In JoVE (1)
Articles by Zachery R. Hernandez in JoVE
A Novel Experimental och analytiskt förhållningssätt till Multimodal Neural avkodning of Intent Under social interaktion i fritt bete Human Spädbarn Jesus G. Cruz-Garza1, Zachery R. Hernandez1, Teresa Tse1,2,3, Eunice Caducoy1,3, Berdakh Abibullaev1, Jose L. Contreras-Vidal1,2 1Laboratory for Noninvasive Brain-Machine Interface Systems, Department of Electrical and Computer Engineering, University of Houston, 2Department of Biomedical Engineering, University of Houston, 3Department of Biology and Biochemistry, University of Houston
Other articles by Zachery R. Hernandez on PubMed
Neural Decoding of Expressive Human Movement from Scalp Electroencephalography (EEG) Frontiers in Human Neuroscience. 2014 | Pubmed ID: 24782734 Although efforts to characterize human movement through electroencephalography (EEG) have revealed neural activities unique to limb control that can be used to infer movement kinematics, it is still unknown the extent to which EEG can be used to discern the expressive qualities that influence such movements. In this study we used EEG and inertial sensors to record brain activity and movement of five skilled and certified Laban Movement Analysis (LMA) dancers. Each dancer performed whole body movements of three Action types: movements devoid of expressive qualities ("Neutral"), non-expressive movements while thinking about specific expressive qualities ("Think"), and enacted expressive movements ("Do"). The expressive movement qualities that were used in the "Think" and "Do" actions consisted of a sequence of eight Laban Effort qualities as defined by LMA-a notation system and language for describing, visualizing, interpreting and documenting all varieties of human movement. We used delta band (0.2-4 Hz) EEG as input to a machine learning algorithm that computed locality-preserving Fisher's discriminant analysis (LFDA) for dimensionality reduction followed by Gaussian mixture models (GMMs) to decode the type of Action. We also trained our LFDA-GMM models to classify all the possible combinations of Action Type and Laban Effort quality (giving a total of 17 classes). Classification accuracy rates were 59.4 ± 0.6% for Action Type and 88.2 ± 0.7% for Laban Effort quality Type. Ancillary analyses of the potential relations between the EEG and movement kinematics of the dancer's body, indicated that motion-related artifacts did not significantly influence our classification results. In summary, this research demonstrates that EEG has valuable information about the expressive qualities of movement. These results may have applications for advancing the understanding of the neural basis of expressive movements and for the development of neuroprosthetics to restore movements.
Decoding of Intentional Actions from Scalp Electroencephalography (EEG) in Freely-behaving Infants Conference Proceedings : ... Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual Conference. 2014 | Pubmed ID: 25570402 The mirror neuron system (MNS) in humans is thought to enable an individual's understanding of the meaning of actions performed by others and the potential imitation and learning of those actions. In humans, electroencephalographic (EEG) changes in sensorimotor a-band at central electrodes, which desynchronizes both during execution and observation of goal-directed actions (i.e., μ suppression), have been considered an analog to MNS function. However, methodological and developmental issues, as well as the nature of generalized μ suppression to imagined, observed, and performed actions, have yet to provide a mechanistic relationship between EEG μ-rhythm and MNS function, and the extent to which EEG can be used to infer intent during MNS tasks remains unknown. In this study we present a novel methodology using active EEG and inertial sensors to record brain activity and behavioral actions from freely-behaving infants during exploration, imitation, attentive rest, pointing, reaching and grasping, and interaction with an actor. We used 5-band (1-4Hz) EEG as input to a dimensionality reduction algorithm (locality-preserving Fisher's discriminant analysis, LFDA) followed by a neural classifier (Gaussian mixture models, GMMs) to decode the each MNS task performed by freely-behaving 6-24 month old infants during interaction with an adult actor. Here, we present results from a 20-month male infant to illustrate our approach and show the feasibility of EEG-based classification of freely occurring MNS behaviors displayed by an infant. These results, which provide an alternative to the μ-rhythm theory of MNS function, indicate the informative nature of EEG in relation to intentionality (goal) for MNS tasks which may support action-understanding and thus bear implications for advancing the understanding of MNS function.