Quasi-movements: A novel motor–cognitive phenomenon

Nikulin, Vadim V.; Hohlefeld, Friederike U.; Jacobs, Arthur; Curio, Gabriel

doi:10.1016/j.neuropsychologia.2007.10.008

Cited by 96 publications

(132 citation statements)

References 79 publications

Supporting

Mentioning

Contrasting

Unclassified

Order By: Relevance

“…The ERD found for the three conditions of the study suggest similar electrophysiological patterns for the movement observation, execution, and imagery. Similar results were also reported by Pineda et al (2000), Nikulin et al (2008), Yuan et al (2010), Bhagat (2014). The relation between movement and observation in the motor cortex was discussed by Tehovnik et al (2013).…”

Section: Discussionsupporting

confidence: 84%

“…Volume 31, Number 4, p. 285-294, 2015 Usually, the detection of movement intention is performed using classifiers that are parameterized, validated and tested in order to obtain the classification accuracy (Bai et al, 2007). In BMI, the most commonly used classifiers include linear discriminant analysis (Kamavuako et al, 2015;Lew et al, 2012;2014), matched filters (Jiang et al, 2015), support vector machines (Bhagat, 2014), common spatial patterns (Nikulin et al, 2008) multivariate linear classifiers (Bai et al, 2011), among others. For successful classification, it is required to have a training record available in order to determine the working parameters of the classifier.…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Detection of movement intention using EEG in a human-robot interaction environment

2015

View full text Add to dashboard Cite

Introduction: This paper presents a detection method for upper limb movement intention as part of a brainmachine interface using EEG signals, whose final goal is to assist disabled or vulnerable people with activities of daily living. Methods: EEG signals were recorded from six naïve healthy volunteers while performing a motor task. Every volunteer remained in an acoustically isolated recording room. The robot was placed in front of the volunteers such that it seemed to be a mirror of their right arm, emulating a Brain Machine Interface environment. The volunteers were seated in an armchair throughout the experiment, outside the reaching area of the robot to guarantee safety. Three conditions are studied: observation, execution, and imagery of right arm's flexion and extension movements paced by an anthropomorphic manipulator robot. The detector of movement intention uses the spectral F test for discrimination of conditions and uses as feature the desynchronization patterns found on the volunteers. Using a detector provides an objective method to acknowledge for the occurrence of movement intention. Results: When using four realizations of the task, detection rates ranging from 53 to 97% were found in five of the volunteers when the movement was executed, in three of them when the movement was imagined, and in two of them when the movement was observed. Conclusions: Detection rates for movement observation raises the question of how the visual feedback may affect the performance of a working brain-machine interface, posing another challenge for the upcoming interface implementation. Future developments will focus on the improvement of feature extraction and detection accuracy for movement intention using EEG data.

show abstract

Section: Discussionsupporting

confidence: 84%

Section: Introductionmentioning

confidence: 99%

Detection of movement intention using EEG in a human-robot interaction environment

2015

View full text Add to dashboard Cite

show abstract

“…But the phenomenon of missing ERD was also observed in participants whose self-assessments provided no evidence for non-compliance with instructions. Here, an alternative approach suggested in Nikulin et al (2008) could be useful: In this study, individuals were guided to perform quasi-movements, which are volitional movements that are minimized under instructions of the experimenter to an extent that they become undetectable by peripheral physiological measures. Although they are similar to imagined movements, the attitude of the performers is different.…”

Section: Cases Of Prediction Failurementioning

confidence: 99%

Neurophysiological predictor of SMR-based BCI performance

et al. 2010

Self Cite

View full text Add to dashboard Cite

Brain-Computer Interfaces (BCIs) allow a user to control a computer application by brain activity as measured, e.g., by electroencephalography (EEG).After about 30 years of BCI research, the success of control that is achieved by means of a BCI system still greatly varies between subjects. For about 20% of potential users the obtained accuracy does not reach the level criterion, meaning that BCI control is not accurate enough to control an application.The determination of factors that may serve to predict BCI performance, and the development of methods to quantify a predictor value from psychological and/or physiological data serves two purposes: a better understanding of the 'BCI-illiteracy phenomenon', and avoidance of a costly and eventually * Corresponding author. Berlin Institute of Technology, Machine Learning Laboratory, Sekr. FR6-9, Franklinstrasse 28/29, 10587 Berlin, Germany. Tel: +49 3031478624, Fax: +49 3031478622. E-mail address: dickhaus@cs.tu-berlin.de (Th. Dickhaus). Preprint submitted to NeuroImageMarch 8 which operates on modulations of sensory motor rhythms (SMRs).

show abstract

“…To some extent, GOI exhibits similarities to 'quasi movements' [Nikulin et al 2008] in which persons have to suppress their muscle activity to a level that cannot be detected by EMG. Quasi movements elicit stronger ERD compared to imagination.…”

Section: Disucssion and Conclusionmentioning

confidence: 99%