Classification of upper limb center-out reaching tasks by means of EEG-based continuous decoding techniques

Abstract

BackgroundOne of the current challenges in brain-machine interfacing is to characterize and decode upper limb kinematics from brain signals, e.g. to control a prosthetic device. Recent research work states that it is possible to do so based on low frequency EEG components. However, the validity of these results is still a matter of discussion. In this paper, we assess the feasibility of decoding upper limb kinematics from EEG signals in center-out reaching tasks during passive and active movements.MethodsThe decoding of arm movement was performed using a multidimensional linear regression. Passive movements were analyzed using the same methodology to study the influence of proprioceptive sensory feedback in the decoding. Finally, we evaluated the possible advantages of classifying reaching targets, instead of continuous trajectories.ResultsThe results showed that arm movement decoding was significantly above chance levels. The results also indicated that EEG slow cortical potentials carry significant information to decode active center-out movements. The classification of reached targets allowed obtaining the same conclusions with a very high accuracy. Additionally, the low decoding performance obtained from passive movements suggests that discriminant modulations of low-frequency neural activity are mainly related to the execution of movement while proprioceptive feedback is not sufficient to decode upper limb kinematics.ConclusionsThis paper contributes to the assessment of feasibility of using linear regression methods to decode upper limb kinematics from EEG signals. From our findings, it can be concluded that low frequency bands concentrate most of the information extracted from upper limb kinematics decoding and that decoding performance of active movements is above chance levels and mainly related to the activation of cortical motor areas. We also show that the classification of reached targets from decoding approaches may be a more suitable real-time methodology than a direct decoding of hand position.

Low-Cost Robotic Guide Based on a Motor Imagery Brain–Computer Interface for Arm Assisted Rehabilitation

“…The relationship of movement between the carriage and the motor revolutions is obtained from Equation (1). 21 The Direct Current (DC) motor that drives the gear system has a nominal supply voltage of 12 V and in nominal operation can vertically raise 3 kg (as measured on the guide carriage). This motor has a coupled reducer and an incremental encoder.…”

“…BCI technology can also be used to assist in rehabilitation, since BCIs aim to translate the patient's MI into external commands to control a helping device. The thought or realization of a motor action is generated in the sensory-motor areas of the cerebral cortex, which causes a variation in the EEG signal [21]. Specifically, imagining motor actions usually modifies the amplitude of the mu/beta rhythms in the sensory-motor cortex [22].…”

Classification of Upper Limb Movements Using Convolutional Neural Network with 3D Inception Block

“…They demonstrated that the low gamma (28-40 Hz) band could improve decoding accuracy for participants. Ubeda et al [29] confirmed the feasibility of decoding upper limb kinematics from EEG in center-out reaching tasks during passive and active movements. They applied the multidimensional linear regression model with kinematics state (position and velocity).…”

Estimation of Neuromuscular Primitives from EEG Slow Cortical Potentials in Incomplete Spinal Cord Injury Individuals for a New Class of Brain-Machine Interfaces

“…In these studies, kinematic parameters were directly decoded from the activity of larger regions of the scalp by applying linear decoders to SCPs for decoding both upper and lower limb joint movements (Bradberry et al, 2010 ; Presacco et al, 2011 ), sitting and standing states (Bulea et al, 2014 ), finger movements (Paek et al, 2014 ), and types of grasping (Agashe et al, 2015 ). Other studies have dealt with the characteristics of the performed movement, showing that hand kinematics are better decoded when continuous and linear movements are performed (Úbeda et al, 2015 ) and exploring the possibility of using them to classify reaching directions (Úbeda et al, 2017 ).…”