Modulation of stimulus-induced 20-Hz activity during lower extremity motor imagery

Kinai, Takahiro; Matsubayashi, Jun; Minami, Chihiro; Tominaga, Wataru; Nakamura, Megumi; Nakagawa, Takashi; Matsuhashi, Masao; Mima, Tatsuya; Fukuyama, Hidenao; Mitani, Akira

doi:10.1016/j.neures.2009.03.013

Cited by 2 publications

(1 citation statement)

References 15 publications

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“…Possible future work on this method would include applying it to data that was recorded with significantly more electrodes in order to evaluate the impact of multi-channel (0'RQWKHFODVVLILHU ¶VDFFXUDF\REMHFWLYHO\LGHQWLI\LQJWKH ERXQGDULHV RI DQ LQGLYLGXDO VXEMHFW ¶V UK\WKP WKURXJK WKH use of averaged training data and investigating the possible usefulness of the 20 Hz rhythm that has been observed during some motor imagery activities [9].…”

Section: Discussionmentioning

confidence: 99%

Novel use of Empirical Mode Decomposition in single-trial classification of motor imagery for use in brain-computer interfaces

Davies

James

2013

2013 35th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC)

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This paper presents a novel method, based on multi-channel Empirical Mode Decomposition (EMD), of classifying the electroencephalogram (EEG) recordings of imagined movement by a subject within a brain-computer interfacing (BCI) framework. EMD is a technique that divides any non-linear or non-stationary signal into groups of frequency harmonics, called Intrinsic Mode Functions (IMFs). As frequency is a key component of both IMFs and the μ rhythm (8-13 Hz brain activity generated during motor imagery), IMFs are then grouped by frequency. EMD is applied to the recordings from two electrodes for each trial and the resulting IMFs are grouped according to peak-frequency band via Hierarchical Clustering Analysis (HCA). The cluster containing the frequency band of the μ rhythm (8-13 Hz) is then selected and the sum-total of the IMFs from each electrode are summed together. A simple linear classifier is then sufficient to classify the motor-imagery with 89% sensitivity from a separate test set.

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Section: Discussionmentioning

confidence: 99%

Novel use of Empirical Mode Decomposition in single-trial classification of motor imagery for use in brain-computer interfaces

Davies

James

2013

2013 35th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC)

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Modulation of stimulus-induced 20-Hz activity for the tongue and hard palate during tongue movement in humans

Maezawa

Onishi

Yagyu

et al. 2016

Clinical Neurophysiology

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Highlights 4 Magnetic 20-Hz event-related synchronization (ERS) was detected in response 5 to tongue and hard palate stimulation at rest, but was inhibited by tongue 6 movement. 7 ERS was induced by oral stimulation, with or without proprioception, but was 8 inhibited by tongue movement regardless of the stimulation or movement area. 9 The results suggest that 20-Hz cortical coordination of the oral sensorimotor 10 system is important for oral movements.

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Modulation of stimulus-induced 20-Hz activity during lower extremity motor imagery

Cited by 2 publications

References 15 publications

Novel use of Empirical Mode Decomposition in single-trial classification of motor imagery for use in brain-computer interfaces

Novel use of Empirical Mode Decomposition in single-trial classification of motor imagery for use in brain-computer interfaces

Modulation of stimulus-induced 20-Hz activity for the tongue and hard palate during tongue movement in humans

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