Classification of different reaching movements from the same limb using EEG

Objective. Brain-computer-interfaces (BCIs) have been proposed not only as assistive technologies but also as rehabilitation tools for lost functions. However, due to the stochastic nature, poor spatial resolution and signal to noise ratio from electroencephalography (EEG), multidimensional decoding...

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Vydané v:Journal of neural engineering Ročník 14; číslo 4; s. 046018 - 46030
Hlavní autori: Shiman, Farid, López-Larraz, Eduardo, Sarasola-Sanz, Andrea, Irastorza-Landa, Nerea, Spüler, Martin, Birbaumer, Niels, Ramos-Murguialday, Ander
Médium: Journal Article
Jazyk:English
Vydavateľské údaje: England IOP Publishing 01.08.2017
Predmet:
Acoustic Stimulation - methods
Adult
Arm - physiology
brain-computer interface (BCI)
Brain-Computer Interfaces - classification
electroencephalography (EEG)
Electroencephalography - classification
Electroencephalography - methods
Exoskeleton Device
Extremities - physiology
Female
Humans
Male
motor rehabilitation
Movement - physiology
Young Adult
ISSN:1741-2560, 1741-2552, 1741-2552
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Popis
Shrnutí:Objective. Brain-computer-interfaces (BCIs) have been proposed not only as assistive technologies but also as rehabilitation tools for lost functions. However, due to the stochastic nature, poor spatial resolution and signal to noise ratio from electroencephalography (EEG), multidimensional decoding has been the main obstacle to implement non-invasive BCIs in real-live rehabilitation scenarios. This study explores the classification of several functional reaching movements from the same limb using EEG oscillations in order to create a more versatile BCI for rehabilitation. Approach. Nine healthy participants performed four 3D center-out reaching tasks in four different sessions while wearing a passive robotic exoskeleton at their right upper limb. Kinematics data were acquired from the robotic exoskeleton. Multiclass extensions of Filter Bank Common Spatial Patterns (FBCSP) and a linear discriminant analysis (LDA) classifier were used to classify the EEG activity into four forward reaching movements (from a starting position towards four target positions), a backward movement (from any of the targets to the starting position and rest). Recalibrating the classifier using data from previous or the same session was also investigated and compared. Main results. Average EEG decoding accuracy were significantly above chance with 67%, 62.75%, and 50.3% when decoding three, four and six tasks from the same limb, respectively. Furthermore, classification accuracy could be increased when using data from the beginning of each session as training data to recalibrate the classifier. Significance. Our results demonstrate that classification from several functional movements performed by the same limb is possible with acceptable accuracy using EEG oscillations, especially if data from the same session are used to recalibrate the classifier. Therefore, an ecologically valid decoding could be used to control assistive or rehabilitation mutli-degrees of freedom (DoF) robotic devices using EEG data. These results have important implications towards assistive and rehabilitative neuroprostheses control in paralyzed patients.
Bibliografia:JNE-101763
ObjectType-Article-1
SourceType-Scholarly Journals-1
ObjectType-Feature-2
content type line 23
ISSN:1741-2560
1741-2552
1741-2552
DOI:10.1088/1741-2552/aa70d2