Classification of upper limb center-out reaching tasks by means of EEG-based continuous decoding techniques
Tóm tắt
One 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. The 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. The 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. This 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.
Tài liệu tham khảo
Nicolelis MAL. Actions from thoughts. Nature. 2001; 409:403–7.
Millán JdR, Rupp R, Müller-Putz GR, Murray-Smith R, Giugliemma C, Tangermann M, et al. Combining brain-computer interfaces and assistive technologies: state-of-the-art and challenges. Front Neurosci. 2010; 4(161).
Mak J, Wolpaw JR. Clinical applications of brain-computer interfaces: current state and future prospects. IEEE Rev Biomed Eng. 2010; 2:187–99.
Pfurtscheller G, Müller GR, Pfurtscheller J, Gerner HJ, Neuper C. Thought-control of functional electrical stimulation to restore hand grasp in a patient with tetraplegia. Neurosci Lett. 2003; 351:33–6.
Pfurtscheller G, Müller-Putz GR, Scherer R, Neuper C. Rehabilitation with brain-computer interface systems. Computer. 2008; 41(10):58–65.
Caria A, Weber C, Brötz D, Ramos A, Ticini LF, Gharabaghi A, et al. Chronic stroke recovery after combined BCI training and physiotherapy: A case report. Psychophysiology. 2011; 48(4):578–82.
Ramos-Murguialday A, Broetz D, Rea M, Läer L, Yilmaz O, Brasil FL, et al. Brain-machine interface in chronic stroke rehabilitation: A controlled study. Ann Neurol. 2013; 74(1):100–8.
Duncan PW, Goldstein LB, Matchar D, Divine GW, Feussner J. Measurement of motor recovery after stroke. Outcome assessment and sample size requirements. Stroke. 1992; 23:1084–9.
Carmena JM, Lebedev MA, Crist RE, O’Doherty JE, Santucci DM, Dimitrov DF, et al. Learning to control a brain-machine interface for reaching and grasping by primates. PLoS Biol. 2003; 1(2):E42.
Velliste M, Perel S, Spalding MC, Whitford AS, Schwartz ABT. Cortical control of a prosthetic arm for self-feeding. Nature. 2008; 453:1098–101.
Hochberg LR, Bacher D, Jarosiewicz B, Masse NY, Simeral JD, Vogel J, et al. Reach and grasp by people with tetraplegia using a neurally controlled robotic arm. 485. 2012:372–5.
Collinger JL, Wodlinger B, Downey JE, Wang W, Tyler-Kabara EC, Weber DJ, et al. High-performance neuroprosthetic control by an individual with tetraplegia. The Lancet. 2013; 381(9866):557–64.
Schalk G, Kubanek J, Miller KJ, Anderson NR, Leuthardt EC. Decoding two-dimensional movement trajectories using electrocorticographic signals in human. J Neural Eng. 2012; 4:264–75.
Pistohl T, Schulze-Bonhage A, Aertsen A, Mehring C, Ball T. Decoding natural grasp types from human ECoG. Neuroimage. 2012; 59(1):248–60.
Georgopoulos AP, Langheim FJ, Leuthold AC, Merkle AN. Magnetoencephalographic signals predict movement trajectory in space. Exp Brain Res. 2005; 167:132–5.
Waldert S, Preissl H, Demandt E, Braun C, Birbaumer N, Aertsen A, et al. Hand movement direction decoded from MEG and EEG. J Neurosci. 2008; 28(4):1000–8.
Bradberry TJ, Gentili RJ, Contreras-Vidal JL. Reconstructing three-dimensional hand movements from non-invasive electroencephalographic signals. J Neurosci. 2010; 30(9):3432–7.
Agashe HA, Contreras-Vidal JL. Reconstructing hand kinematics during reach to grasp movements from electroencephalographic signals. Intl Conf IEEE EMBS. 2011;:5444–7.
Ofner P, Müller-Putz GR. Decoding of velocities and positions of 3D arm movement from EEG. Intl Conf IEEE EMBS. 2012;:6406–9.
Úbeda A, Hortal E, Iáñez E, Perez-Vidal C, Azorín JM. Assessing movement factors in upper limb kinematics decoding from EEG signals. PLoS ONE. 2015; 10(5):e0128456.
Antelis JM, Montesano L, Ramos-Murguialday A, Birbaumer N, Minguez J. On the usage of linear regression models to reconstruct limb kinematics from low frequency EEG signals. PLoS ONE. 2013; 8(4):e61976.
Bradberry TJ, Gentili RJ, Contreras-Vidal JL. Fast attainment of computer cursor control with noninvasively acquired brain signals. J Neural Eng. 2011;8(3).
Poli R, Salvaris M. Comment on “Fast attainment of computer cursor control with noninvasively acquired brain signals”. J Neural Eng. 2011;8(5).
Bradberry TJ, Gentili RJ, Contreras-Vidal JL. Reply to comment on “Fast attainment of computer cursor control with noninvasively acquired brain signals”. J Neural Eng. 2011;8(5).
Choi K. Reconstructing four joint angles on the shoulder and elbow from noninvasive electroencephalographic signals through electromyography. Front Neurosci. 2013; 7:190.
Beuchat NJ, Chavarriaga R, Degallier S, Millán JdR. Offline decoding of upper limb muscle synergies from EEG slow cortical potentials. Intl Conf IEEE EMBS. 2013;:3594–7.
Garipelli G, Chavarriaga R, Millán JdR. Single trial analysis of slow cortical potentials: a study on anticipation related potential. J Neural Eng. 2013; 10(3):036014.
Agashe HA, Paek AY, Zhang Y, Contreras-Vidal JL. Global cortical activity predicts shape of hand during grasping. Front Neurosci. 2015; 9:121.
Bhagat NA, Venkatakrishnan A, Abibullaev ArtzEJ, Yozbatiran N, Blank AA, et al. Design and optimization of an EEG-based brain machine interface (BMI) to an upper-limb exoskeleton for stroke survivors. Front Neurosci. 2016;10:122.
Obermaier B, Neuper C, Guger C, Pfurtscheller G. Information transfer rate in a five-classes brain-computer interface. IEEE Trans Neural Syst Rehabil Eng. 2001; 9(3):283–8.
Hammon PS, Makeig S, Poizner H, Todorov E, de Sa VR. Predicting reaching targets from human EEG. IEEE Signal Process Mag. 2008; 25:69–77.
Robinson N, Guan C, Vinod AP, Ang KK, Tee KP. Multi-class EEG classification of voluntary hand movement directions. J Neural Eng. 2013; 10(5):056018.
Lew E, Chavarriaga R, Silvoni S, Millán JdR. Single trial prediction of self-paced reaching directions from EEG signals. Front Neurosci. 2014; 8:222.
Lew E, Chavarriaga R, Silvoni S, Millán JdR. Detection of self-paced reaching movement intention from EEG signals. Front Neuroeng. 2012; 5:13.
Takeuchi N, Izumi SI. Rehabilitation with poststroke motor recovery: A review with a focus on neural plasticity. Stroke Res Treatment. 2013; 2013:128641.
Farina D, Negro F. Common synaptic input in motor neurons, motor unit synchronization, and force control. Exerc Sport Sci Rev. 2015; 43(1):23–33.
Gwin JT, Ferris DP. Beta- and gamma-range human lower limb corticomuscular coherence. Front Neurosci. 2012;6:258.
Ushiyama J, Masakado Y, Fujiwara T, Tsuji T, Hase K, Kimura A, Ushiba J. Contraction level-related modulation of corticomuscular coherence differs between tibialis anterior and soleus muscles in humans. J Appl Physiol. 2012; 112:1258–67.