128 Hz recordings differed from the expected 160 Hz; the clean baseline contains 106 of 109 audited subjects. LOSO means Leave-One-Session-Out within subjects across R04, R08 and R12, not leave-one-subject-out or clinical performance. Tuning cohort size and uncertainty estimates are not separately supplied. Tuning yielded only about 1–1.2 percentage points of reported mean LOSO improvement; subject/session non-stationarity remained a larger constraint than classifier choice alone.

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