← Within-Session Motor Imagery (left hand vs. right hand)

Papers with Code · CC BY-SA 4.0

GrosseWentrup2009 MOABB

아카이브 평가 19건 · 마지막 적재/변경 2026-10-30

원본 데이터셋 링크 1

평가 기록

과거 수집 기록이며 최신 순위가 아닙니다. 데이터셋 버전·split·실험 조건과 지표 방향이 검증되지 않은 결과는 직접 비교할 수 없습니다. 날짜는 논문 날짜이며 최신 평가일을 뜻하지 않습니다.

표가 화면보다 넓으면 좌우로 스크롤하여 논문과 구현 링크를 확인하세요.

원본 기록 순서 · 순위 미부여
모델	원본 지표·값	논문	구현
TS + EL	AUC-ROC 89.2466661 training time (s) 243.142401 CO2 Emission (g) 0.269148976	The largest EEG-based BCI reproducibility study for open science: the MOABB benchmark 2024-04-03	코드 1
TS + SVM	AUC-ROC 88.0777776 training time (s) 265.222582 CO2 Emission (g) 0.28341764999999997	The largest EEG-based BCI reproducibility study for open science: the MOABB benchmark 2024-04-03	코드 1
TS + LR	AUC-ROC 87.60000099999999 training time (s) 34.60417205 CO2 Emission (g) 0.0139535835	The largest EEG-based BCI reproducibility study for open science: the MOABB benchmark 2024-04-03	코드 1
ACM + TS + SVM	AUC-ROC 87.42888820000002 training time (s) 325.665074 CO2 Emission (g) 6.55720808	The largest EEG-based BCI reproducibility study for open science: the MOABB benchmark 2024-04-03	코드 1
FgMDM	AUC-ROC 87.0177778 training time (s) 85.13785279999999 CO2 Emission (g) 0.15610860310000002	The largest EEG-based BCI reproducibility study for open science: the MOABB benchmark 2024-04-03	코드 1
ShallowConvNet	AUC-ROC 86.52888920000001 training time (s) 277.2352575 CO2 Emission (g) 2.3070343	The largest EEG-based BCI reproducibility study for open science: the MOABB benchmark 2024-04-03	코드 1
EEGNet-8,2	AUC-ROC 83.0155557 training time (s) 117.19713259999999 CO2 Emission (g) 0.9723642659999999	The largest EEG-based BCI reproducibility study for open science: the MOABB benchmark 2024-04-03	코드 1
DeepConvNet	AUC-ROC 82.382222 training time (s) 110.56912460000001 CO2 Emission (g) 0.9234428940000001	The largest EEG-based BCI reproducibility study for open science: the MOABB benchmark 2024-04-03	코드 1
LogVar + SVM	AUC-ROC 81.7311101 training time (s) 6.762669579999999 CO2 Emission (g) 0.00362474352	The largest EEG-based BCI reproducibility study for open science: the MOABB benchmark 2024-04-03	코드 1
FBCSP + SVM	AUC-ROC 79.6511118 training time (s) 117.8105055 CO2 Emission (g) 21.6308037	The largest EEG-based BCI reproducibility study for open science: the MOABB benchmark 2024-04-03	코드 1
LogVar + LDA	AUC-ROC 78.7111117 training time (s) 4.040722504 CO2 Emission (g) 0.000772556251	The largest EEG-based BCI reproducibility study for open science: the MOABB benchmark 2024-04-03	코드 1
TRCSP + LDA	AUC-ROC 78.2866669 training time (s) 14.640627636 CO2 Emission (g) 0.002421635666	The largest EEG-based BCI reproducibility study for open science: the MOABB benchmark 2024-04-03	코드 1
CSP + SVM	AUC-ROC 77.8066665 training time (s) 37.7887083 CO2 Emission (g) 0.0356465945	The largest EEG-based BCI reproducibility study for open science: the MOABB benchmark 2024-04-03	코드 1
CSP + LDA	AUC-ROC 76.4377781 training time (s) 4.92442061 CO2 Emission (g) 0.000984520588	The largest EEG-based BCI reproducibility study for open science: the MOABB benchmark 2024-04-03	코드 1
DLCSPauto + shLDA	AUC-ROC 76.4022231 training time (s) 8.404077422 CO2 Emission (g) 0.001667790323	The largest EEG-based BCI reproducibility study for open science: the MOABB benchmark 2024-04-03	코드 1
EEGITNet	AUC-ROC 72.18666590000001 training time (s) 106.55474389999999 CO2 Emission (g) 0.8872510549999999	The largest EEG-based BCI reproducibility study for open science: the MOABB benchmark 2024-04-03	코드 1
EEGTCNet	AUC-ROC 68.4511113 training time (s) 183.107304 CO2 Emission (g) 1.518727995	The largest EEG-based BCI reproducibility study for open science: the MOABB benchmark 2024-04-03	코드 1
MDM	AUC-ROC 64.291111 training time (s) 14.947136800000001 CO2 Emission (g) 0.01076914422	The largest EEG-based BCI reproducibility study for open science: the MOABB benchmark 2024-04-03	코드 1
EEGNeX	AUC-ROC 56.99555540000001 training time (s) 422.45747300000005 CO2 Emission (g) 3.4729265	The largest EEG-based BCI reproducibility study for open science: the MOABB benchmark 2024-04-03	코드 1