4.6 Article

Discrepancy between inter- and intra-subject variability in EEG-based motor imagery brain-computer interface: Evidence from multiple perspectives

Related references

Note: Only part of the references are listed.
Article Engineering, Biomedical

MIN2Net: End-to-End Multi-Task Learning for Subject-Independent Motor Imagery EEG Classification

Phairot Autthasan et al.

Summary: Advances in motor imagery-based brain-computer interfaces (BCIs) allow control of multiple applications using electroencephalography (EEG) recordings. However, changes in EEG rhythms pose challenges to classification performance in a subject-independent manner. To tackle this problem, the researchers propose MIN2Net, a method that combines deep metric learning and multi-task autoencoder to improve classification performance.

IEEE TRANSACTIONS ON BIOMEDICAL ENGINEERING (2022)

Article Neurosciences

A new perspective on individual reliability beyond group effect for event-related potentials: A multisensory investigation and computational modeling

Zhenxing Hu et al.

Summary: This study rigorously evaluated the test-retest reliability of ERPs in a multisensory and cognitive experiment involving 82 healthy adolescents. It found that a stronger group-level response in ERPs did not guarantee higher individual reliability. The consistency between group-level ERP responses and individual reliability was influenced by inter-subject latency jitter and inter-trial variability. These findings suggest the need to consider a neural oscillation perspective when assessing the reliability of ERPs.

NEUROIMAGE (2022)

Article Neurosciences

M3CV: A multi-subject, multi-session, and multi-task database for EEG-based biometrics challenge

Gan Huang et al.

Summary: EEG signals exhibit commonality and variability across subjects, sessions, and tasks. This study introduces an EEG-based biometric competition based on a large-scale M3CV database to promote the development of machine learning algorithms and achieve a better understanding of the commonality and variability of EEG signals.

NEUROIMAGE (2022)

Article Multidisciplinary Sciences

A large EEG dataset for studying cross-session variability in motor imagery brain-computer interface

Jun Ma et al.

Summary: Building a practical and robust brain-computer interface (BCI) faces the challenge of classifying motor imagery (MI) from electroencephalography (EEG) signals due to their large variability. This study collected a large dataset of MI from 25 subjects across 5 different days, presenting benchmarking classification accuracy for within-session classification, cross-session classification, and cross-session adaptation. The results show that cross-session adaptation improves the accuracy significantly, which is important for addressing challenges in BCI research.

SCIENTIFIC DATA (2022)

Editorial Material Mathematical & Computational Biology

Editorial: Inter- and Intra-subject Variability in Brain Imaging and Decoding

Chun-Shu Wei et al.

FRONTIERS IN COMPUTATIONAL NEUROSCIENCE (2021)

Article Mathematical & Computational Biology

Identifying Individuals With Mild Cognitive Impairment Using Working Memory-Induced Intra-Subject Variability of Resting-State EEGs

Thanh-Tung Trinh et al.

Summary: The extractive spectral-power-based task-induced intra-subject EEG variability may serve as a valuable feature for the early detection of MCI. Experimental results suggest that this method shows promising classification performance between healthy controls and individuals with MCI.

FRONTIERS IN COMPUTATIONAL NEUROSCIENCE (2021)

Article Engineering, Biomedical

Dynamic Joint Domain Adaptation Network for Motor Imagery Classification

Xiaolin Hong et al.

Summary: The proposed method utilizes an adversarial learning strategy and introduces a dynamic joint domain adaptation network to learn domain-invariant feature representation, significantly improving EEG classification performance.

IEEE TRANSACTIONS ON NEURAL SYSTEMS AND REHABILITATION ENGINEERING (2021)

Review Mathematical & Computational Biology

Intra- and Inter-subject Variability in EEG-Based Sensorimotor Brain Computer Interface: A Review

Simanto Saha et al.

FRONTIERS IN COMPUTATIONAL NEUROSCIENCE (2020)

Article Engineering, Biomedical

BCI for stroke rehabilitation: motor and beyond

Ravikiran Mane et al.

JOURNAL OF NEURAL ENGINEERING (2020)

Article Computer Science, Hardware & Architecture

Transfer Learning Based on Regularized Common Spatial Patterns Using Cosine Similarities of Spatial Filters for Motor-Imagery BCI

Yilu Xu et al.

JOURNAL OF CIRCUITS SYSTEMS AND COMPUTERS (2019)

Article Engineering, Biomedical

Weighted Transfer Learning for Improving Motor Imagery-Based Brain-Computer Interface

Ahmed M. Azab et al.

IEEE TRANSACTIONS ON NEURAL SYSTEMS AND REHABILITATION ENGINEERING (2019)

Article Engineering, Biomedical

Latent variable method for automatic adaptation to background states in motor imagery BCI

Nikolay Dagaev et al.

JOURNAL OF NEURAL ENGINEERING (2018)

Article Engineering, Biomedical

Evidence of Variabilities in EEG Dynamics During Motor Imagery-Based Multiclass Brain-Computer Interface

Simanto Saha et al.

IEEE TRANSACTIONS ON NEURAL SYSTEMS AND REHABILITATION ENGINEERING (2018)

Review Engineering, Biomedical

A review of classification algorithms for EEG-based brain-computer interfaces: a 10 year update

F. Lotte et al.

JOURNAL OF NEURAL ENGINEERING (2018)

Review Behavioral Sciences

Interpreting and Utilising Intersubject Variability in Brain Function

Mohamed L. Seghier et al.

TRENDS IN COGNITIVE SCIENCES (2018)

Article Neurosciences

Regularized common spatial patterns with subject-to-subject transfer of EEG signals

Minmin Cheng et al.

COGNITIVE NEURODYNAMICS (2017)

Article Computer Science, Artificial Intelligence

Transfer Learning in Brain-Computer Interfaces

Vinay Jayaram et al.

IEEE COMPUTATIONAL INTELLIGENCE MAGAZINE (2016)

Review Biochemical Research Methods

Performance variation in motor imagery brain-computer interface: A brief review

Minkyu Ahn et al.

JOURNAL OF NEUROSCIENCE METHODS (2015)

Article Behavioral Sciences

A subject-independent pattern-based Brain-Computer Interface

Andreas M. Ray et al.

FRONTIERS IN BEHAVIORAL NEUROSCIENCE (2015)

Editorial Material Neurosciences

Motor variability is not noise, but grist for the learning mill

David J. Herzfeld et al.

NATURE NEUROSCIENCE (2014)

Article Engineering, Biomedical

Transferring Subspaces Between Subjects in Brain-Computer Interfacing

Wojciech Samek et al.

IEEE TRANSACTIONS ON BIOMEDICAL ENGINEERING (2013)

Article Automation & Control Systems

Unsupervised adaptation of electroencephalogram signal processing based on fuzzy C-means algorithm

Guangquan Liu et al.

INTERNATIONAL JOURNAL OF ADAPTIVE CONTROL AND SIGNAL PROCESSING (2012)

Article Clinical Neurology

Towards a Cure for BCI Illiteracy

Carmen Vidaurre et al.

BRAIN TOPOGRAPHY (2010)

Article Engineering, Biomedical

Application of Covariate Shift Adaptation Techniques in Brain-Computer Interfaces

Yan Li et al.

IEEE TRANSACTIONS ON BIOMEDICAL ENGINEERING (2010)

Article Neurosciences

Variability in fMRI: A re-examination of inter-session differences

SM Smith et al.

HUMAN BRAIN MAPPING (2005)