title: Single trial prediction of self-paced reaching directions from EEG signals.
abstract: Early detection of movement intention could possibly minimize the delays in the activation of neuroprosthetic devices. As yet, single trial analysis using non-invasive approaches for understanding such movement preparation remains a challenging task. We studied the feasibility of predicting movement directions in self-paced upper limb center-out reaching tasks, i.e., spontaneous movements executed without an external cue that can better reflect natural motor behavior in humans. We reported results of non-invasive electroencephalography (EEG) recorded from mild stroke patients and able-bodied participants. Previous studies have shown that low frequency EEG oscillations are modulated by the intent to move and therefore, can be decoded prior to the movement execution. Motivated by these results, we investigated whether slow cortical potentials (SCPs) preceding movement onset can be used to classify reaching directions and evaluated the performance using 5-fold cross-validation. For able-bodied subjects, we obtained an average decoding accuracy of 76% (chance level of 25%) at 62.5 ms before onset using the amplitude of on-going SCPs with above chance level performances between 875 to 437.5 ms prior to onset. The decoding accuracy for the stroke patients was on average 47% with their paretic arms. Comparison of the decoding accuracy across different frequency ranges (i.e., SCPs, delta, theta, alpha, and gamma) yielded the best accuracy using SCPs filtered between 0.1 to 1 Hz. Across all the subjects, including stroke subjects, the best selected features were obtained mostly from the fronto-parietal regions, hence consistent with previous neurophysiological studies on arm reaching tasks. In summary, we concluded that SCPs allow the possibility of single trial decoding of reaching directions at least 312.5 ms before onset of reach.
authors.affiliation: Defitech Chair in Non-Invasive Brain-Machine Interface, Center for Neuroprosthetics, School of Engineering, Ecole Polytechnique Fédérale de Lausanne Lausanne, Switzerland ; Laboratory for Experimental Research on Behavior, Institute of Psychology, University of Lausanne Lausanne, Switzerland.
authors.forename: Eileen Y L
authors.initials: EY
authors.lastname: Lew
authors.affiliation: Defitech Chair in Non-Invasive Brain-Machine Interface, Center for Neuroprosthetics, School of Engineering, Ecole Polytechnique Fédérale de Lausanne Lausanne, Switzerland.
authors.forename: Ricardo
authors.initials: R
authors.lastname: Chavarriaga
authors.affiliation: Laboratory of Robotics and Kinematics, I.R.C.C.S. S. Camillo Hospital Foundation Venice, Italy.
authors.forename: Stefano
authors.initials: S
authors.lastname: Silvoni
authors.affiliation: Defitech Chair in Non-Invasive Brain-Machine Interface, Center for Neuroprosthetics, School of Engineering, Ecole Polytechnique Fédérale de Lausanne Lausanne, Switzerland.
authors.forename: José Del R
authors.initials: Jdel R
authors.lastname: Millán
keywords: EEG; brain-machine interface; movement direction; movement-related potentials; self-paced voluntary movement; stroke