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EEG-based Motor Imagery empowered by Subject Identification in a Multi-task and Multi-scale framework

Simone Bove, Martino Giaquinto*, Member, IEEE, Gennaro Percannella, Member, IEEE, Alessia Saggese, Member, IEEE, Mario Vento, Member, IEEE

All the authors are with the Department of Information and Electrical Engineering and Applied Mathematics (DIEM), University of Salerno, Fisciano, 84084 Italy.

*Corresponding author: mgiaquinto@unisa.it

All the authors contributed equally to this work.

Official repository for the paper "EEG-based Motor Imagery empowered by Subject Identification in a Multi-task and Multi-scale framework", introducing MIRACLE, a multi-task, multi-scale deep learning architecture for EEG-based Motor Imagery (MI) classification jointly trained with a Subject Identification (SI) auxiliary task.

Note: This repository contains only the code required to test/evaluate the pretrained MIRACLE model. Pretrained model weights and the preprocessed test datasets are not included in this repository and must be downloaded separately (see Download below).

Table of Contents

Overview

MIRACLE is designed to improve Motor Imagery decoding from EEG signals by leveraging subject-specific information through a multi-task learning framework, combined with a multi-scale, frequency-selective feature extraction strategy.

This repository provides the test/inference pipeline used to reproduce the evaluation results reported in the paper on the:

  • BCI Competition IV 2008 Dataset 2a
  • BCI Competition IV 2008 Dataset 2b
  • OpenBMI
  • PhysioNetMI

MIRACLE Architecture

The figure below shows an overview of the proposed MIRACLE architecture, comprising:

  • (a) Normalization block — based on Z-score standardization of the input EEG signals.
  • (b) Frequency-Selective Multi-Scale (FSMS) block — four parallel convolutional branches paired with Squeeze-and-Excitation (SE) modules, designed to capture discriminative patterns across multiple frequency scales.
  • (c) Feature-Selective Global Temporal (FSGT) block — integrates SE-based feature weighting with a Transformer encoder to model long-range temporal dependencies.
  • (d) Multi-Task Classification (MTC) block — two classification heads, one for Motor Imagery (MI) classification and one for Subject Identification (SI), trained jointly to improve generalization and subject-invariant/subject-aware feature learning.

MIRACLE architecture overview

Figure: Overview of the proposed MIRACLE architecture — (a) normalization, (b) FSMS block, (c) FSGT block, (d) MTC block.

Repository Structure

.
├── README.md
├── requirements.txt
├── MIRACLE.py                       # MIRACLE model definition (FSMS, FSGT, MTC blocks)
├── test_motor_imagery.py            # Test / inference entry point
├── preprocess_test_OpenBMI.py       # Merges the split OpenBMI test set into a single .npz file
├── utils.py                         # Data loading, normalization, metrics, losses, network factory
├── Test_Sets/                       # >>> downloaded test data goes here (flat .npz files, no subfolders) <<<
│   ├── test_2a.npz
│   ├── test_2b.npz
│   ├── test_OpenBMI.npz             # created by preprocess_test_OpenBMI.py after merging the two downloaded parts
│   └── test_PhysioNetMI.npz
└── Weights_Models/                  # >>> downloaded model weights archive extracts here, with these subfolders already included <<<
    ├── Results_2a/
    │   ├── MIRACLE_mean_data.pt
    │   ├── MIRACLE_std_data.pt
    │   ├── MIRACLE_seed71_validation_log.txt
    │   └── MIRACLE_seed71_best_model_fold{N}.pth
    ├── Results_2b/
    │   ├── MIRACLE_mean_data.pt
    │   ├── MIRACLE_std_data.pt
    │   ├── MIRACLE_seed157_validation_log.txt
    │   └── MIRACLE_seed157_best_model_fold{N}.pth
    ├── Results_OpenBMI/
    │   ├── MIRACLE_mean_data.pt
    │   ├── MIRACLE_std_data.pt
    │   ├── MIRACLE_seed149_validation_log.txt
    │   └── MIRACLE_seed149_best_model_fold{N}.pth
    └── Results_PhysioNetMI/
        ├── MIRACLE_mean_data.pt
        ├── MIRACLE_std_data.pt
        ├── MIRACLE_seed131_validation_log.txt
        └── MIRACLE_seed131_best_model_fold{N}.pth

Requirements

  • Python 3.9
  • PyTorch
  • einops
  • NumPy
  • scikit-learn
  • Matplotlib
  • Seaborn

Install all dependencies with:

pip install -r requirements.txt

Download

Since the pretrained MIRACLE model weights and the preprocessed test datasets are not hosted in this repository, they must be downloaded from the corresponding release before running the test scripts:

  • 🔗 Model weights release

  • 🔗 Test sets release

  • Model weights: distributed as a single archive that already contains the correct subfolder structure (Weights_Models/Results_2a/, Weights_Models/Results_2b/, Weights_Models/Results_OpenBMI/, Weights_Models/Results_PhysioNetMI/). Simply extract it in the repository root — no manual reorganization needed.

  • Test datasets: distributed as individual .npz files. Create a Test_Sets/ folder in the repository root (if not already present) and place all downloaded test files directly inside it (flat, no subfolders):

Test_Sets/test_2a.npz
Test_Sets/test_2b.npz
Test_Sets/test_OpenBMI_first.npz
Test_Sets/test_OpenBMI_last.npz
Test_Sets/test_PhysioNetMI.npz

Note on the OpenBMI test set: since it exceeds 2GB, it is distributed split into two parts, test_OpenBMI_first.npz and test_OpenBMI_last.npz. After placing both inside Test_Sets/, merge them into a single test_OpenBMI.npz file by running:

python preprocess_test_OpenBMI.py

This concatenates the two parts into Test_Sets/test_OpenBMI.npz and automatically deletes the two original split files. All other test sets (2a, 2b, PhysioNetMI) are ready to use as downloaded, with no preprocessing needed.

Once everything is extracted and placed as described, the folder layout should match Repository Structure above.

Datasets

Evaluation is performed on the following public EEG Motor Imagery benchmarks:

  • BCI Competition IV — Dataset 2a: 4-class Motor Imagery (left hand, right hand, feet, tongue), recorded from 9 subjects using 22 EEG channels.
  • BCI Competition IV — Dataset 2b: 2-class Motor Imagery (left hand, right hand), recorded from 9 subjects using 3 EEG channels.
  • OpenBMI: large-scale public EEG Motor Imagery dataset (Lee et al., 2019), 2-class Motor Imagery (left hand, right hand), recorded from 54 subjects using 62 EEG channels.
  • PhysioNetMI: EEG Motor Movement/Imagery dataset (Schalk et al., 2004), 2-class Motor Imagery (left hand, right hand), evaluated here on 106 subjects using 64 EEG channels.

Only the test partitions required to reproduce the paper's results are provided via the download link above; the raw/original datasets are publicly available from the respective official sources for research purposes.

During training, the MI and SI task losses are combined with a weighting coefficient α, set to 0.001 for Dataset 2a, 0.01 for Dataset 2b, 0.001 for OpenBMI, and 0.1 for PhysioNetMI.

Results

The tables below report the per-subject classification accuracy (%) obtained on the test sets, across all the seeds evaluated for each dataset. For simplicity, only the model weights of the best-performing seed per dataset are made available for download (highlighted in bold below): seed 71 for Dataset 2a and seed 157 for Dataset 2b.

Dataset 2a (α = 0.001)

Seed S1 S2 S3 S4 S5 S6 S7 S8 S9 Average
42 87.15 64.24 94.44 77.78 54.51 68.06 87.85 85.76 78.12 77.55
71 (best) 86.81 70.49 94.44 81.94 67.01 70.14 87.50 86.81 77.78 80.32
101 86.46 68.75 92.71 79.17 45.49 65.97 81.94 84.72 82.64 76.43
113 85.76 66.32 94.44 77.78 61.11 67.71 90.28 86.81 84.38 79.40
127 86.46 59.37 95.14 77.43 75.69 65.97 85.76 82.64 77.78 78.47
131 84.03 58.33 93.75 78.12 71.87 67.36 89.24 85.07 74.31 78.01
139 85.07 58.68 93.75 78.12 57.99 64.58 89.93 86.11 77.78 76.89
149 86.11 65.28 95.49 76.74 48.96 70.14 90.28 88.19 82.99 78.24
157 86.46 58.33 94.79 79.17 54.51 69.44 85.42 86.81 86.11 77.89
163 84.38 61.46 94.79 73.61 54.86 67.01 89.24 82.99 79.17 76.39
173 86.81 58.33 93.40 73.61 52.08 64.58 73.61 87.50 82.64 74.73
181 85.76 67.36 95.49 78.82 52.43 64.93 81.60 85.07 80.90 76.93
322 85.42 64.58 95.14 81.94 48.96 67.71 86.11 87.15 80.21 77.47
521 82.99 63.19 94.10 77.78 61.46 69.10 88.54 84.38 81.25 78.09
Average 85.69 63.19 94.42 78.00 57.64 67.34 86.24 85.72 80.43 77.63

Values are per-subject classification accuracy (%) on the MI task test set. The bolded row (seed 71) is the one whose model weights are provided via the Download link and used in the Usage example commands below.

Dataset 2b (α = 0.01)

Seed S1 S2 S3 S4 S5 S6 S7 S8 S9 Average
42 78.75 73.21 82.19 93.44 97.50 85.00 91.87 96.56 87.19 87.30
71 78.12 72.14 82.50 94.69 99.06 82.19 91.87 96.25 89.06 87.32
101 79.37 72.14 84.38 95.00 98.44 88.75 91.87 96.88 87.19 88.22
113 78.12 72.50 83.44 96.25 98.44 88.12 92.81 95.63 86.56 87.99
127 79.37 71.07 82.81 94.38 97.50 85.31 92.50 95.94 88.44 87.48
131 77.81 70.36 82.19 95.63 96.88 86.87 91.25 95.94 86.87 87.09
139 75.00 71.43 83.75 89.69 97.81 87.50 93.75 96.25 88.12 87.03
149 78.44 69.64 84.69 95.94 98.12 83.44 91.56 95.31 87.81 87.22
157 (best) 79.37 74.29 86.25 93.44 97.50 89.06 92.19 97.19 89.69 88.78
163 78.44 72.86 85.31 94.69 98.12 85.62 91.25 96.25 87.50 87.78
173 79.06 71.07 82.19 95.63 97.81 84.06 92.50 95.94 88.13 87.38
181 77.50 71.43 84.38 93.44 98.12 88.13 92.81 95.94 87.81 87.73
322 76.56 72.14 85.31 95.00 98.12 87.50 92.19 96.56 89.06 88.05
521 81.25 73.57 85.00 95.63 97.81 86.25 92.81 95.31 87.81 88.38
Average 78.37 71.99 83.88 94.49 97.94 86.27 92.23 96.14 87.95 87.70

Values are per-subject classification accuracy (%) on the MI task test set. The bolded row (seed 157) is the one whose model weights are provided via the Download link and used in the Usage example commands below.

Dataset OpenBMI (α = 0.001)

Subject 42 71 101 113 127 131 139 149 157 163 173 181 322 521 Average
S1 87.00 84.50 85.00 87.00 83.00 85.50 86.00 83.50 85.50 84.50 81.00 85.00 86.00 82.00 84.68
S2 79.00 84.00 80.50 81.50 84.00 78.50 84.50 82.50 79.50 80.00 81.00 80.00 81.00 82.00 81.29
S3 98.50 98.00 97.00 98.50 97.00 96.00 98.50 99.00 99.50 97.50 97.00 98.00 98.00 97.50 97.86
S4 87.50 86.50 80.50 83.50 88.00 87.00 86.50 90.50 84.50 88.50 86.00 89.00 91.00 90.00 87.07
S5 86.00 89.50 89.50 88.00 92.00 87.50 92.00 90.00 87.50 85.00 87.50 86.50 88.50 89.00 88.46
S6 99.00 97.50 98.00 97.50 99.50 98.00 98.00 99.00 98.00 99.00 98.00 98.50 99.50 98.50 98.43
S7 83.50 85.00 80.00 85.00 86.50 83.50 86.00 85.00 84.50 86.50 86.00 88.00 84.00 87.50 85.07
S8 88.00 87.50 86.00 87.50 88.00 85.00 91.50 88.50 89.50 86.50 87.00 85.00 86.00 85.50 87.25
S9 83.50 82.50 85.50 82.50 82.50 82.50 85.00 82.00 85.50 82.50 82.50 82.00 84.00 84.50 83.36
S10 68.00 66.50 67.00 71.50 68.50 67.00 73.50 68.00 72.00 67.00 66.00 67.50 72.50 69.00 68.86
S11 73.00 73.00 73.00 72.50 73.00 73.00 72.00 75.50 72.50 74.00 76.00 73.00 73.00 73.50 73.36
S12 86.00 84.00 86.00 84.50 82.00 82.00 83.00 83.00 84.00 83.00 83.00 87.00 85.00 81.50 83.86
S13 77.00 78.50 71.50 70.50 73.50 78.50 70.50 79.50 77.00 72.00 73.00 74.50 71.00 74.50 74.39
S14 72.00 62.00 72.00 76.00 66.50 68.50 70.00 72.50 68.00 67.50 81.00 71.50 74.00 65.50 70.50
S15 91.00 85.00 89.00 87.50 86.00 90.00 83.50 89.50 87.00 90.00 84.50 88.00 89.00 88.50 87.75
S16 93.00 91.50 92.00 90.50 92.50 93.50 92.50 92.00 92.50 90.00 91.50 91.50 90.50 92.00 91.82
S17 75.00 70.00 75.50 71.50 74.00 71.00 76.00 72.50 71.00 71.50 69.00 67.00 69.50 68.50 71.57
S18 94.50 89.00 94.50 89.50 94.00 94.00 93.50 95.50 96.00 87.50 93.50 96.00 92.00 93.00 93.04
S19 84.50 82.50 88.50 82.00 88.00 84.50 86.00 86.00 85.00 85.00 86.00 88.50 84.50 83.50 85.32
S20 94.50 89.50 92.00 92.00 93.00 93.50 91.50 92.00 93.00 93.00 91.50 94.00 91.50 93.00 92.43
S21 99.00 99.50 100.00 99.00 100.00 100.00 100.00 100.00 99.50 99.50 98.50 98.50 100.00 100.00 99.54
S22 83.50 85.50 84.50 83.00 84.50 84.00 85.50 84.50 84.50 81.00 84.00 85.50 79.50 84.00 83.82
S23 75.50 74.00 76.00 76.00 76.00 69.50 71.50 67.50 74.00 79.00 74.50 74.00 73.00 70.00 73.61
S24 60.50 55.00 65.00 62.50 53.50 60.50 56.50 59.50 61.00 61.50 66.00 60.50 63.00 61.00 60.43
S25 99.00 99.00 99.50 98.50 98.00 99.50 97.50 98.00 99.00 99.00 99.50 99.00 99.00 99.00 98.82
S26 83.00 86.00 85.50 82.00 86.00 85.50 85.50 84.00 86.50 85.50 89.00 85.00 85.50 87.00 85.43
S27 94.50 93.50 93.50 93.00 86.50 94.00 91.00 94.50 92.50 94.00 95.00 92.50 93.50 94.00 93.00
S28 96.50 96.00 97.00 98.50 93.50 97.50 95.00 96.00 96.00 97.50 96.00 97.50 98.00 95.50 96.46
S29 86.50 90.50 85.00 88.00 92.00 86.00 87.50 90.00 87.00 87.50 87.50 89.50 86.50 88.50 88.00
S30 75.00 73.00 74.50 76.50 74.00 73.50 75.00 76.50 78.50 72.00 74.00 76.50 74.00 75.00 74.86
S31 87.00 86.00 87.00 85.00 85.50 86.50 87.00 86.00 83.00 84.50 86.50 85.50 85.00 88.00 85.89
S32 96.00 97.50 98.00 98.00 97.00 97.00 98.50 98.00 97.50 97.50 97.50 96.50 97.50 96.50 97.36
S33 98.50 98.50 98.00 97.50 98.50 98.00 98.00 99.00 98.00 98.00 98.00 98.50 99.00 98.50 98.29
S34 66.00 66.00 69.50 67.00 71.50 62.50 65.50 67.50 67.00 62.50 70.00 67.00 66.00 66.50 66.75
S35 91.50 95.00 93.00 93.50 92.00 92.50 94.50 95.50 95.00 93.50 93.50 93.50 93.00 93.50 93.54
S36 99.00 99.00 98.50 100.00 98.50 99.50 98.50 98.50 99.00 99.00 100.00 98.50 99.50 100.00 99.11
S37 97.00 97.00 95.50 96.50 97.00 93.00 98.00 98.00 96.50 96.00 97.50 96.00 96.50 98.50 96.64
S38 73.50 79.00 72.50 71.00 74.50 74.00 81.50 81.00 80.00 70.50 78.00 84.50 77.00 80.50 76.96
S39 89.00 90.00 87.50 88.50 86.50 90.00 90.00 90.00 88.50 87.00 88.00 89.00 90.00 89.50 88.82
S40 82.50 81.50 80.50 80.50 85.00 79.00 83.50 83.00 78.50 82.50 79.50 89.00 78.50 83.50 81.93
S41 75.00 77.50 77.50 79.00 79.00 76.00 82.50 80.50 79.00 78.00 82.50 80.50 80.50 82.50 79.29
S42 76.50 77.50 75.50 76.50 77.00 77.00 74.00 74.00 77.00 76.00 76.50 76.00 76.00 74.50 76.00
S43 89.50 87.50 86.50 87.00 89.50 90.00 89.00 93.50 87.50 88.00 84.00 90.00 88.50 90.50 88.64
S44 95.50 94.00 95.50 95.50 91.50 95.50 93.00 93.50 96.00 95.50 95.00 95.50 96.00 94.00 94.71
S45 90.00 93.00 91.00 92.50 90.50 93.50 91.50 89.50 90.00 92.00 88.50 92.00 92.50 91.50 91.29
S46 85.50 81.50 83.50 86.50 81.00 82.50 84.00 85.50 83.00 84.50 85.00 84.00 81.00 84.50 83.71
S47 88.00 91.00 92.50 91.00 91.50 93.00 93.00 91.50 92.00 89.50 94.00 96.50 87.00 93.50 91.71
S48 82.00 83.00 82.50 84.50 83.00 78.50 79.00 83.00 81.00 81.50 82.00 85.50 80.50 83.00 82.07
S49 89.50 92.50 86.50 85.50 88.50 87.50 89.50 93.50 88.50 90.00 83.50 89.50 90.00 89.00 88.82
S50 55.50 62.00 61.50 62.50 65.50 57.00 59.00 61.00 61.50 53.00 65.50 56.50 57.50 61.00 59.93
S51 83.50 83.50 84.00 83.00 84.00 81.00 84.50 82.50 86.00 84.50 83.50 82.00 85.00 82.50 83.54
S52 90.00 90.50 90.00 92.00 90.50 92.00 89.50 92.00 89.50 88.50 93.00 91.00 89.50 92.00 90.71
S53 79.50 80.50 79.00 82.00 80.00 79.50 79.50 76.00 79.00 78.50 80.50 78.00 82.00 84.00 79.86
S54 75.50 75.00 76.00 73.00 72.00 74.50 72.00 72.50 73.00 72.50 74.00 76.00 74.50 71.00 73.68
Average 84.98 84.76 84.91 84.89 84.91 84.42 85.19 85.58 85.12 84.26 85.20 85.56 84.91 85.21 84.99

Values are per-subject classification accuracy (%) on the MI task test set; subjects are listed as rows and seeds as columns (the opposite orientation of the BCI IV 2a/2b tables above, given the larger number of subjects). The bolded column (seed 149) is the one whose model weights are provided via the Download link and used in the Usage example commands below.

Dataset PhysioNetMI (α = 0.1)

Subject 42 71 101 113 127 131 139 149 157 163 173 181 322 521 Average
S1 92.86 86.61 86.61 86.61 86.61 86.61 86.61 86.61 86.61 86.61 92.86 92.86 86.61 86.61 87.95
S2 86.61 80.36 85.71 85.71 86.61 85.71 93.75 85.71 100.00 85.71 100.00 79.46 100.00 86.61 88.71
S3 80.36 74.11 74.11 67.86 74.11 80.36 74.11 86.61 67.86 74.11 80.36 80.36 74.11 80.36 76.34
S4 100.00 93.75 100.00 100.00 100.00 100.00 92.86 92.86 87.50 100.00 100.00 92.86 100.00 100.00 97.13
S5 54.46 66.96 54.46 54.46 60.71 67.86 66.96 48.21 66.96 54.46 54.46 48.21 55.36 54.46 57.71
S6 92.86 92.86 92.86 100.00 100.00 100.00 92.86 85.71 92.86 100.00 92.86 92.86 85.71 100.00 94.39
S7 100.00 100.00 100.00 100.00 100.00 100.00 100.00 100.00 100.00 100.00 100.00 100.00 100.00 100.00 100.00
S8 87.50 87.50 93.75 87.50 93.75 93.75 93.75 93.75 93.75 87.50 87.50 93.75 86.61 87.50 90.56
S9 87.50 80.36 66.96 79.46 74.11 74.11 87.50 79.46 81.25 87.50 87.50 79.46 73.21 87.50 80.42
S10 73.21 66.07 86.61 66.07 73.21 73.21 79.46 66.07 80.36 73.21 80.36 72.32 80.36 86.61 75.51
S11 78.57 93.75 87.50 85.71 85.71 85.71 85.71 85.71 85.71 92.86 100.00 78.57 85.71 92.86 87.43
S12 80.36 86.61 92.86 86.61 86.61 92.86 92.86 92.86 92.86 92.86 86.61 86.61 80.36 86.61 88.40
S13 67.86 59.82 67.86 79.46 66.96 73.21 74.11 75.00 54.46 60.71 73.21 59.82 66.96 75.00 68.17
S14 80.36 74.11 86.61 73.21 67.86 86.61 86.61 86.61 87.50 93.75 74.11 93.75 85.71 93.75 83.61
S15 100.00 100.00 100.00 100.00 100.00 100.00 100.00 100.00 100.00 100.00 100.00 100.00 100.00 100.00 100.00
S16 65.18 57.14 71.43 64.29 64.29 51.79 50.89 78.57 78.57 73.21 57.14 66.07 68.75 57.14 64.60
S17 92.86 92.86 100.00 85.71 85.71 92.86 100.00 100.00 92.86 92.86 85.71 86.61 92.86 85.71 91.90
S18 100.00 100.00 100.00 100.00 100.00 100.00 100.00 100.00 100.00 100.00 100.00 100.00 100.00 100.00 100.00
S19 86.61 86.61 80.36 79.46 79.46 86.61 81.25 86.61 87.50 80.36 79.46 79.46 86.61 73.21 82.40
S20 92.86 92.86 92.86 100.00 92.86 92.86 100.00 92.86 92.86 100.00 92.86 85.71 92.86 92.86 93.88
S21 61.61 62.50 55.36 55.36 62.50 61.61 68.75 62.50 62.50 62.50 68.75 55.36 81.25 62.50 63.08
S22 100.00 100.00 100.00 100.00 100.00 100.00 100.00 100.00 100.00 100.00 100.00 100.00 100.00 100.00 100.00
S23 85.71 85.71 92.86 85.71 86.61 78.57 78.57 80.36 80.36 86.61 85.71 85.71 85.71 86.61 84.63
S24 68.75 55.36 67.86 80.36 75.00 62.50 67.86 81.25 67.86 67.86 62.50 61.61 74.11 68.75 68.69
S25 87.50 55.36 50.00 75.00 68.75 68.75 75.00 62.50 62.50 62.50 56.25 80.36 81.25 68.75 68.18
S26 33.04 32.14 38.39 39.29 21.43 26.79 27.68 26.79 20.54 33.04 32.14 25.89 25.89 26.79 29.27
S27 80.36 74.11 73.21 66.96 74.11 66.96 73.21 74.11 74.11 66.96 74.11 73.21 79.46 79.46 73.60
S28 79.46 73.21 66.96 87.50 66.96 86.61 73.21 66.96 74.11 74.11 80.36 73.21 66.96 79.46 74.93
S29 85.71 85.71 92.86 85.71 85.71 85.71 85.71 85.71 85.71 86.61 85.71 85.71 85.71 92.86 86.80
S30 86.61 86.61 86.61 80.36 80.36 86.61 80.36 80.36 80.36 80.36 86.61 86.61 86.61 86.61 83.93
S31 93.75 93.75 86.61 87.50 86.61 93.75 87.50 93.75 93.75 93.75 93.75 93.75 80.36 86.61 90.37
S32 100.00 100.00 93.75 100.00 100.00 92.86 92.86 92.86 100.00 92.86 100.00 100.00 92.86 92.86 96.49
S33 93.75 75.00 93.75 93.75 93.75 100.00 87.50 100.00 100.00 100.00 100.00 100.00 93.75 93.75 94.64
S34 71.43 71.43 85.71 78.57 78.57 71.43 57.14 85.71 78.57 85.71 92.86 64.29 100.00 78.57 78.57
S35 78.57 78.57 78.57 78.57 78.57 78.57 78.57 71.43 78.57 85.71 85.71 78.57 78.57 78.57 79.08
S36 74.11 74.11 80.36 80.36 80.36 80.36 74.11 80.36 80.36 74.11 74.11 74.11 74.11 74.11 76.79
S37 92.86 92.86 92.86 71.43 92.86 85.71 92.86 85.71 85.71 57.14 85.71 92.86 71.43 85.71 84.69
S38 67.86 60.71 56.25 75.00 66.96 73.21 56.25 56.25 48.21 49.11 66.07 55.36 62.50 62.50 61.16
S39 59.82 81.25 81.25 66.96 80.36 81.25 81.25 81.25 80.36 81.25 66.96 81.25 66.96 81.25 76.53
S40 100.00 100.00 100.00 100.00 93.75 100.00 93.75 100.00 100.00 100.00 100.00 100.00 93.75 100.00 98.66
S41 100.00 100.00 100.00 93.75 93.75 93.75 93.75 93.75 93.75 100.00 93.75 100.00 100.00 100.00 96.88
S42 85.71 92.86 85.71 92.86 85.71 85.71 86.61 92.86 92.86 87.50 92.86 85.71 92.86 78.57 88.46
S43 86.61 79.46 78.57 86.61 85.71 86.61 79.46 93.75 72.32 72.32 93.75 79.46 93.75 79.46 83.42
S44 87.50 80.36 87.50 87.50 87.50 80.36 80.36 87.50 87.50 87.50 87.50 81.25 80.36 87.50 85.01
S45 54.46 60.71 60.71 61.61 60.71 60.71 60.71 67.86 67.86 67.86 60.71 61.61 60.71 60.71 61.92
S46 60.71 52.68 66.07 52.68 59.82 66.96 52.68 60.71 66.96 60.71 74.11 52.68 59.82 59.82 60.46
S47 85.71 92.86 85.71 86.61 86.61 81.25 81.25 75.00 75.00 87.50 79.46 86.61 79.46 93.75 84.06
S48 92.86 100.00 100.00 92.86 100.00 100.00 100.00 100.00 100.00 100.00 100.00 100.00 100.00 92.86 98.47
S49 80.36 80.36 86.61 100.00 87.50 80.36 86.61 100.00 92.86 86.61 93.75 87.50 80.36 100.00 88.78
S50 80.36 79.46 85.71 80.36 87.50 93.75 72.32 66.07 80.36 80.36 79.46 71.43 71.43 87.50 79.72
S51 64.29 71.43 78.57 78.57 86.61 92.86 79.46 100.00 85.71 92.86 78.57 92.86 66.96 92.86 82.97
S52 100.00 100.00 100.00 100.00 100.00 100.00 92.86 100.00 100.00 100.00 100.00 100.00 92.86 100.00 98.98
S53 93.75 100.00 100.00 100.00 93.75 100.00 100.00 100.00 93.75 93.75 100.00 100.00 100.00 93.75 97.77
S54 100.00 92.86 100.00 93.75 100.00 100.00 100.00 100.00 93.75 100.00 100.00 93.75 86.61 100.00 97.19
S55 86.61 86.61 86.61 92.86 86.61 92.86 86.61 92.86 92.86 92.86 86.61 92.86 86.61 92.86 89.73
S56 100.00 92.86 92.86 100.00 92.86 100.00 100.00 100.00 100.00 85.71 100.00 92.86 85.71 100.00 95.92
S57 93.75 93.75 93.75 93.75 93.75 100.00 93.75 93.75 100.00 93.75 93.75 93.75 93.75 93.75 94.64
S58 87.50 80.36 85.71 80.36 80.36 86.61 86.61 87.50 86.61 87.50 87.50 86.61 93.75 87.50 86.03
S59 59.82 51.79 58.93 73.21 52.68 52.68 66.07 52.68 58.93 66.07 58.93 66.07 65.18 66.07 60.65
S60 93.75 93.75 86.61 100.00 93.75 93.75 93.75 100.00 93.75 93.75 93.75 100.00 93.75 100.00 95.03
S61 100.00 100.00 100.00 92.86 100.00 92.86 92.86 92.86 92.86 100.00 92.86 100.00 92.86 100.00 96.43
S62 100.00 100.00 100.00 92.86 100.00 100.00 100.00 100.00 93.75 100.00 100.00 100.00 100.00 100.00 99.04
S63 58.93 66.96 65.18 73.21 73.21 73.21 66.07 65.18 58.93 73.21 74.11 73.21 65.18 65.18 67.98
S64 64.29 57.14 50.00 50.00 50.00 57.14 50.00 50.00 57.14 50.00 50.00 57.14 57.14 57.14 54.08
S65 81.25 81.25 87.50 87.50 87.50 87.50 87.50 87.50 87.50 80.36 87.50 87.50 86.61 87.50 86.03
S66 65.18 72.32 66.07 80.36 80.36 80.36 80.36 73.21 73.21 73.21 66.07 79.46 74.11 80.36 74.62
S67 52.68 51.79 64.29 64.29 57.14 64.29 64.29 64.29 64.29 57.14 64.29 39.29 50.89 64.29 58.80
S68 87.50 81.25 87.50 81.25 81.25 87.50 87.50 81.25 81.25 87.50 81.25 75.00 81.25 87.50 83.48
S69 79.46 93.75 100.00 93.75 92.86 93.75 86.61 93.75 86.61 92.86 79.46 85.71 78.57 79.46 88.33
S70 100.00 100.00 100.00 100.00 100.00 100.00 100.00 100.00 100.00 100.00 100.00 100.00 100.00 100.00 100.00
S71 73.21 87.50 80.36 87.50 86.61 87.50 73.21 87.50 93.75 87.50 73.21 80.36 80.36 93.75 83.74
S72 50.89 58.04 65.18 79.46 79.46 72.32 72.32 66.07 72.32 73.21 79.46 79.46 72.32 72.32 70.92
S73 78.57 71.43 78.57 78.57 64.29 71.43 64.29 71.43 64.29 64.29 71.43 71.43 78.57 71.43 71.43
S74 71.43 85.71 78.57 71.43 85.71 78.57 71.43 85.71 92.86 71.43 71.43 64.29 78.57 78.57 77.55
S75 73.21 86.61 79.46 87.50 66.07 73.21 80.36 66.96 92.86 73.21 93.75 73.21 72.32 72.32 77.93
S76 71.43 78.57 71.43 78.57 85.71 78.57 71.43 85.71 85.71 71.43 78.57 78.57 85.71 78.57 78.57
S77 65.18 72.32 79.46 72.32 80.36 73.21 65.18 78.57 72.32 86.61 79.46 72.32 79.46 86.61 75.96
S78 73.21 67.86 66.96 66.96 60.71 74.11 66.96 67.86 66.96 72.32 66.96 79.46 80.36 80.36 70.79
S79 78.57 72.32 86.61 86.61 72.32 72.32 78.57 72.32 86.61 72.32 71.43 78.57 85.71 72.32 77.61
S80 93.75 92.86 87.50 100.00 100.00 100.00 93.75 87.50 87.50 86.61 93.75 86.61 100.00 93.75 93.11
S81 79.46 93.75 93.75 86.61 93.75 93.75 93.75 93.75 93.75 93.75 92.86 86.61 86.61 93.75 91.14
S82 86.61 86.61 86.61 86.61 80.36 92.86 86.61 92.86 86.61 85.71 86.61 86.61 86.61 79.46 86.48
S83 65.18 58.93 68.75 58.93 73.21 59.82 59.82 74.11 73.21 61.61 44.64 78.57 60.71 66.07 64.54
S84 87.50 68.75 75.00 81.25 81.25 87.50 75.00 75.00 75.00 75.00 87.50 87.50 67.86 93.75 79.85
S85 100.00 100.00 100.00 100.00 100.00 100.00 100.00 100.00 100.00 93.75 100.00 93.75 100.00 100.00 99.11
S86 100.00 100.00 100.00 100.00 100.00 100.00 100.00 100.00 100.00 100.00 100.00 100.00 100.00 100.00 100.00
S87 52.68 46.43 41.07 53.57 52.68 53.57 47.32 33.93 54.46 49.11 46.43 47.32 41.07 40.18 47.13
S88 79.46 73.21 79.46 73.21 73.21 74.11 80.36 80.36 80.36 74.11 79.46 73.21 80.36 86.61 77.68
S89 93.75 100.00 93.75 93.75 100.00 100.00 93.75 100.00 92.86 100.00 100.00 100.00 92.86 93.75 96.75
S90 100.00 93.75 93.75 93.75 87.50 93.75 92.86 86.61 93.75 93.75 93.75 100.00 87.50 86.61 92.67
S91 72.32 78.57 78.57 72.32 72.32 72.32 72.32 78.57 78.57 78.57 78.57 72.32 72.32 78.57 75.44
S92 75.00 100.00 100.00 87.50 87.50 93.75 81.25 81.25 93.75 87.50 93.75 81.25 87.50 93.75 88.84
S93 33.04 50.89 44.64 51.79 46.43 39.29 44.64 39.29 51.79 45.54 53.57 47.32 59.82 38.39 46.17
S94 93.75 93.75 100.00 100.00 93.75 100.00 93.75 100.00 87.50 93.75 93.75 93.75 93.75 87.50 94.64
S95 93.75 87.50 93.75 87.50 79.46 80.36 79.46 93.75 86.61 87.50 87.50 93.75 93.75 86.61 87.95
S96 41.07 45.54 33.93 33.04 45.54 39.29 40.18 33.04 26.79 56.25 39.29 47.32 47.32 40.18 40.63
S97 72.32 87.50 73.21 87.50 87.50 73.21 66.07 81.25 73.21 52.68 79.46 80.36 81.25 59.82 75.38
S98 93.75 93.75 93.75 100.00 100.00 100.00 100.00 93.75 100.00 100.00 100.00 93.75 87.50 87.50 95.98
S99 71.43 64.29 71.43 64.29 71.43 71.43 64.29 71.43 64.29 71.43 71.43 64.29 64.29 71.43 68.37
S100 93.75 93.75 93.75 93.75 93.75 100.00 87.50 100.00 93.75 87.50 100.00 93.75 87.50 87.50 93.30
S101 80.36 74.11 74.11 73.21 67.86 74.11 74.11 80.36 80.36 79.46 80.36 80.36 74.11 74.11 76.21
S102 87.50 81.25 93.75 93.75 100.00 93.75 81.25 81.25 93.75 93.75 93.75 87.50 100.00 87.50 90.62
S103 73.21 66.96 72.32 72.32 66.96 73.21 73.21 73.21 66.96 66.07 66.96 59.82 66.07 59.82 68.36
S104 53.57 60.71 46.43 66.96 52.68 46.43 45.54 46.43 53.57 51.79 54.46 46.43 45.54 60.71 52.23
S105 80.36 86.61 80.36 80.36 80.36 86.61 86.61 80.36 74.11 86.61 86.61 74.11 92.86 74.11 82.15
S106 66.96 58.04 85.71 65.18 73.21 71.43 72.32 65.18 79.46 65.18 72.32 71.43 71.43 73.21 70.79
Average 80.57 80.33 81.85 82.08 81.43 82.47 80.27 81.78 81.92 81.28 82.40 80.80 81.09 82.09 81.45

Values are per-subject classification accuracy (%) on the MI task test set; subjects are listed as rows and seeds as columns (the opposite orientation of the BCI IV 2a/2b tables above, given the larger number of subjects). The bolded column (seed 131) is the one whose model weights are provided via the Download link and used in the Usage example commands below.

Usage

Once models and datasets have been downloaded and placed in the correct folders (remember to merge the OpenBMI test set first, see Download), run the test script specifying the test set, the saved-results folder, and the seed of the model weights to evaluate:

python test_motor_imagery.py \
  --test_set Test_Sets/test_2a.npz \
  --name_model MIRACLE \
  --saved_path Weights_Models/Results_2a \
  --seed 71 \
  --paradigm Cross
python test_motor_imagery.py \
  --test_set Test_Sets/test_2b.npz \
  --name_model MIRACLE \
  --saved_path Weights_Models/Results_2b \
  --seed 157 \
  --paradigm Cross
python test_motor_imagery.py \
  --test_set Test_Sets/test_OpenBMI.npz \
  --name_model MIRACLE \
  --saved_path Weights_Models/Results_OpenBMI \
  --seed 149 \
  --paradigm Cross
python test_motor_imagery.py \
  --test_set Test_Sets/test_PhysioNetMI.npz \
  --name_model MIRACLE \
  --saved_path Weights_Models/Results_PhysioNetMI \
  --seed 131 \
  --paradigm Cross

Results (F1, accuracy, balanced accuracy, and Cohen's kappa for both the MI and SI tasks) are saved to {saved_path}/Final_results_{name_model}_seed{seed}.json.

Note: This paper is currently under review. Citation details will be added once the work is accepted and published.

Citation

If you find this repository useful in your research, please consider citing our paper (citation will be added after publication).

Contact

For questions or further information, please contact the authors of the paper.

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