Imagined 3D Hand Movement Trajectory Decoding from Sensorimotor EEG Rhythms

Attila Korik, Ronen Sosnik, N Siddique, Damien Coyle

Research output: Chapter in Book/Report/Conference proceedingConference contribution

6 Citations (Scopus)

Abstract

Reconstruction of the three-dimensional (3D) trajectory of an imagined limb movement using electroencephalography (EEG) poses many challenges. However, if achieved, more advanced non-invasive brain-computer interfaces (BCIs) for the physically impaired could be realized. The most common motion trajectory prediction (MTP) BCI employs a time-series of band-pass filtered EEG potentials for reconstructing the 3D trajectory of limb movement using multiple linear regression (mLR). Most MTP BCI studies report the best accuracy using low delta (0.5-2Hz) band-pass filtered EEG potentials. In a recent study, we showed spatiotemporalpower distribution of theta (4-8Hz), mu (8-12Hz), and beta (12-28Hz) EEG frequency bands contain richer information associated with movement trajectory. This finding is in line withthe results in the extensive literature on traditional sensorimotor rhythm (SMR) based multiclass (MC) BCI studies, which report the best accuracy of limb movement classification using power values of mu and beta frequency bands. Here, we show the reconstruction of actual and imagined 3D limb movement trajectory with an MTP BCI using a time-series of bandpower values (BTS model). Furthermore, we show the proposed BTS model outperforms the standard potential time-series model (PTS model). The BTS model yielded best results in the mu and beta bands (R~0.5 for actual and R~0.2 for imagined movement reconstruction) and not in the low delta band, as previously reported for MTP studies using the PTS model. Our results show for the first time how mu and beta activity can be used for decoding imagined 3D hand movement from EEG.
LanguageEnglish
Title of host publicationUnknown Host Publication
Pages4591-4596
Number of pages6
DOIs
Publication statusPublished - 9 Oct 2016
EventIEEE International Conference on Systems, Man, and Cybernetics -
Duration: 9 Oct 2016 → …

Conference

ConferenceIEEE International Conference on Systems, Man, and Cybernetics
Period9/10/16 → …

Fingerprint

Brain-Computer Interfaces
Electroencephalography
Hand
Extremities
Linear Models

Keywords

  • 3D motion trajectory prediction
  • brain-computer interface (BCI)
  • imagined hand movement
  • electroencephalography (EEG)
  • motor imagery (MI)
  • sensorimotor rhythms (SMR)

Cite this

Korik, Attila ; Sosnik, Ronen ; Siddique, N ; Coyle, Damien. / Imagined 3D Hand Movement Trajectory Decoding from Sensorimotor EEG Rhythms. Unknown Host Publication. 2016. pp. 4591-4596
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title = "Imagined 3D Hand Movement Trajectory Decoding from Sensorimotor EEG Rhythms",
abstract = "Reconstruction of the three-dimensional (3D) trajectory of an imagined limb movement using electroencephalography (EEG) poses many challenges. However, if achieved, more advanced non-invasive brain-computer interfaces (BCIs) for the physically impaired could be realized. The most common motion trajectory prediction (MTP) BCI employs a time-series of band-pass filtered EEG potentials for reconstructing the 3D trajectory of limb movement using multiple linear regression (mLR). Most MTP BCI studies report the best accuracy using low delta (0.5-2Hz) band-pass filtered EEG potentials. In a recent study, we showed spatiotemporalpower distribution of theta (4-8Hz), mu (8-12Hz), and beta (12-28Hz) EEG frequency bands contain richer information associated with movement trajectory. This finding is in line withthe results in the extensive literature on traditional sensorimotor rhythm (SMR) based multiclass (MC) BCI studies, which report the best accuracy of limb movement classification using power values of mu and beta frequency bands. Here, we show the reconstruction of actual and imagined 3D limb movement trajectory with an MTP BCI using a time-series of bandpower values (BTS model). Furthermore, we show the proposed BTS model outperforms the standard potential time-series model (PTS model). The BTS model yielded best results in the mu and beta bands (R~0.5 for actual and R~0.2 for imagined movement reconstruction) and not in the low delta band, as previously reported for MTP studies using the PTS model. Our results show for the first time how mu and beta activity can be used for decoding imagined 3D hand movement from EEG.",
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author = "Attila Korik and Ronen Sosnik and N Siddique and Damien Coyle",
year = "2016",
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doi = "10.1109/SMC.2016.7844955",
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Korik, A, Sosnik, R, Siddique, N & Coyle, D 2016, Imagined 3D Hand Movement Trajectory Decoding from Sensorimotor EEG Rhythms. in Unknown Host Publication. pp. 4591-4596, IEEE International Conference on Systems, Man, and Cybernetics, 9/10/16. https://doi.org/10.1109/SMC.2016.7844955

Imagined 3D Hand Movement Trajectory Decoding from Sensorimotor EEG Rhythms. / Korik, Attila; Sosnik, Ronen; Siddique, N; Coyle, Damien.

Unknown Host Publication. 2016. p. 4591-4596.

Research output: Chapter in Book/Report/Conference proceedingConference contribution

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N2 - Reconstruction of the three-dimensional (3D) trajectory of an imagined limb movement using electroencephalography (EEG) poses many challenges. However, if achieved, more advanced non-invasive brain-computer interfaces (BCIs) for the physically impaired could be realized. The most common motion trajectory prediction (MTP) BCI employs a time-series of band-pass filtered EEG potentials for reconstructing the 3D trajectory of limb movement using multiple linear regression (mLR). Most MTP BCI studies report the best accuracy using low delta (0.5-2Hz) band-pass filtered EEG potentials. In a recent study, we showed spatiotemporalpower distribution of theta (4-8Hz), mu (8-12Hz), and beta (12-28Hz) EEG frequency bands contain richer information associated with movement trajectory. This finding is in line withthe results in the extensive literature on traditional sensorimotor rhythm (SMR) based multiclass (MC) BCI studies, which report the best accuracy of limb movement classification using power values of mu and beta frequency bands. Here, we show the reconstruction of actual and imagined 3D limb movement trajectory with an MTP BCI using a time-series of bandpower values (BTS model). Furthermore, we show the proposed BTS model outperforms the standard potential time-series model (PTS model). The BTS model yielded best results in the mu and beta bands (R~0.5 for actual and R~0.2 for imagined movement reconstruction) and not in the low delta band, as previously reported for MTP studies using the PTS model. Our results show for the first time how mu and beta activity can be used for decoding imagined 3D hand movement from EEG.

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