Multi-channel EEG recording during motor imagery of different joints from the same limb

Ma, Xuelin; Qiu, Shuang; He, Hong

doi:10.1038/s41597-020-0535-2

Cited by 31 publications

(27 citation statements)

References 30 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…A. Dataset and Pre-processing MI-2 Dataset: MI-2 Dataset is a new MI dataset, in which each subject conducted MI with different joints from the same limb [39]. This MI paradigm can provide intuitive control over external devices [32], and it is of great significance to decode the MI within the same limb.…”

Section: Methodsmentioning

confidence: 99%

“…This MI paradigm can provide intuitive control over external devices [32], and it is of great significance to decode the MI within the same limb. Therefore, our proposed S-CAMLP-Net was evaluated on the newly built MI-2 dataset, which consists of 25 right-handed healthy subjects [39]. In this dataset, there were three different imaging categories, including imagining grasp movement with right hand, imagining right elbow movement, and keeping resting state with eyes open.…”

Section: Methodsmentioning

confidence: 99%

“…The EEG signals of different MI tasks were recoded using the 64-channel gel electrode cap of the standard 10/20 system, and the validity period of each record was 4s. In this study, we adopted the pre-processed data for further classification task, and totally 62-channel EEG electrodes (except HEO, VEO) are used for MI task [39]. The preprocessing pipeline included a band-pass filter and a 50 Hz notch filter.…”

Section: Methodsmentioning

confidence: 99%

“…Based on Reference [32] and [39], the sliding window approach was applied to segment the data of 7 sessions for each subject, in which 5 sessions contained 20 trials for every movement imagination, and the following 2 sessions included totally 100 trials of resting state. In this work, the size of the sliding window was set as 150 time points with an overlap of 10 points in adjacent slices, because such slice length has the best classification accuracy in previous study [32].…”

Section: Methodsmentioning

confidence: 99%

See 3 more Smart Citations

A Self-Supervised Learning Based Channel Attention MLP-Mixer Network for Motor Imagery Decoding

Jun

et al. 2022

IEEE Trans. Neural Syst. Rehabil. Eng.

View full text Add to dashboard Cite

Convolutional Neural Network (CNN) is commonly used for the Electroencephalogram (EEG) based motor-imagery (MI) decoding. However, its performance is generally limited due to the small size sample problem. An alternative way to address such issue is to segment EEG trials into small slices for data augmentation, but this approach usually inevitably loses the valuable long-range dependencies of temporal information in EEG signals. To this end, we propose a novel self-supervised learning (SSL) based channel attention MLP-Mixer network (S-CAMLP-Net) for MI decoding with EEG. Specifically, a new EEG slice prediction task is designed as the pretext task to capture the long-range information of EEG trials in the time domain. In the downstream task, a newly proposed MLP-Mixer is applied to the classification task for signals rather than for images. Moreover, in order to effectively learn the discriminative spatial representations in EEG slices, an attention mechanism is integrated into MLP-Mixer to adaptively estimate the importance of each EEG channel without any prior information. Thus, the proposed S-CAMLP-Net can effectively learn more long-range temporal information and global spatial features of EEG signals. Extensive experiments are conducted on the public MI-2 dataset and the BCI Competition IV Dataset 2A. The experimental results indicate that our proposed S-CAMLP-Net achieves superior classification performance over all the compared algorithms.

show abstract

Section: Methodsmentioning

confidence: 99%

Section: Methodsmentioning

confidence: 99%

Section: Methodsmentioning

confidence: 99%

Section: Methodsmentioning

confidence: 99%

See 2 more Smart Citations

A Self-Supervised Learning Based Channel Attention MLP-Mixer Network for Motor Imagery Decoding

Jun

et al. 2022

IEEE Trans. Neural Syst. Rehabil. Eng.

View full text Add to dashboard Cite

show abstract

“…Artificial intelligence is widely applied in EEG-based assistive technology during post-stroke rehabilitation to accurately analyse complex signals [28], [69]- [73], [77]. Neural networks consists of layers of nodes, known as neurons, designed to estimate non-linear decision boundaries [72], [73].…”

Section: Computational Intelligence For Stroke Rehabilitationmentioning

confidence: 99%

Virtual Reality Assisted Motor Imagery for Early Post-Stroke Recovery: A Review

Choy

Cloherty

Pirogova

et al. 2023

IEEE Rev. Biomed. Eng.

View full text Add to dashboard Cite

Stroke is a serious neurological disease that may lead to long-term disabilities and even death for stroke patients worldwide. The acute period, (≤1 mo post-stroke), is crucial for rehabilitation but the current standard clinical practice may be ineffective for patients with severe motor impairment, since most rehabilitation programs involve physical movement. Imagined movement -the so-called motor imagery (MI) -has been shown to activate motor areas of the brain without physical movement. MI therefore offers an opportunity for early rehabilitation of stroke patients. MI, however, is not widely employed in clinical practice due to a lack of evidence-based research. Here, we review MI-based approaches to rehabilitation of stroke patients and immersive virtual reality (VR) technologies to potentially assist MI and thus, promote recovery of motor function.

show abstract