Conclusion and Perspectives

Clerc, Maureen; Bougrain, Laurent; Lotte, Fabien

doi:10.1002/9781119332428.ch17

Cited by 50 publications

(91 citation statements)

References 19 publications

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“…Brain-computer interfaces (BCIs) are communication/control systems that can be employed to transform the user's intention into different actions by means of brain activity [9][10][11][12]. BCIs include sensors that record brain activity and software that processes this information in order to interact with the environment by means of actuators.…”

Section: Introductionmentioning

confidence: 99%

Low-Cost Robotic Guide Based on a Motor Imagery Brain–Computer Interface for Arm Assisted Rehabilitation

Quiles

Suay

Candela

et al. 2020

IJERPH

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Motor imagery has been suggested as an efficient alternative to improve the rehabilitation process of affected limbs. In this study, a low-cost robotic guide is implemented so that linear position can be controlled via the user’s motor imagination of movement intention. The patient can use this device to move the arm attached to the guide according to their own intentions. The first objective of this study was to check the feasibility and safety of the designed robotic guide controlled via a motor imagery (MI)-based brain–computer interface (MI-BCI) in healthy individuals, with the ultimate aim to apply it to rehabilitation patients. The second objective was to determine which are the most convenient MI strategies to control the different assisted rehabilitation arm movements. The results of this study show a better performance when the BCI task is controlled with an action–action MI strategy versus an action–relaxation one. No statistically significant difference was found between the two action–action MI strategies.

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Section: Introductionmentioning

confidence: 99%

Low-Cost Robotic Guide Based on a Motor Imagery Brain–Computer Interface for Arm Assisted Rehabilitation

Quiles

Suay

Candela

et al. 2020

IJERPH

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“…This is due to a phenomenon called source leakage (e.g. [3]): the imprecision of the source reconstruction spreads the source activities to nearby sources when the sources are reconstructed. Thus, when the volumetric sources are reconstructed in the method, some relevant information leaks to the ROI even if the source is not originally inside it.…”

Section: Example Simulationsmentioning

confidence: 99%

“…To obtain a discrete command, the BCI methods process and classify segments of the EEG signal. Unfortunately the current BCIs leave a lot to be desired in terms of accuracy [1,2,3]. Why is this the case?…”

Section: Introductionmentioning

confidence: 99%

simBCI—A Framework for Studying BCI Methods by Simulated EEG

Lindgren

Merlini

Lécuyer

et al. 2018

IEEE Trans. Neural Syst. Rehabil. Eng.

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“…The signal quality of EEG is lower than with invasive methods and the recording is very sensitive to noise, nonetheless possible applications offer promising results [11]. As technologies and signal processing techniques are more and more mature, out-of-the-lab applications and commercial systems are the focus of growing interests [3]. These applications and systems rely on a small number of electrodes for recording and low-cost hardware for signal processing.…”

Section: Introductionmentioning

confidence: 99%

“…Real-time recording and decoding of brain signals allow to control a large variety of systems, such as wheelchairs, exoskeletons, robotic arms or other types of Brain-Computer Interface (BCI) devices [3]. With electroencephalography (EEG), the brain signal is recorded at the surface of the head (on the scalp), offering a simple setup that does not require surgery as it is the case for invasive recording methods.…”

Section: Introductionmentioning

confidence: 99%

Inductive Means and Sequences Applied to Online Classification of EEG

Massart

Chevallier²

2017

Lecture Notes in Computer Science

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Abstract. The translation of brain activity into user command, through Brain-Computer Interfaces (BCI), is a very active topic in machine learning and signal processing. As commercial applications and out-of-the-lab solutions are proposed, there is an increased pressure to provide online algorithms and real-time implementations. Electroencephalography (EEG) systems offer lightweight and wearable solutions, at the expense of signal quality. Approaches based on covariance matrices have demonstrated good robustness to noise and provide a suitable representation for classification tasks, relying on advances in Riemannian geometry. We propose to equip the minimum distance to mean (MDM) classifier with a new family of means, based on the inductive mean, for block-online classification tasks and to embed the inductive mean in an incremental learning algorithm for online classification of EEG.

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Conclusion and Perspectives

Cited by 50 publications

References 19 publications

Low-Cost Robotic Guide Based on a Motor Imagery Brain–Computer Interface for Arm Assisted Rehabilitation

Low-Cost Robotic Guide Based on a Motor Imagery Brain–Computer Interface for Arm Assisted Rehabilitation

simBCI—A Framework for Studying BCI Methods by Simulated EEG

Inductive Means and Sequences Applied to Online Classification of EEG

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