Subject_independent_MI_classification_paper
Experimental setup : Leave-one-subject-out (LOSO) is widely used.
Title |
Author |
Date |
Publication |
Link |
Accuracy |
Dataset |
Model |
MIN2Net : End-to-End Multi-Task Learning for Subject-Independent Motor Imagery EEG Classification |
Phairot Autthansan, et al. |
June-2022 |
IEEE Transactions on Biomedical Engineering |
Paper Code |
60.03% 59.79% 72.03% |
BCIC_IV_2a SMR-BCI OpenBMI |
AE CNN triplet |
Subject-independent Brain-Computer Interfaces Based on Deep Convolutional Neural Networks |
O-Yeon Kwon, et al. |
Oct-2020 |
IEEE Transactions on Neural Networks and Learning Systems |
Paper Code |
74.15% |
OpenBMI |
FB CNN |
A subject-independent pattern-based Brain-Computer Interface |
Andreas M. Ray, et al. |
Oct-2015 |
Frontiers in Behavioral Neuroscience |
Paper Code |
66.2% (10x10 CV) |
Private |
FBCSP SVM |
Comparison of Designs Towards a Subject-Independent Brain-Computer Interface based on Motor Imagery |
Fabien Lotte, et al. |
2009 |
International Conference of the IEEE EMBS |
Paper Code |
70.99% |
BCIC_IV_2a (left, right hand) |
multi-resoulution-FBCSP SVM |
Title |
Author |
Date |
Publication |
Link |
Accuracy |
Dataset |
Model |
Dataset |
Subjects |
Classes |
Channels |
Sampling rate |
#Training per subject |
#Test per subject |
BCIC_IV_2a |
9 |
4 (rihgt hand, left hand, both feet, tongue) |
22EEG 3EOG |
250Hz |
288 |
288 |
BCIC_IV_2b |
9 |
2 (rihgt hand, left hand) |
3EEG 3EOG |
250Hz |
400±a |
320±a |
HGD(High-Gamma Dataset) |
14 |
4 (right hand, left hand, rest, feet) |
128 ME-EEG |
500Hz |
~880 |
~160 |
OpenBMI |
54 |
2 (right hand, left hand) |
62EEG 4EMG |
1,000Hz |
200 |
200 |