Towards decoding of functional movements from the same limb using EEG

Abstract

In recent years, there has been an increasing interest in using electroencephalographic (EEG) activity to close the loop between brain oscillations and movement to induce functional motor rehabilitation. Rehabilitation robots or exoskeletons have been controlled using EEG activity. However, all studies have used a 2-class or one-dimensional decoding scheme. In this study we investigated EEG decoding of 5 functional movements of the same limb towards an online scenario. Six healthy participants performed a three-dimensional center-out reaching task based on direction movements (four directions and rest) wearing a 32-channel EEG cap. A BCI design based on multiclass extensions of Spectrally Weighted Common Spatial Patterns (Spec-CSP) and a linear discriminant analysis (LDA) classifier was developed and tested offline. The decoding accuracy was 5-fold cross-validated. A decoding accuracy of 39.5% on average for all the six subjects was obtained (chance level being 20%). The results of the current study demonstrate multiple functional movements decoding (significantly higher than chance level) from the same limb using EEG data. This study represents first steps towards a same limb multi degree of freedom (DOF) online EEG based BCI for motor restoration.

Brain–computer interface robotics for hand rehabilitation after stroke: a systematic review

“…After deduplication, 330 studies underwent title and abstract screening. Forty six studies passed this stage and among these, 16 were removed after full-text screening due to the following reasons: insufficient EEG and robotic data [59][60][61][62][63][64][65], the study was out of scope [66][67][68], the study design was not for hand/finger movement [69][70][71][72], no robot or mechatronic device was involved in the study [73,74]. A final sample of 30 studies were included in the qualitative review.…”

Rule-Based EEG Classifier Utilizing Local Entropy of Time–Frequency Distributions

“…Lastly, unlike the methods of [48][49][50][51][52][53][54][55][56], the proposed method showed the potential to classify both real (executed) and imagined movements by applying estimation classifiers not only to one-dimensional functions (e.g., PSD), but relying on features extracted from structures of the TFDs, providing additional insights into the signal's characteristics. Contrary to the known methods for limb movement EEG classification and analysis, which are usually focused on responses of certain electrode channels (in [48,49], C3 and C4; in [50], F3, F4, C3, C4, P3, P4, O1, and O2; in [56] Fz, FC5, FC1, FCz, FC2, FC6, C3, Cz, C4, CP5, CP1, CP2, CP6, P3, Pz, and P4), in the proposed approach, valuable information is also obtained from electrodes that are usually neglected for this kind of estimation (F7, F8, and T4).…”

“…Contrary to the known methods for limb movement EEG classification and analysis, which are usually focused on responses of certain electrode channels (in [48,49], C3 and C4; in [50], F3, F4, C3, C4, P3, P4, O1, and O2; in [56] Fz, FC5, FC1, FCz, FC2, FC6, C3, Cz, C4, CP5, CP1, CP2, CP6, P3, Pz, and P4), in the proposed approach, valuable information is also obtained from electrodes that are usually neglected for this kind of estimation (F7, F8, and T4). In light of this, the classification performance of the above-cited methods [48][49][50][51][52][53][54][55][56] could be upgraded and integrated into multi-criteria computer-based clinical decision-support systems.…”

“…In [50], again, the mean power of the signal evaluated in the frequency domain (PSD) in eight EEG frequency bands and a linear-discriminant-analysis-based classifier were used to distinguish between real (executed) upper limb movements. Recently, several studies [51][52][53][54][55] have achieved classification of the same limb movements with appreciable performance using EEG data, although many of these studies classified only two movements. In [56], several classifiers (LS-SVM, QP-SVM, SMO, k-NN, SVM, etc.)…”

Brain-Computer Interface Robotics for Hand Rehabilitation After Stroke: A Systematic Review

“…After deduplication, 330 studies underwent title and abstract screening. Forty six studies passed this stage and among these, 16 were removed after full-text screening due to the following reasons: insufficient EEG and robotic data [44][45][46][47][48][49][50], the study was out of scope [51][52][53], the study design was not for hand/finger movement [54][55][56][57], no robot or mechatronic device was involved in the study [58,59]. A final list with 30 studies was identified as eligible for qualitative review.…”