2 research outputs found
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 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.This study was funded by the Baden-Württemberg Stiftung
(GRUENS), the Deutsche Forschungsgemeinschaft (DFG,
Koselleck and SP-1533/2-1), Bundes Ministerium fĂĽr Bildung
und Forschung BMBF MOTORBIC (FKZ 13GW0053), the
fortune-Program of the University of TĂĽbingen (2422-0-0),
and AMORSA (FKZ 16SV7754). A Sarasola-Sanz’s work is
supported by the La Caixa-DAAD scholarship, and N IrastorzaLanda’s
work by the Basque Government and IKERBASQUE,
Basque Foundation for Science
EEG and ECoG features for Brain Computer Interface in Stroke Rehabilitation
The ability of non-invasive Brain-Computer Interface (BCI) to control an exoskeleton was
used for motor rehabilitation in stroke patients or as an assistive device for the paralyzed.
However, there is still a need to create a more reliable BCI that could be used to control
several degrees of Freedom (DoFs) that could improve rehabilitation results. Decoding
different movements from the same limb, high accuracy and reliability are some of the main
difficulties when using conventional EEG-based BCIs and the challenges we tackled in this
thesis.
In this PhD thesis, we investigated that the classification of several functional hand reaching
movements from the same limb using EEG is possible with acceptable accuracy. Moreover,
we investigated how the recalibration could affect the classification results. For this reason,
we tested the recalibration in each multi-class decoding for within session, recalibrated
between-sessions, and between sessions.
It was shown the great influence of recalibrating the generated classifier with data from the
current session to improve stability and reliability of the decoding. Moreover, we used a
multiclass extension of the Filter Bank Common Spatial Patterns (FBCSP) to improve the
decoding accuracy based on features and compared it to our previous study using CSP.
Sensorimotor-rhythm-based BCI systems have been used within the same frequency ranges
as a way to influence brain plasticity or controlling external devices. However, neural
oscillations have shown to synchronize activity according to motor and cognitive functions.
For this reason, the existence of cross-frequency interactions produces oscillations with
different frequencies in neural networks. In this PhD, we investigated for the first time the
existence of cross-frequency coupling during rest and movement using ECoG in chronic
stroke patients. We found that there is an exaggerated phase-amplitude coupling between
the phase of alpha frequency and the amplitude of gamma frequency, which can be used as feature or target for neurofeedback interventions using BCIs. This coupling has been also
reported in another neurological disorder affecting motor function (Parkinson and dystonia)
but, to date, it has not been investigated in stroke patients. This finding might change the
future design of assistive or therapeuthic BCI systems for motor restoration in stroke
patients