Cho2017 or GigaDB
Ref: Cho et al. (2017)
1 Dataset Overview
| Field | Details |
|---|---|
| Modality | EEG + EMG |
| Paradigm/task | Cued left/right-hand MI |
| Participants | 52 healthy |
| Channels | 64 EEG + 2 EMG |
| Sampling rate | 512 Hz |
| Classes/events | 2 classes |
| Sessions/runs | 1 session, 5 or 6 runs |
| Trials | 100 or 120 trials/class (200 or 240/participant) |
| Trial/epoch duration | 3 s MI period |
| Hardware/montage | BioSemi ActiveTwo, CMS/DRL active electrodes, standard_1005 BCI2000 |
| License | CC BY 4.0 |
| Data/source | Data Paper |
| MOABB | Cho2017 |
Note: subjects 31, 45, and 48 are usually removed in the literature; the source paper’s own analysis used 8–30 Hz band-pass and 10-fold CV with 7/3 train/test splits.
1.1 Montage and Trial Paradigm


Source: Cho et al. (2017)

Source: Cho et al. (2017)
Random Chance Cho et al. (2017)
The literature showed that the upper confidence limits of chance with α = 5% were 70% (classification accuracy) in a 2-class prob- lem, with 10 trials for each class.
2 Results
2.1 Summary
2.1.1 Protocol-Unclear Benchmark Extract
The local extract from Chevallier et al. (2024) does not identify enough protocol detail to assign these results without guessing.
| pipeline | Cho2017 |
|---|---|
| ACM+TS+SVM | 73.56±14.54 |
| CSP+LDA | 71.38±14.54 |
| CSP+SVM | 71.92±14.25 |
| DLCSPauto+shLDA | 71.16±14.53 |
| DeepConvNet | 71.67±12.91 |
| EEGITNet | 57.20±12.21 |
| EEGNeX | 53.28±10.60 |
| EEGNet-8,2 | 66.79±16.34 |
| EEGTCNet | 58.34±12.63 |
| FBCSP+SVM | 67.91±15.63 |
| FgMDM | 72.90±12.70 |
| LogVar+LDA | 64.49±10.08 |
| LogVar+SVM | 65.46±11.71 |
| MDM | 63.39±13.69 |
| ShallowConvNet | 73.84±14.95 |
| TRCSP+LDA | 71.85±13.84 |
| TS+EL | 76.23±14.21 |
| TS+LR | 75.01±13.71 |
| TS+SVM | 74.62±14.19 |
| Average | 68.47 |
2.2 Within-Session / Within-Subject
2.2.1 Cho Et Al. (2017) — 10-Fold Evaluation
The mean accuracy of all BCI performances (Fig. 4C) over the 50 subjects, excluding bad subjects (29 and 34), was 67.46% (±13.17%) in our datasets. Using Common Spatial Pattern (CSP) to extract 2 spatial filters as feature extraction method, and Fisher’s Linear Discriminant Analysis (FLDA) as classifier.
Source: Cho et al. (2017)
2.2.2 Kumar Et Al. (2019) — 10×10-Fold Cross-Validation
The source describes these results as subject-dependent 10×10-fold cross-validation misclassification rates. Cho2017 contains one recording session.
| Subject | CSP | DFBCSP | SBLFB | SFTOFSRC | OPTICAL* | OPTICAL |
|---|---|---|---|---|---|---|
| 1 | 27.30 ± 12.91 | 37.20 ± 9.54 | 37.20 ± 9.85 | 40.17 ± 10.63 | 20.70 ± 8.33 | 20.00 ± 9.37 |
| 2 | 49.00 ± 9.31 | 50.20 ± 11.56 | 44.10 ± 12.65 | 58.33 ± 8.64 | 45.70 ± 11.47 | 47.67 ± 12.37 |
| 3 | 11.40 ± 6.70 | 33.00 ± 10.15 | 8.40 ± 7.25 | 11.50 ± 6.97 | 8.60 ± 6.15 | 6.00 ± 5.78 |
| 4 | 39.10 ± 9.41 | 49.40 ± 13.46 | 19.40 ± 8.84 | 20.83 ± 10.99 | 24.10 ± 8.67 | 22.00 ± 10.05 |
| 5 | 1.00 ± 2.02 | 1.30 ± 2.22 | 1.00 ± 2.02 | 0.50 ± 1.53 | 0.90 ± 1.94 | 1.00 ± 2.03 |
| 6 | 20.20 ± 9.31 | 20.20 ± 8.02 | 17.50 ± 8.16 | 20.67 ± 6.40 | 16.60 ± 7.66 | 17.67 ± 7.74 |
| 7 | 50.42 ± 7.68 | 54.67 ± 10.02 | 45.17 ± 8.96 | 48.33 ± 10.92 | 47.75 ± 11.33 | 47.22 ± 7.92 |
| 8 | 49.60 ± 11.20 | 55.00 ± 11.78 | 53.90 ± 11.97 | 58.00 ± 9.43 | 43.80 ± 11.45 | 44.33 ± 11.65 |
| 9 | 49.50 ± 9.77 | 49.42 ± 11.67 | 44.33 ± 9.92 | 44.44 ± 9.75 | 43.75 ± 10.95 | 43.60 ± 10.07 |
| 10 | 42.70 ± 11.62 | 58.30 ± 8.84 | 36.40 ± 10.05 | 48.00 ± 9.06 | 30.90 ± 11.77 | 27.00 ± 10.31 |
| 11 | 45.80 ± 9.55 | 41.70 ± 9.98 | 49.80 ± 10.15 | 45.33 ± 7.98 | 49.30 ± 11.20 | 42.67 ± 9.89 |
| 12 | 31.00 ± 9.85 | 42.30 ± 8.82 | 37.40 ± 10.26 | 45.00 ± 10.51 | 34.50 ± 10.61 | 32.83 ± 8.97 |
| 13 | 11.10 ± 5.92 | 46.30 ± 9.57 | 11.50 ± 8.03 | 18.33 ± 8.64 | 14.60 ± 7.75 | 15.50 ± 8.24 |
| 14 | 4.80 ± 4.28 | 35.60 ± 10.63 | 5.20 ± 5.34 | 6.17 ± 5.20 | 4.70 ± 4.56 | 3.50 ± 3.51 |
| 15 | 49.90 ± 10.47 | 51.80 ± 11.94 | 33.20 ± 11.77 | 48.83 ± 13.75 | 43.00 ± 14.29 | 35.33 ± 11.29 |
| 16 | 51.50 ± 11.92 | 48.30 ± 11.00 | 51.60 ± 10.02 | 51.33 ± 10.25 | 52.10 ± 10.26 | 52.50 ± 11.89 |
| 17 | 51.80 ± 8.68 | 48.80 ± 9.88 | 47.20 ± 10.79 | 47.50 ± 10.06 | 50.20 ± 12.78 | 50.33 ± 9.46 |
| 18 | 49.00 ± 11.61 | 51.90 ± 12.81 | 41.10 ± 11.31 | 50.83 ± 9.83 | 49.30 ± 8.75 | 46.67 ± 8.34 |
| 19 | 45.50 ± 11.35 | 40.20 ± 9.20 | 35.40 ± 10.64 | 47.00 ± 12.36 | 42.90 ± 10.79 | 42.83 ± 9.16 |
| 20 | 36.20 ± 9.61 | 42.70 ± 11.83 | 48.70 ± 10.73 | 47.17 ± 11.12 | 28.30 ± 10.67 | 27.17 ± 10.14 |
| 21 | 45.70 ± 11.25 | 38.30 ± 10.53 | 35.20 ± 10.83 | 40.33 ± 9.73 | 40.20 ± 10.15 | 34.00 ± 11.33 |
| 22 | 45.70 ± 10.93 | 45.70 ± 9.69 | 43.40 ± 11.71 | 44.00 ± 11.33 | 46.60 ± 9.71 | 41.33 ± 10.08 |
| 23 | 32.60 ± 9.75 | 32.50 ± 10.66 | 25.40 ± 7.06 | 24.17 ± 9.11 | 19.80 ± 9.15 | 15.83 ± 8.31 |
| 24 | 53.30 ± 11.41 | 54.50 ± 12.71 | 49.40 ± 9.18 | 50.50 ± 9.94 | 46.90 ± 10.97 | 39.33 ± 10.23 |
| 25 | 56.70 ± 10.72 | 53.00 ± 12.78 | 46.90 ± 11.47 | 47.67 ± 10.73 | 49.00 ± 9.74 | 47.00 ± 14.18 |
| 26 | 4.20 ± 4.21 | 4.30 ± 3.91 | 3.30 ± 3.73 | 5.00 ± 3.94 | 3.70 ± 4.02 | 3.17 ± 3.34 |
| 27 | 52.50 ± 11.21 | 44.90 ± 11.72 | 51.00 ± 10.50 | 46.50 ± 11.00 | 57.00 ± 10.40 | 55.33 ± 9.99 |
| 28 | 20.30 ± 8.60 | 24.80 ± 7.49 | 21.00 ± 8.08 | 24.17 ± 10.35 | 19.50 ± 8.28 | 19.17 ± 4.93 |
| 29 | 54.80 ± 9.95 | 53.00 ± 11.87 | 57.70 ± 10.46 | 52.00 ± 10.55 | 56.30 ± 11.15 | 57.00 ± 10.88 |
| 30 | 44.00 ± 9.53 | 47.80 ± 10.16 | 40.70 ± 11.95 | 45.67 ± 10.06 | 44.80 ± 9.42 | 44.50 ± 10.45 |
| 31 | 45.00 ± 10.35 | 52.90 ± 11.78 | 38.20 ± 12.32 | 38.33 ± 6.61 | 38.60 ± 10.45 | 37.67 ± 10.97 |
| 32 | 49.60 ± 11.01 | 52.00 ± 10.93 | 51.60 ± 12.27 | 49.17 ± 10.67 | 49.60 ± 13.47 | 49.43 ± 11.99 |
| 33 | 48.90 ± 10.80 | 45.90 ± 10.63 | 46.20 ± 10.18 | 51.67 ± 11.24 | 47.20 ± 10.60 | 44.33 ± 12.23 |
| 34 | 44.10 ± 12.02 | 46.40 ± 9.95 | 45.60 ± 10.03 | 45.83 ± 10.35 | 42.70 ± 9.96 | 42.00 ± 7.50 |
| 35 | 18.90 ± 6.87 | 23.30 ± 8.96 | 27.70 ± 11.66 | 25.33 ± 9.91 | 18.40 ± 8.72 | 18.17 ± 9.33 |
| 36 | 47.30 ± 12.50 | 46.70 ± 9.72 | 44.90 ± 11.85 | 48.67 ± 10.82 | 33.70 ± 12.49 | 30.50 ± 10.20 |
| 37 | 26.60 ± 8.30 | 26.90 ± 9.94 | 26.00 ± 8.81 | 29.00 ± 8.65 | 23.80 ± 8.12 | 23.00 ± 10.20 |
| 38 | 53.40 ± 10.66 | 51.10 ± 10.02 | 51.70 ± 9.35 | 53.00 ± 9.52 | 52.40 ± 12.09 | 51.50 ± 12.12 |
| 39 | 28.60 ± 9.26 | 41.50 ± 10.22 | 29.80 ± 11.82 | 37.00 ± 9.25 | 28.20 ± 11.33 | 27.00 ± 9.25 |
| 40 | 48.60 ± 9.32 | 47.50 ± 10.99 | 53.10 ± 10.25 | 47.17 ± 7.62 | 48.40 ± 10.81 | 48.83 ± 9.16 |
| 41 | 25.70 ± 9.04 | 10.40 ± 6.05 | 19.60 ± 10.39 | 27.50 ± 7.74 | 15.90 ± 7.47 | 14.67 ± 8.80 |
| 42 | 56.00 ± 7.95 | 57.00 ± 10.83 | 51.00 ± 10.97 | 54.50 ± 8.84 | 51.10 ± 10.51 | 51.67 ± 10.11 |
| 43 | 6.70 ± 5.31 | 3.80 ± 4.47 | 3.60 ± 4.52 | 2.00 ± 2.82 | 4.90 ± 4.79 | 4.17 ± 4.17 |
| 44 | 10.60 ± 6.11 | 9.80 ± 6.54 | 10.70 ± 6.47 | 17.00 ± 8.37 | 8.40 ± 7.45 | 10.83 ± 7.55 |
| 45 | 46.20 ± 9.18 | 49.20 ± 10.27 | 49.40 ± 12.60 | 55.50 ± 11.62 | 45.70 ± 8.45 | 47.50 ± 10.97 |
| 46 | 30.58 ± 7.46 | 35.58 ± 9.72 | 24.92 ± 9.16 | 31.81 ± 9.44 | 24.67 ± 8.07 | 25.42 ± 8.71 |
| 47 | 27.10 ± 11.21 | 25.10 ± 8.36 | 29.10 ± 8.79 | 30.83 ± 11.07 | 22.00 ± 8.02 | 25.83 ± 9.83 |
| 48 | 44.00 ± 10.69 | 11.80 ± 7.68 | 19.00 ± 8.45 | 30.00 ± 8.41 | 35.30 ± 11.31 | 21.83 ± 9.69 |
| 49 | 10.50 ± 7.16 | 13.00 ± 6.70 | 10.40 ± 4.93 | 12.50 ± 8.59 | 13.70 ± 6.91 | 12.50 ± 6.66 |
| 50 | 0.00 ± 0.00 | 0.00 ± 0.00 | 0.00 ± 0.00 | 0.00 ± 0.00 | 0.00 ± 0.00 | 0.00 ± 0.00 |
| 51 | 50.40 ± 9.52 | 43.10 ± 11.29 | 47.10 ± 9.69 | 45.67 ± 9.89 | 47.90 ± 11.39 | 46.83 ± 10.71 |
| 52 | 42.50 ± 9.75 | 49.60 ± 10.59 | 39.50 ± 9.81 | 43.00 ± 9.15 | 40.10 ± 10.86 | 38.17 ± 13.10 |
| Average | 36.31 ± 9.14 | 38.46 ± 9.62 | 33.88 ± 9.38 | 36.80 ± 9.06 | 33.23 ± 9.38 | 31.81 ± 9.06 |
Source: Kumar et al. (2019)
2.3 Online / Real-Time
2.3.1 Kumar Et Al. (2019) — Real-Time Simulation
The source reports a simulated three-class real-time implementation. Values are misclassification rates (%).
| Subject | Real-time |
|---|---|
| 1 | 15.00 ± 9.05 |
| 2 | 23.25 ± 4.26 |
| 3 | 2.75 ± 2.19 |
| 4 | 14.50 ± 5.50 |
| 5 | 0.75 ± 1.21 |
| 6 | 8.00 ± 6.32 |
| 7 | 31.67 ± 7.20 |
| 8 | 24.00 ± 4.28 |
| 9 | 27.50 ± 4.89 |
| 10 | 14.25 ± 6.02 |
| 11 | 26.00 ± 6.69 |
| 12 | 15.75 ± 7.55 |
| 13 | 9.00 ± 2.93 |
| 14 | 1.50 ± 2.42 |
| 15 | 18.25 ± 7.91 |
| 16 | 26.75 ± 6.67 |
| 17 | 24.00 ± 4.12 |
| 18 | 24.00 ± 5.55 |
| 19 | 22.25 ± 5.95 |
| 20 | 12.00 ± 4.97 |
| 21 | 16.75 ± 6.46 |
| 22 | 20.75 ± 3.55 |
| 23 | 9.75 ± 3.22 |
| 24 | 23.25 ± 5.14 |
| 25 | 23.00 ± 4.97 |
| 26 | 1.50 ± 1.75 |
| 27 | 28.50 ± 5.30 |
| 28 | 12.00 ± 2.58 |
| 29 | 31.25 ± 7.38 |
| 30 | 22.25 ± 4.63 |
| 31 | 21.00 ± 3.57 |
| 32 | 24.75 ± 4.63 |
| 33 | 24.25 ± 5.66 |
| 34 | 24.50 ± 3.50 |
| 35 | 10.00 ± 2.89 |
| 36 | 19.50 ± 6.21 |
| 37 | 13.50 ± 6.15 |
| 38 | 33.50 ± 3.76 |
| 39 | 20.00 ± 6.12 |
| 40 | 31.00 ± 6.69 |
| 41 | 8.00 ± 2.30 |
| 42 | 27.25 ± 6.06 |
| 43 | 2.50 ± 2.36 |
| 44 | 5.25 ± 3.22 |
| 45 | 31.25 ± 8.76 |
| 46 | 12.70 ± 5.05 |
| 47 | 12.75 ± 5.46 |
| 48 | 11.50 ± 5.80 |
| 49 | 15.00 ± 6.12 |
| 50 | 0.00 ± 0.00 |
| 51 | 27.00 ± 5.11 |
| 52 | 19.00 ± 4.89 |
| Average | 17.78 ± 4.90 |
Source: Kumar et al. (2019)
2.4 Cross-Subject
2.4.1 Rodrigues Et Al. (2019) — RPA Transfer Learning
Results from: Rodrigues et al. (2019)
| Dataset | N | MEAN AUC - DCT | MEAN AUC - RCT | MEAN AUC - PRL | MEAN AUC - OPT | MEAN AUC - RPA |
|---|---|---|---|---|---|---|
| Cho2017 | 1 | 0.54 | 0.59 | 0.58 | 0.57 | 0.54 |
| Cho2017 | 5 | 0.55 | 0.61 | 0.61 | 0.57 | 0.59 |
| Cho2017 | 10 | 0.55 | 0.62 | 0.62 | 0.57 | 0.62 |
| Cho2017 | 25 | 0.57 | 0.64 | 0.64 | 0.58 | 0.66 |
2.4.2 Ouahidi Et Al. (2024) — Cross-Subject Ablation Study
The paper explicitly states that this ablation is conducted under the cross-subject evaluation paradigm. The Online row is its simulated-online ablation variant.
Results from: Ouahidi et al. (2024)
| Pipeline | BNCI | Zhou | Physionet | Cho | Average Gain |
|---|---|---|---|---|---|
| Full pipeline | 72.1 ± 7.3 | 81.8 ± 1.2 | 64.6 ± 4.4 | 75.4 ± 4.6 | — |
| - BN | 67.1 ± 8.4 | 77.3 ± 2.9 | 62.8 ± 4.2 | 74.5 ± 5.5 | −3.0 |
| - EA | 66.7 ± 6.5 | 80.5 ± 1.3 | 63.0 ± 4.6 | 74.6 ± 4.6 | −2.3 |
| - Session | 70.4 ± 7.2 | 79.9 ± 1.4 | — | — | −1.8 |
| Online | 58.4 ± 6.9 | 73.1 ± 6.6 | 61.0 ± 4.9 | 73.5 ± 5.3 | −6.8 |
| - Mixup | 69.1 ± 9.2 | 80.1 ± 0.7 | 60.0 ± 4.2 | 73.8 ± 5.2 | −2.7 |
| - Reg S | 71.4 ± 7.9 | 81.8 ± 1.0 | 63.6 ± 4.1 | 74.2 ± 4.4 | −0.72 |
| - Everything | 56.4 ± 9.0 | 74.5 ± 3.6 | 57.4 ± 4.6 | 72.6 ± 4.9 | −8.3 |
| + EOG | 79.8 ± 5.8 | 81.9 ± 1.6 | — | — | +3.9 |
| - LMSO | — | — | 65.0 ± 14.3 | 76.2 ± 10.3 | +0.6 |