A binary motor imagery tasks based brain-computer interface for two-dimensional movement control

Xia, Bin; Cao, Lei; Oladazimi, Maysam; Li, Jie; Xie, Hong; Su, Caixia; Birbaumer, Niels

doi:10.1088/1741-2552/aa7ee9

Cited by 16 publications

(5 citation statements)

References 39 publications

(45 reference statements)

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“…visual stimulation [7], [8]), aiding thus in the communication restoration [9], the motor rehabilitation process [10] and the wheelchair/exoskeleton control [11]. Mental commands include among others, mental arithmetic [12], [13], (internal) speech imagination [14], [15], writing imagination [16] and movement imagination (or motor imagery (MI)) [17]- [19], with the latter being identified as the most prominent choice.…”

Section: Introductionmentioning

confidence: 99%

Covariation Informed Graph Slepians for Motor Imagery Decoding

Georgiadis

Adamos

Nikolopoulos

et al. 2021

IEEE Trans. Neural Syst. Rehabil. Eng.

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Graph signal processing (GSP) provides signal analytic tools for data defined in irregular domains, as is the case of non-invasive electroencephalography (EEG). In this work, the recently introduced technique of Graph Slepian functions is exploited for the robust decoding of motor imagery (MI) brain activity. The particular technique builds over the concept of graph Fourier transform (GFT) and provides additional flexibility in the subsequent data analysis by incorporating domain knowledge. Based on contrastive learning, we introduce an algorithmic pipeline that attains a data driven and subject specific design of Graph Slepian functions. These functions, by incorporating both the topology of the sensor array and the empirical evidence about the differential functional covariation, act as spatial filters that enhance the information conveyed by the multichannel signal and specifically relates to the participant's intention. The proposed technique for crafting Graph Slepians is incorporated in a MI-decoding scheme, in which the informed projections are fed to a support vector machine (SVM) that casts a prediction regarding the type of intended movement. The employed MI-decoder is evaluated based on two publicly available datasets and its superiority against popular alternatives in the field is established. Computational efficiency is listed among its main advantages, since it involves only simple matrix operations, allowing to consider its use in real-time implementations.

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Section: Introductionmentioning

confidence: 99%

Covariation Informed Graph Slepians for Motor Imagery Decoding

Georgiadis

Adamos

Nikolopoulos

et al. 2021

IEEE Trans. Neural Syst. Rehabil. Eng.

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“…The motor imagery-based (MIbased) BMI, which does not depend on the external stimulus, allows for asynchronous control paradigms to move the robotic arm (Meng et al, 2016;Penaloza and Nishio, 2018). Nevertheless, the user has to switch many discrete MI states during the task; for instance, he/she needs to perform the left/right hand/both hands MI as well as both hands relaxing to move the endeffector leftward/rightward/upward/downward (limited discrete directions only) (Meng et al, 2016;Xia et al, 2017;Xu et al, 2019). In addition, the movement speed is usually not controlled or merely coarsely modulated by the signal's time-resolved power.…”

Section: Introductionmentioning

confidence: 99%

Semi-Autonomous Robotic Arm Reaching With Hybrid Gaze–Brain Machine Interface

Zeng

Shen

et al. 2020

Front. Neurorobot.

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Recent developments in the non-muscular human-robot interface (HRI) and shared control strategies have shown potential for controlling the assistive robotic arm by people with no residual movement or muscular activity in upper limbs. However, most non-muscular HRIs only produce discrete-valued commands, resulting in non-intuitive and less effective control of the dexterous assistive robotic arm. Furthermore, the user commands and the robot autonomy commands usually switch in the shared control strategies of such applications. This characteristic has been found to yield a reduced sense of agency as well as frustration for the user according to previous user studies. In this study, we firstly propose an intuitive and easy-to-learn-and-use hybrid HRI by combing the Brain-machine interface (BMI) and the gaze-tracking interface. For the proposed hybrid gaze-BMI, the continuous modulation of the movement speed via the motor intention occurs seamlessly and simultaneously to the unconstrained movement direction control with the gaze signals. We then propose a shared control paradigm that always combines user input and the autonomy with the dynamic combination regulation. The proposed hybrid gaze-BMI and shared control paradigm were validated for a robotic arm reaching task performed with healthy subjects. All the users were able to employ the hybrid gaze-BMI for moving the end-effector sequentially to reach the target across the horizontal plane while also avoiding collisions with obstacles. The shared control paradigm maintained as much volitional control as possible, while providing the assistance for the most difficult parts of the task. The presented semi-autonomous robotic system yielded continuous, smooth, and collision-free motion trajectories for the end effector approaching the target. Compared to a system without assistances from robot autonomy, it significantly reduces the rate of failure as well as the time and effort spent by the user to complete the tasks.

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“…These two issues have been resolved by developing BCIs based on mental commands, but at the expense of time required for user training. Mental strategies include movement imagination (Solis-Escalante et al 2008, Ge et al 2014, Xia et al 2017, internalized speech , Nguyen et al 2017 and mental arithmetic Sourina 2013, Dimitriadis et al 2016). The first one, also known as motor imagery (MI), remains the most popular option due to its adhesion with a planned action.…”

Section: Introductionmentioning

confidence: 99%

Connectivity steered graph Fourier transform for motor imagery BCI decoding

Georgiadis

Laskaris²,

Nikolopoulos

et al. 2019

J. Neural Eng.

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Objective. Graph signal processing (GSP) concepts are exploited for brain activity decoding and particularly the detection and recognition of a motor imagery (MI) movement. A novel signal analytic technique that combines graph Fourier transform (GFT) with estimates of cross-frequency coupling (CFC) and discriminative learning is introduced as a means to recover the subject’s intention from the multichannel signal. Approach. Adopting a multi-view perspective, based on the popular concept of co-existing and interacting brain rhythms, a multilayer network model is first built from empirical data and its connectivity graph is used to derive the GFT-basis. A personalized decoding scheme supporting a binary decision, either ‘left versus right’ or ‘rest versus MI’, is crafted from a small set of training trials. Electroencephalographic (EEG) activity from 12 volunteers recorded during two randomly alternating, externally cued, MI tasks (clenching either left or right fist) and a rest condition is used to introduce and validate our methodology. In addition, the introduced methodology was further validated based on dataset IVa of BCI III competition. Main results. Our GFT-domain decoding scheme achieves nearly optimal performance and proves superior to alternative techniques that are very popular in the field. Significance. At a conceptual level, our work suggests a fruitful way to introduce network neuroscience in BCI research. At a more practical level, it is characterized by efficiency. Training is realized using a small number of exemplar trials and decoding requires very simple operations that leaves room for real-time implementation.

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A binary motor imagery tasks based brain-computer interface for two-dimensional movement control

Abstract: The results demonstrate that participants could move the cursor in the 2D plane effectively. The proposed control strategy is based only on a basic two-motor imagery BCI, which enables more people to use it in real-life applications.

Cited by 16 publications

References 39 publications

Covariation Informed Graph Slepians for Motor Imagery Decoding

Covariation Informed Graph Slepians for Motor Imagery Decoding

Semi-Autonomous Robotic Arm Reaching With Hybrid Gaze–Brain Machine Interface

Connectivity steered graph Fourier transform for motor imagery BCI decoding

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