Mu rhythm (de)synchronization and EEG single-trial classification of different motor imagery tasks

Pfurtscheller, Gert; Brunner, Clemens; Schlögl, Alois; Silva, F. H. Lopes da

doi:10.1016/j.neuroimage.2005.12.003

Cited by 1,357 publications

(864 citation statements)

References 30 publications

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“…[52] Similarly, a theta increase was also reported during anxious personal object rumination compared to non-anxious object rumination. [63] The alpha rhythm (8 to 13 Hz) is most prominent over parietal and occipital regions, especially when the eyes are closed, and decreases in response to visual, [64] auditory (tau-rhythm [65]) and tactile (central mu-rhythm [66]) stimulation or during mental tasks. The eventrelated desynchronization in the alpha band (decrease of alpha power) in response to stimulation is believed to represent increased sensory processing, and hence has been associated with an activation of task-relevant sensory cortical regions.…”

Section: Frequency-domain Correlatesmentioning

confidence: 99%

A survey of affective brain computer interfaces: principles, state-of-the-art, and challenges

Mühl

Allison

Nijholt

et al. 2014

Brain-Computer Interfaces

234

159

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Affective states, moods and emotions, are an integral part of human nature: they shape our thoughts, govern the behavior of the individual, and influence our interpersonal relationships. The last decades have seen a growing interest in the automatic detection of such states from voice, facial expression, and physiological signals, primarily with the goal of enhancing human-computer interaction with an affective component. With the advent of brain-computer interface research, the idea of affective brain-computer interfaces (aBCI), enabling affect detection from brain signals, arose. In this article, we set out to survey the field of neurophysiology-based affect detection. We outline possible applications of aBCI in a general taxonomy of brain-computer interface approaches and introduce the core concepts of affect and their neurophysiological fundamentals. We show that there is a growing body of literature that evidences the capabilities, but also the limitations and challenges of affect detection from neurophysiological activity

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Section: Frequency-domain Correlatesmentioning

confidence: 99%

A survey of affective brain computer interfaces: principles, state-of-the-art, and challenges

Mühl

Allison

Nijholt

et al. 2014

Brain-Computer Interfaces

234

159

View full text Add to dashboard Cite

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“…First, beta rhythms, together with mu rhythms, are typically considered to be electrophysiological markers of motor-related activity in the brain. Many studies have found both mu and beta suppression when people perform actions, observe actions or imagine themselves performing actions (Jurkiewicz et al, 2006;McFarland et al, 2000aMcFarland et al, , 2000bNeuper et al, 2006;Orgs et al, 2008;Pfurtscheller and Lopes da Silva, 1999;Pfurtscheller et al, 2006;Pineda, 2005). Suppression of beta rhythms has also been reported when people understand action language compared to abstract language (Moreno et al, 2013), or action language referring to animals compared to action language referring to humans (van Elk et al, 2010).…”

Section: Congruence Effects In Action Languagementioning

confidence: 99%

“…Specifically, mu synchronization reflects neural activity correlating with a deactivated motor cortex, whereas mu suppression or desynchronization can be attributed to an increase in neural activity of the motor and premotor cortex (Kuhlman, 1978;Pfurtscheller et al, 1997). Thus, these rhythms become suppressed or desynchronized when participants move, observe other's movements, or imagine their own movements, especially if these movements are manual (Cochin et al, 1998;McFarland et al, 2000aMcFarland et al, , 2000bPfurtscheller et al, 2006;Pineda, 2005).…”

Section: Introductionmentioning

confidence: 99%

Brain dynamics in the comprehension of action-related language. A time-frequency analysis of mu rhythms

et al. 2015

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a b s t r a c t a r t i c l e i n f oArticle history: Accepted 2 January 2015 Available online 9 January 2015 Keywords:Mu rhythms Beta rhythms Embodied meaning Action language Perceptive language EEG mu rhythms (8-13 Hz) recorded at fronto-central electrodes are generally considered as markers of motor cortical activity in humans, because they are modulated when participants perform an action, when they observe another's action or even when they imagine performing an action. In this study, we analyzed the time-frequency (TF) modulation of mu rhythms while participants read action language ("You will cut the strawberry cake"), abstract language ("You will doubt the patient's argument"), and perceptive language ("You will notice the bright day"). The results indicated that mu suppression at fronto-central sites is associated with action language rather than with abstract or perceptive language. Also, the largest difference between conditions occurred quite late in the sentence, while reading the first noun, (contrast Action vs. Abstract), or the second noun following the action verb (contrast Action vs. Perceptive). This suggests that motor activation is associated with the integration of words across the sentence beyond the lexical processing of the action verb. Source reconstruction localized mu suppression associated with action sentences in premotor cortex (BA 6). The present study suggests (1) that the understanding of action language activates motor networks in the human brain, and (2) that this activation occurs online based on semantic integration across multiple words in the sentence.

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“…EEG and event-related synchronization/desynchronization (ERS/ERD) [6] have been employed for research on brain functional activity for many decades and have become the scientific basis of motor imagery. Studies have shown that distinct phenomena such as ERD/ERS are detectable from EEGs for both real and imagined motor movements in healthy subjects [7,8,9]. Common spatial pattern (CSP) [10] is very successful in constructing spatial filters for detecting ERS/ERD.…”

Section: Introductionmentioning

confidence: 99%

Removing Unrelated Features Based on Linear Dynamical System for Motor-Imagery-Based Brain-Computer Interface

Shi

2011

Neural Information Processing

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Abstract. Common spatial pattern (CSP) is very successful in constructing spatial filters for detecting event-related synchronization and event-related desynchronization. In statistics, a CSP filter can optimally separate the motor-imagery-related components. However, for a single trail, the EEG features extracted after a CSP filter still include features not related to motor imagery. In this study, we introduce a linear dynamical system (LDS) approach to motor-imagery-based brain-computer interface (MI-BCI) to reduce the influence of these unrelated EEG features. This study is conducted on a BCI competition data set, which comprises EEG signals from several subjects performing various movements. Experimental results show that our proposed algorithm with LDS performs better than a traditional algorithm on average. The results reveal a promising direction in the application of LDS-based approach to MI-BCI.

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Mu rhythm (de)synchronization and EEG single-trial classification of different motor imagery tasks

Cited by 1,357 publications

References 30 publications

A survey of affective brain computer interfaces: principles, state-of-the-art, and challenges

A survey of affective brain computer interfaces: principles, state-of-the-art, and challenges

Brain dynamics in the comprehension of action-related language. A time-frequency analysis of mu rhythms

Removing Unrelated Features Based on Linear Dynamical System for Motor-Imagery-Based Brain-Computer Interface

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