Development of a Novel Motor Imagery Control Technique and Application in a Gaming Environment

We present a methodology for a hybrid brain-computer interface (BCI) system, with the recognition of motor imagery (MI) based on EEG and blink EOG signals. We tested the BCI system in a 3D Tetris and an analogous 2D game playing environment. To enhance player’s BCI control ability, the study focused on feature extraction from EEG and control strategy supporting Game-BCI system operation. We compared the numerical differences between spatial features extracted with common spatial pattern (CSP) and the proposed multifeature extraction. To demonstrate the effectiveness of 3D game environment at enhancing player’s event-related desynchronization (ERD) and event-related synchronization (ERS) production ability, we set the 2D Screen Game as the comparison experiment. According to a series of statistical results, the group performing MI in the 3D Tetris environment showed more significant improvements in generating MI-associated ERD/ERS. Analysis results of game-score indicated that the players’ scores presented an obvious uptrend in 3D Tetris environment but did not show an obvious downward trend in 2D Screen Game. It suggested that the immersive and rich-control environment for MI would improve the associated mental imagery and enhance MI-based BCI skills.

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What if I had a third arm? An EEG study of a supernumerary BCI system

10.1101/817205 ◽

2019 ◽

Author(s):

Jaime A. Riascos ◽

David Steeven Villa ◽

Anderson Maciel ◽

Luciana Nedel ◽

Dante Barone

Keyword(s):

Motor Imagery ◽

Body Parts ◽

Task Load ◽

The Third ◽

Person Perspective ◽

Human Ability ◽

Robotic Prosthesis ◽

And Control ◽

Bci System ◽

Psychological Experiments

AbstractMotor imagery Brain-Computer Interface (MI-BCI) enables bodyless communication by means of the imagination of body movements. Since its apparition, MI-BCI has been widely used in applications such as guiding a robotic prosthesis, or the navigation in games and virtual reality (VR) environments. Although psychological experiments, such as the Rubber Hand Illusion - RHI, suggest the human ability for creating body transfer illusions, MI-BCI only uses the imagination of real body parts as neurofeedback training and control commands. The present work studies and explores the inclusion of an imaginary third arm as a part of the control commands for MI-BCI systems. It also compares the effectiveness of using the conventional arrows and fixation cross as training step (Graz condition) against realistic human hands performing the corresponding tasks from a first-person perspective (Hands condition); both conditions wearing a VR headset. Ten healthy subjects participated in a two-session EEG experiment involving open-close hand tasks, including a third arm that comes out from the chest. The EEG analysis shows a strong power decrease in the sensory-motor areas for the third arm task in both training conditions. Such activity is significantly stronger for Hands than Graz condition, suggesting that the realistic scenario can reduce the abstractness of the third arm and improve the generation of motor imagery signals. The cognitive load is also assessed both by NASA-TLX and Task Load index.

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Information based source number estimation for probabilistic common spatial pattern in motor imagery BCI system

2016 24th Iranian Conference on Electrical Engineering (ICEE) ◽

10.1109/iraniancee.2016.7585584 ◽

2016 ◽

Cited By ~ 1

Author(s):

Asghar Zarei ◽

Farnaz Ghassemi ◽

Mohammad Hassan Moradi

Keyword(s):

Spatial Pattern ◽

Motor Imagery ◽

Common Spatial Pattern ◽

Source Number Estimation ◽

Bci System ◽

Source Number

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EEG-Based BCI System Using Adaptive Features Extraction and Classification Procedures

Computational Intelligence and Neuroscience ◽

10.1155/2016/4562601 ◽

2016 ◽

Vol 2016 ◽

pp. 1-14 ◽

Cited By ~ 10

Author(s):

Valeria Mondini ◽

Anna Lisa Mangia ◽

Angelo Cappello

Keyword(s):

Motor Imagery ◽

Training Session ◽

Adaptive Strategy ◽

Support Vector ◽

Common Spatial Pattern ◽

User Training ◽

Common Control ◽

Adaptive Thresholds ◽

Giving Feedback ◽

Bci System

Motor imagery is a common control strategy in EEG-based brain-computer interfaces (BCIs). However, voluntary control of sensorimotor (SMR) rhythms by imagining a movement can be skilful and unintuitive and usually requires a varying amount of user training. To boost the training process, a whole class of BCI systems have been proposed, providing feedback as early as possible while continuously adapting the underlying classifier model. The present work describes a cue-paced, EEG-based BCI system using motor imagery that falls within the category of the previously mentioned ones. Specifically, our adaptive strategy includes a simple scheme based on a common spatial pattern (CSP) method and support vector machine (SVM) classification. The system’s efficacy was proved by online testing on 10 healthy participants. In addition, we suggest some features we implemented to improve a system’s “flexibility” and “customizability,” namely, (i) a flexible training session, (ii) an unbalancing in the training conditions, and (iii) the use of adaptive thresholds when giving feedback.

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Decoding Motor Imagery through Common Spatial Pattern Filters at the EEG Source Space

Computational Intelligence and Neuroscience ◽

10.1155/2018/7957408 ◽

2018 ◽

Vol 2018 ◽

pp. 1-10 ◽

Cited By ~ 12

Author(s):

Ioannis Xygonakis ◽

Alkinoos Athanasiou ◽

Niki Pandria ◽

Dimitris Kugiumtzis ◽

Panagiotis D. Bamidis

Keyword(s):

Spatial Pattern ◽

Motor Imagery ◽

Degrees Of Freedom ◽

Common Spatial Pattern ◽

Source Imaging ◽

Spatial Features ◽

Current State ◽

Eeg Source Imaging ◽

Accuracy Increase

Brain-Computer Interface (BCI) is a rapidly developing technology that aims to support individuals suffering from various disabilities and, ultimately, improve everyday quality of life. Sensorimotor rhythm-based BCIs have demonstrated remarkable results in controlling virtual or physical external devices but they still face a number of challenges and limitations. Main challenges include multiple degrees-of-freedom control, accuracy, and robustness. In this work, we develop a multiclass BCI decoding algorithm that uses electroencephalography (EEG) source imaging, a technique that maps scalp potentials to cortical activations, to compensate for low spatial resolution of EEG. Spatial features were extracted using Common Spatial Pattern (CSP) filters in the cortical source space from a number of selected Regions of Interest (ROIs). Classification was performed through an ensemble model, based on individual ROI classification models. The evaluation was performed on the BCI Competition IV dataset 2a, which features 4 motor imagery classes from 9 participants. Our results revealed a mean accuracy increase of 5.6% with respect to the conventional application method of CSP on sensors. Neuroanatomical constraints and prior neurophysiological knowledge play an important role in developing source space-based BCI algorithms. Feature selection and classifier characteristics of our implementation will be explored to raise performance to current state-of-the-art.

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Insights into the combination of neuromuscular electrical stimulation and motor imagery in a training-based approach

European Journal of Applied Physiology ◽

10.1007/s00421-020-04582-4 ◽

2021 ◽

Author(s):

Amandine Bouguetoch ◽

Alain Martin ◽

Sidney Grosprêtre

Keyword(s):

Electrical Stimulation ◽

Motor Imagery ◽

Neural Plasticity ◽

Neuromuscular Electrical Stimulation ◽

Voluntary Contraction ◽

Muscle Architecture ◽

Fascicle Length ◽

Before And After ◽

V Wave ◽

And Control

Abstract Introduction Training stimuli that partially activate the neuromuscular system, such as motor imagery (MI) or neuromuscular electrical stimulation (NMES), have been previously shown as efficient tools to induce strength gains. Here the efficacy of MI, NMES or NMES + MI trainings has been compared. Methods Thirty-seven participants were enrolled in a training program of ten sessions in 2 weeks targeting plantar flexor muscles, distributed in four groups: MI, NMES, NMES + MI and control. Each group underwent forty contractions in each session, NMES + MI group doing 20 contractions of each modality. Before and after, the neuromuscular function was tested through the recording of maximal voluntary contraction (MVC), but also electrophysiological and mechanical responses associated with electrical nerve stimulation. Muscle architecture was assessed by ultrasonography. Results MVC increased by 11.3 ± 3.5% in NMES group, by 13.8 ± 5.6% in MI, while unchanged for NMES + MI and control. During MVC, a significant increase in V-wave without associated changes in superimposed H-reflex has been observed for NMES and MI, suggesting that neural adaptations occurred at supraspinal level. Rest spinal excitability was increased in the MI group while decreased in the NMES group. No change in muscle architecture (pennation angle, fascicle length) has been found in any group but muscular peak twitch and soleus maximal M-wave increased in the NMES group only. Conclusion Finally, MI and NMES seem to be efficient stimuli to improve strength, although both exhibited different and specific neural plasticity. On its side, NMES + MI combination did not provide the expected gains, suggesting that their effects are not simply cumulative, or even are competitive.

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Induction of Neural Plasticity Using a Low-Cost Open Source Brain-Computer Interface and a 3D-Printed Wrist Exoskeleton

Sensors ◽

10.3390/s21020572 ◽

2021 ◽

Vol 21 (2) ◽

pp. 572

Author(s):

Mads Jochumsen ◽

Taha Al Muhammadee Janjua ◽

Juan Carlos Arceo ◽

Jimmy Lauber ◽

Emilie Simoneau Buessinger ◽

...

Keyword(s):

Open Source ◽

Neural Plasticity ◽

Motor Evoked Potentials ◽

Low Cost ◽

True Positive Rate ◽

Positive Rate ◽

3D Printed ◽

Major Factors ◽

And Control ◽

Bci System

Brain-computer interfaces (BCIs) have been proven to be useful for stroke rehabilitation, but there are a number of factors that impede the use of this technology in rehabilitation clinics and in home-use, the major factors including the usability and costs of the BCI system. The aims of this study were to develop a cheap 3D-printed wrist exoskeleton that can be controlled by a cheap open source BCI (OpenViBE), and to determine if training with such a setup could induce neural plasticity. Eleven healthy volunteers imagined wrist extensions, which were detected from single-trial electroencephalography (EEG), and in response to this, the wrist exoskeleton replicated the intended movement. Motor-evoked potentials (MEPs) elicited using transcranial magnetic stimulation were measured before, immediately after, and 30 min after BCI training with the exoskeleton. The BCI system had a true positive rate of 86 ± 12% with 1.20 ± 0.57 false detections per minute. Compared to the measurement before the BCI training, the MEPs increased by 35 ± 60% immediately after and 67 ± 60% 30 min after the BCI training. There was no association between the BCI performance and the induction of plasticity. In conclusion, it is possible to detect imaginary movements using an open-source BCI setup and control a cheap 3D-printed exoskeleton that when combined with the BCI can induce neural plasticity. These findings may promote the availability of BCI technology for rehabilitation clinics and home-use. However, the usability must be improved, and further tests are needed with stroke patients.

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