/Subject_independent_MI_classification_paper

Subject independent MI classification paper

Subject_independent_MI_classification_paper

Experimental setup : Leave-one-subject-out (LOSO) is widely used.

Papers

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

Papers to read

Title Author Date Publication Link Accuracy Dataset Model

Public Datasets

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