Combining ERD and ERS features to create a system-paced BCI

Thomas, Eoin; Fruitet, Joan; Clerc, Maureen

doi:10.1016/j.jneumeth.2013.03.026

Cited by 18 publications

(8 citation statements)

References 24 publications

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“…Typically, BMI users are rigorously trained to dominate MI skills, so as to magnify the ERD/ERS effects on the scalp, regardless of evolutionary genetics, skill acquisition along lifespan, and sensory-cognitive information and resources at a time (Figure 2). BMI is not only associated with the analysis of EEG signals prior to, during and after MI activity [48][49][50], but it is also related to previous knowledge of user, current user state, user profile, and environmental conditions: factors that determine the level of synchrony of neural oscillations as well.…”

Section: Neural Oscillations In Bmimentioning

confidence: 99%

“…In [48,49], it was found that pre-stimulus sensory-motor rhythms can predict user performance, and can lead to better classifiable EEG patterns as well. In [50], it was demonstrated that the most optimal features to differentiate MI tasks were post-MI period, rather than peri-MI period. Nevertheless, the signal analysis is not limited to this proposal.…”

Section: How To Design New Paradigms Based On the Sensory-motor Systementioning

confidence: 99%

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Characterizing Motor System to Improve Training Protocols Used in Brain-Machine Interfaces Based on Motor Imagery

Alonso-Valerdi¹,

González-Garrido²

2018

Cognitive and Computational Neuroscience - Principles, Algorithms and Applications

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Motor imagery (MI)-based brain-machine interface (BMI) is a technology under development that actively modifies users' perception and cognition through mental tasks, so as to decode their intentions from their neural oscillations, and thereby bringing some kind of activation. So far, MI as control task in BMIs has been seen as a skill that must be acquired, but neither user conditions nor controlled learning conditions have been taken into account. As motor system is a complex mechanism trained along lifetime, and MI-based BMI attempts to decode motor intentions from neural oscillations in order to put a device into action, motor mechanisms should be considered when prototyping BMI systems. It is hypothesized that the best way to acquire MI skills is following the same rules humans obey to move around the world. On this basis, new training paradigms consisting of ecological environments, identification of control tasks according to the ecological environment, transparent mapping, and multisensory feedback are proposed in this chapter. These new MI training paradigms take advantages of previous knowledge of users and facilitate the generation of mental image due to the automatic development of sensory predictions and motor behavior patterns in the brain. Furthermore, the effectuation of MI as an actual movement would make users feel that their mental images are being executed, and the resulting sensory feedback may allow forward model readjusting the imaginary movement in course.

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Section: Neural Oscillations In Bmimentioning

confidence: 99%

Section: How To Design New Paradigms Based On the Sensory-motor Systementioning

confidence: 99%

Characterizing Motor System to Improve Training Protocols Used in Brain-Machine Interfaces Based on Motor Imagery

Alonso-Valerdi¹,

González-Garrido²

2018

Cognitive and Computational Neuroscience - Principles, Algorithms and Applications

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“…Specifically, during the experiment, the cerebral cortex where channels C 3 and C 4 were located represented the area of right and left hands motor imagery in the brain; during the motor imagery, ERD/ERS in the areas on both sides of the brain was symmetrical, i.e., during the right hand motor imagery, ERD occurred on the cerebral cortex for motor imagery in the left area of the brain while ERS occurred on the cerebral cortex in the right area of the brain; and vice versa. Further research showed that during the motor imagery, obvious ERD/ERS phenomenon occurred mainly within the frequencies of Rhythm μ (8-12Hz) and Rhythm β (18-24Hz) of the corresponding cerebral cortex for motor imagery in the contralateral brain areas, and at these time points, the energy in the frequencies of Rhythm μ and Rhythm β decreased (or increased) [3][4][5][6] . A complete BCI system mainly includes acquisition, preprocessing, feature extraction, pattern recognition, control equipment and peripheral equipment for EEG signals [7] , among which feature extraction and pattern recognition are the most critical parts.…”

Section: Introductionmentioning

confidence: 99%

Research on Algorithm for Feature Extraction and Classification of Motor Imagery EEG Signals

Tian

Zhang

2017

BIO Web Conf.

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Abstract.This paper made a research on the feature extraction and pattern recognition of left and right hands motor imagery EEG signals. In combination with the data from BCI Competition III, denoising preprocessing is carried out for EEG signals firstly; and then, the relative wavelet energy is extracted as a feature vector from the Channels C3 and C4 by use of the algorithm for relative wavelet energy, and pattern recognition is carried out by use of the radial basis function neural network (RBFNN). Simulation results show that the proposed method achieves good classification results.

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“…There are a large number of noninvasive types of BCI now under study, many of which utilize EEG. For example, there are studies under way concerning systems for a subject to transmit his/her intentions . Specifically, the patient is asked to perform multiple motor imagery tasks to induce a specific brain activity that associates to each of these motor imagery tasks, and these patterns are specified based on brain activity and associated with commands prepared in advance.…”

Section: Introductionmentioning

confidence: 99%

A Single‐Trial Multiclass Classification of Various Motor Imagery Tasks for EEG‐Based Brain–Computer Interface Communication

Misawa

Matsuda

2016

Elect Comm in Japan

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SUMMARY We studied the brain activity (alpha and beta rhythms) with various motor imagery tasks for improvement of brain–computer interface (BCI) usability using 14 electroencephalography (EEG) electrodes in five healthy subjects. For this purpose, we estimated two‐class and four‐class classification accuracy on the EEG signals with four motor imagery tasks derived from each type motor imagery (three classical motor imagery and one proposed mental strategy) task using t‐test and super vector machine. The proposed mental strategy was imagery writing Kanji (Japanese characters). It has the possibility of both sensorimotor cortex and the visual cortex activation. Therefore, we expected to extract the distinct activity different from the activation with classical motor imagery tasks. In the two‐class classification results, the classification accuracy was 73.7% on average in all combination of derived motor imagery task. Moreover, we demonstrated that four‐class classification accuracy was 40.1% and the proposed task had possibility of the visual cortex activation dominantly. In experimental results, we proposed the new way for improvement of BCI application usability.

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Combining ERD and ERS features to create a system-paced BCI

Cited by 18 publications

References 24 publications

Characterizing Motor System to Improve Training Protocols Used in Brain-Machine Interfaces Based on Motor Imagery

Characterizing Motor System to Improve Training Protocols Used in Brain-Machine Interfaces Based on Motor Imagery

Research on Algorithm for Feature Extraction and Classification of Motor Imagery EEG Signals

A Single‐Trial Multiclass Classification of Various Motor Imagery Tasks for EEG‐Based Brain–Computer Interface Communication

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