Enhancement of motor imagery training efficiency by an online adaptive training paradigm integrated with error related potential

Tao, Tangfei; Jia, Yaguang; Xu, Guanghua; Liang, Renghao; Zhang, Qiuxiang; Chen, Longting; Gao, Yuxiang; Chen, Ruiquan; Zheng, Xiaowei; Yu, Yunhui

doi:10.1088/1741-2552/acb102

Cited by 11 publications

(4 citation statements)

References 60 publications

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“…Moreover, the time complexity is an important metric for evaluating the applicability of classification methods in online MI-BCI systems. Studies have indicated (Arpaia et al, 2022 ; Tao et al, 2023 ) that achieving stable online MI-BCI control performance with millisecond-level response is crucial for generating control commands. Our DS-KTL method demonstrates competitive time complexity (see Tables 11 , 12 ).…”

Section: Discussionmentioning

confidence: 99%

Dual selections based knowledge transfer learning for cross-subject motor imagery EEG classification

Luo

2023

Front. Neurosci.

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IntroductionMotor imagery electroencephalograph (MI-EEG) has attracted great attention in constructing non-invasive brain-computer interfaces (BCIs) due to its low-cost and convenience. However, only a few MI-EEG classification methods have been recently been applied to BCIs, mainly because they suffered from sample variability across subjects. To address this issue, the cross-subject scenario based on domain adaptation has been widely investigated. However, existing methods often encounter problems such as redundant features and incorrect pseudo-label predictions in the target domain.MethodsTo achieve high performance cross-subject MI-EEG classification, this paper proposes a novel method called Dual Selections based Knowledge Transfer Learning (DS-KTL). DS-KTL selects both discriminative features from the source domain and corrects pseudo-labels from the target domain. The DS-KTL method applies centroid alignment to the samples initially, and then adopts Riemannian tangent space features for feature adaptation. During feature adaptation, dual selections are performed with regularizations, which enhance the classification performance during iterations.Results and discussionEmpirical studies conducted on two benchmark MI-EEG datasets demonstrate the feasibility and effectiveness of the proposed method under multi-source to single-target and single-source to single-target cross-subject strategies. The DS-KTL method achieves significant classification performance improvement with similar efficiency compared to state-of-the-art methods. Ablation studies are also conducted to evaluate the characteristics and parameters of the proposed DS-KTL method.

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Section: Discussionmentioning

confidence: 99%

Dual selections based knowledge transfer learning for cross-subject motor imagery EEG classification

Luo

2023

Front. Neurosci.

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“…We positioned the electrodes according to the 10–5 international system at Fp1, Fp2, AF3, AFz, AF4, F7, F5, F3, F1, Fz, F2, F4, F6, F8, FT7, FC5, FC3, FC1, FCz, FC2, FC4, FC6, FT8, FFC3h, FCC1h, FCC2h, FCC4h, T7, C5, C3, C1, Cz, C2, C4, C6, T8, CCP3h, CCP1h, CCP2h, CCP4h, TP7, CP5, CP3, CP1, CP2, CP4, CP6, TP8, P7, P5, P3, P1, Pz, P2, P4, P6, P8, PO3, POz, PO4, O1, Oz, and O2. CPz and AFz were used for reference and ground electrodes, respectively, as in previous works 17 , 39 . When mounting the EEG electrodes, we made sure that the impedances between the scalp and electrodes were below 10 kΩ.…”

Section: Methodsmentioning

confidence: 99%

Multimodal decoding of error processing in a virtual reality flight simulation

Wimmer,

Weidinger,

Veas

et al. 2024

Sci Rep

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Technological advances in head-mounted displays (HMDs) facilitate the acquisition of physiological data of the user, such as gaze, pupil size, or heart rate. Still, interactions with such systems can be prone to errors, including unintended behavior or unexpected changes in the presented virtual environments. In this study, we investigated if multimodal physiological data can be used to decode error processing, which has been studied, to date, with brain signals only. We examined the feasibility of decoding errors solely with pupil size data and proposed a hybrid decoding approach combining electroencephalographic (EEG) and pupillometric signals. Moreover, we analyzed if hybrid approaches can improve existing EEG-based classification approaches and focused on setups that offer increased usability for practical applications, such as the presented game-like virtual reality flight simulation. Our results indicate that classifiers trained with pupil size data can decode errors above chance. Moreover, hybrid approaches yielded improved performance compared to EEG-based decoders in setups with a reduced number of channels, which is crucial for many out-of-the-lab scenarios. These findings contribute to the development of hybrid brain-computer interfaces, particularly in combination with wearable devices, which allow for easy acquisition of additional physiological data.

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“…For example, Wang et al [137] proposed a shared control model to fuse machine intelligence and human intention automatically. Besides, a hybrid BCI based on multiple brain signals can be effective by making up for the shortcomings of one kind of BCI, such as the combinations of MI with error-related potentials (ErRP) and P300 as the control signals of a robotic arm [138], and MI training combined with ErRP to improve BCI performance [139].…”

Section: E Fusion Techniques Of Data Model and Systemmentioning

confidence: 99%

EEG-Based Motor BCIs for Upper Limb Movement: Current Techniques and Future Insights

Wang,

Bi,

Fei

2023

IEEE Trans. Neural Syst. Rehabil. Eng.

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Motor brain-computer interface (BCI) refers to the BCI that decodes voluntary motion intentions from brain signals directly and outputs corresponding control commands without activating peripheral nerves and muscles. Motor BCIs can be used for the restoration, compensation, and augmentation of motor function by activating the neuromuscular circuit and facilitating neural plasticity. The essential applications of motor BCIs include neurorehabilitation and daily-life assistance for motor-impaired patients. In recent years, studies on motor BCIs mainly concentrate on neural signatures, movement decoding, and its applications. In this review, we aim to provide a comprehensive review of the state-of-the-art research of electroencephalography (EEG) signals-based motor BCIs for the first time. We also aim to give some insights into advancing motor BCIs to a more natural and practical application scenario. In particular, we focus on the motor BCIs for the movements of the upper limbs. Specifically, the experimental paradigms, techniques, and application systems of upper-limb BCIs are reviewed. Several vital issues in developing more natural and practical upper-limb motor BCIs, including developing target-users-oriented, distraction-robust, and multi-limbs motor BCIs, and applying fusion techniques to promote the natural and practical motor BCIs, are discussed.

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Enhancement of motor imagery training efficiency by an online adaptive training paradigm integrated with error related potential

Cited by 11 publications

References 60 publications

Dual selections based knowledge transfer learning for cross-subject motor imagery EEG classification

Dual selections based knowledge transfer learning for cross-subject motor imagery EEG classification

Multimodal decoding of error processing in a virtual reality flight simulation

EEG-Based Motor BCIs for Upper Limb Movement: Current Techniques and Future Insights

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